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  • 51.
    Galar, Diego
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Peters, Ralph
    Maintenance Excellence Institute.
    Berges, Luis
    Manufacturing Engineering and Advanced Metrology Group, Aragon Institute of Engineering Research (13A), University of Zaragoza.
    Stenström, Christer
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Kumar, Uday
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Composite indicators in asset management2012Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Composite indicators are formed when individual indicators are compiled into a single index. A composite indicator should ideally measure multidimensional concepts which cannot be captured by a single index. Since asset management is multidisciplinary, composite indicators would be helpful. The paper describes a method of monitoring a complex entity in a processing plant. In this scenario, a plurality of use indices and weighting values are used to create a composite use index from a combination of lower level use indices and weighting values. Each use index contains status information on one aspect of the lower level entities, and each weighting value corresponds to one lower level entity. The resulting composite indicator can be a decision-making tool for asset managers.Keywords – Indicator, aggregation, KPI, performance, hierarchy, DSS

  • 52.
    Galar, Diego
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Pilar, Lamban
    Manufacturing and Design Engineering Department, University of Zaragoza.
    Luis, Berges
    Manufacturing and Design Engineering Department, University of Zaragoza.
    Application of dynamic benchmarking of rotating machinery for e-maintenance2010Inngår i: International Journal of Systems Assurance Engineering and Management, ISSN 0975-6809, E-ISSN 0976-4348, Vol. 1, nr 3, s. 246-262Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    The vibration analysis and condition monitoring technology is based on comparison of measurements obtained with benchmarks suggested by manufacturers or standards. In this case, the references provided by current rules are static and independent of parameters such as age, operational or environmental conditions in which the machine is analyzed. It creates false alarms and many unnecessary interventions. New communication technologies allow the integration of e-maintenance systems, production and real-time data or the result of vibration routes. The integration of all these data allows data mining and extraction of parameters to be incorporated into decision making typical of CBM, such as repairs, downtime, overhauls, etc. Absolute vibration data and spectral analysis of rotating machinery require the study of several signals by machine, which become hundreds of values and spectra to analyze where there, is a large number of machines. It is therefore necessary to find proper benchmark points to compare with vibration parameters. These parameters and benchmark points have to be adapted to the real status of the plant and vibratory conditions have to be automated to be easily understood by persons not connected with the detailed analysis of spectra. The trend of the measured data and its comparison with benchmarks should assess the success of the implementation of CBM and other decisions about implementation and changes in maintenance programs. This article proposes the use of two new indicators that result from data mining as a reference dynamic, not static as proposed by the standard, manufacturer or the expertise of maintenance technicians. These values show the real condition of the machine in terms of vibration. The application of these references to the decision making process of the maintenance manager and its inclusion in maintenance scorecard avoids unnecessary repairs caused by false alarms and thus prolongs the life of the equipment, resulting in the improvement of parameters such as the MTBF, in a e-maintenance system

  • 53.
    Galar, Diego
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Sandborn, Peter
    University of Maryland.
    Kumar, Uday
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Johansson, Carl-Anders
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    SMART - Integrating human safety risk assessment with asset integrity2013Inngår i: Advances in Condition Monitoring of Machinery in Non-Stationary Operations: Proceedings of the third International Conference on Condition Monitoring of Machinery in Non-Stationary Operations CMMNO 2013 / [ed] Giorgio Dalpiaz; Riccardo Rubini; Gianluca DÉlia; Marco Cocconcelli; Fakher Chaari; Radoslaw Zimroz; Walter Bartelmus; Mohamed Haddar, Encyclopedia of Global Archaeology/Springer Verlag, 2013, s. 37-59Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Maintenance activities are commonly organized into scheduled and unsched-uled actions. Scheduled maintenance is undertaken during pre-programmed in-spections. Such maintenance operations try to minimize the risk of deterioration based on a priori knowledge of failure mechanisms and their timing. However, in complex systems it is not always possible to schedule maintenance actions to mit-igate all undesired effects, and SMART systems, which monitor selected parame-ters, propose actions to correct any deviation in normal behavior. Maintenance decisions must be made on the basis of accepted risk. Performed or not performed scheduled tasks as well as deferred corrective actions can have positive or negative consequences for the company, technicians and machines. These three risks should be properly assessed and prioritized as a function of the goals to be achieved. This paper focuses on how best practices in risk assessment for human safety can be successfully transferred to risk assessment for asset integrity.

  • 54.
    Galar, Diego
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Seneviratne, DammikaLuleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Management Systems in Production Engineering: Maintenance Performance Measurement and Management Challenges:  From Sensing to Decision Support2017Collection/Antologi (Annet vitenskapelig)
  • 55.
    Galar, Diego
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Seneviratne, DammikaLuleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    MPMM 2016, Maintenance, Performance, Measurement & Management: conference proceedings2017Konferanseproceedings (Fagfellevurdert)
    Abstract [en]

    The maintenance function is inherent to production but its activities are not always understood or quantified. A characteristic of maintenance is that its activity involves more than a group of people or a workshop and goes beyond the limits of a traditional department.

    The scope of maintenance in a manufacturing environment is illustrated by its various definitions. British Standards Institute defines maintenance as a combination of all technical and associated administrative activities required to keep equipment, installations and other physical assets in the desired operating condition or restore them to this condition, some authors indicate that maintenance is about achieving the required asset capabilities within an economic or business context, or consists of the engineering decisions and associated actions necessary and sufficient for the optimization of specified equipment ‘capability’ where capability is the ability to perform a specified function within a range of performance levels that may relate to capacity, rate, quality, safety and responsiveness. However, they all agree that the objective of maintenance is to achieve the agreed-upon output level and operating pattern at minimum resource cost within the constraints of system condition and safety.

    We can summarize the maintenance objectives under the following categories: ensuring asset functions (availability, reliability, product quality etc.); ensuring design life; ensuring asset and environmental safety; ensuring cost effectiveness in maintenance; ensuring efficient use of resources (energy and raw materials). For production equipment, ensuring the system functions as it should is the prime maintenance objective. Maintenance must provide the required reliability, availability, efficiency and capability of production systems. Ensuring system life refers to keeping the equipment in good condition to achieve or prolong its designed life. In this case, cost has to be optimized to achieve the desired plant condition. Asset safety is very important, as failures can have catastrophic consequences. The cost of maintenance has to be minimized while keeping the risks within strict limits and meeting the statutory requirements.

    For a long time, maintenance was carried out by the workers themselves, in a more loosely organized style of maintenance with no haste for the machinery or tools to be operational again. However, things have changed.

    •        First, there is a need for higher asset availability. With scale economies dominating the global map, the demand for products is increasing. However, companies suffer financially from the costs of expansion, purchase of industrial buildings, production equipment, acquisitions of companies in the same sector, and so on. Productive capacities must be kept at a maximum, and organizations are beginning to worry about keeping track of the parameters that may affect the availability of their plants and machinery.

    •        The second concern follows from the first. When organizations begin to optimize their production costs and create cost models attributable to the finished product, they start to question maintenance cost. This function has grown to include assets, personnel etc., consuming a significant percentage of the overall organization budget. Therefore, when companies are establishing policies to streamline costs, the question of the maintenance budget arises, followed by questions about the success of this budget. They start to consider availability and quality parameters.

    A question that has haunted maintenance throughout history now appears: how do we maximize availability at the lowest cost? To answer this question, various methodologies, technologies and batteries of indicators are being developed to observe the impacts of improvements.

  • 56.
    Galar, Diego
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Stenström, Christer
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Parida, Aditya
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Kumar, Rupesh
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Berges, Luis
    University of Zaragoza.
    Human factor in maintenance performance measurement2011Inngår i: IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), Piscataway, NJ: IEEE Communications Society, 2011, s. 1569-1576Konferansepaper (Fagfellevurdert)
    Abstract [en]

    The maintenance performance measurement is often faced with a lack in knowledge about the real function of the maintenance department within organizations, and consequently the absence of appropriate targets emanating from the global mission and vision. These facts bring about metrics not adapted to the real needs, which has a strong load of human factor and without a roadmap of the amount of data to be collected, their processing and use in decision making. This article proposes a model where qualitative and quantitative methods are combined in order to complement advantages and disadvantages of them both.

  • 57.
    Galar, Diego
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Thaduri, Adithya
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Catelani, Marcantonio
    Department of Information Engineering, University of Florence.
    Ciani, Lorenzo
    Department of Information Engineering, University of Florence.
    Context awareness for maintenance decision making: A diagnosis and prognosis approach2015Inngår i: Measurement, ISSN 0263-2241, E-ISSN 1873-412X, Vol. 67, s. 137-150Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    All assets necessarily suffer wear and tear during operation. Prognostics can assess the current health of a system and predict its remaining life based on features capturing the gradual degradation of its operational capabilities. Prognostics are critical to improve safety, plan successful work, schedule maintenance, and reduce maintenance costs and down time. Prognosis is a relatively new area but has become an important part of Condition-based Maintenance (CBM) of systems. Broadly stated, prognostic methods are either data-driven, rule based, or model-based. Each approach has advantages and disadvantages; consequently, they are often combined in hybrid applications. A hybrid model can combine some or all model types; thus, more complete information can be gathered, leading to more accurate recognition of the fault state. In this context, it is important to evaluate the consistency and reliability of the measurement data obtained during laboratory testing and the prognostic/diagnostic monitoring of the system under examination.This approach is especially relevant in systems where the maintainer and operator know some of the failure mechanisms with a sufficient amount of data, but the sheer complexity of the assets precludes the development of a complete model-based approach. This paper addresses the process of data aggregation into a contextual awareness hybrid model to get Residual Useful Life (RUL) values within logical confidence intervals so that the life cycle of assets can be managed and optimised.

  • 58.
    Galar, Diego
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Thaduri, Adithya
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Kumar, Uday
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Pascual, Rodrigo
    Pontificia Universidad Católica de Chile.
    SMART maintenance and prescriptive asset management for mining2016Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Operation and maintenance (O&M) activities are commonly organized into scheduled and unscheduled actions. Scheduled maintenance is undertaken during pre-programmed inspections. Such maintenance operations try to minimize the risk of deterioration based on a priori knowledge of failure mechanisms and their timing. However, in complex systems it is not always possible to schedule maintenance actions to mitigate all undesired effects, and SMART systems, which monitor selected parameters, propose actions to correct any deviation in normal behaviour. Indeed, SMARTness is one step beyond the prediction of failure time but also a proposition of operation and maintenance profiles in order to fulfill the company goals. Therefore prognosis and RUL estimation become a part of the process in order to achieve prescriptive actions and control the degradation and operational aspects of the asset as per expected demand and customer request. These O&M decisions must be made on the basis of accepted risk. Performed or unperformed scheduled tasks as well as deferred corrective actions can have positive or negative consequences for the company, technicians, and machines. These three risks should be properly assessed and prioritized as a function of the goals to be achieved. This paper focuses on the SMARTness of assets in order to go one step forwards and propose prescriptive O&M decisions based on a self-risk assessment as a trade-off for asset integrity and company goals.

  • 59.
    Galar, Diego
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Thaduri, Adithya
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Simon, Victor
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Catelani, Marcantonio
    Department of Information Engineering, University of Florence.
    Ciani, Lorenzo
    Department of Information Engineering, University of Florence.
    Prognostic Hybrid Modelling from Data Fusion on Machine Tools2016Inngår i: Measurement, ISSN 1536-6367, E-ISSN 1536-6359Artikkel i tidsskrift (Annet (populærvitenskap, debatt, mm))
    Abstract [en]

    This paper proposes an enhancement of remaining useful life (RUL) prediction method based on degradation trajectory tracking under the scope of machine tools. The operational condition data of the machine over time provides the potential degradation state at the next estimation iteration step, based on data-driven techniques. The model-based approach is considered as long-term prognostics method assuming that a physical model describing the degradation behaviour is available. Fusing the aforementioned techniques outputs a hybrid model for RUL estimation.

  • 60.
    Galar, Diego
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Villarejo, Roberto
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Johansson, Carl-Anders
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Kumar, Uday
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Berges, Luis
    Zaragoza University (Higher Polytechnic Centre), Division of Design and Production Engineering.
    Hybrid models for PHM deployment techniques in railway2013Inngår i: 10th International Conference on Condition Monitoring and Machinery Failure Prevention Technologies 2013, CM 2013 and MFPT 2013, 2013, Vol. 2, s. 1047-1056Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Many railway assets exhibit increasing wear and tear of equipment during operation. Prognostics are viewed as an add-on capability to diagnosis; they assess the current health of a system and predict its remaining life based on features that capture the gradual degradation in the operational capabilities of a system. Prognostics are critical to improve safety, plan successful missions, schedule maintenance, reduce maintenance cost and down time. Unlike fault diagnosis, prognosis is a relatively new area and became an important part of Condition-based Maintenance (CBM) of systems. Currently, there are many prognostic techniques; their usage must be tuned for each application. The prognostic methods can be classified as being associated with one or more of the following two approaches: data-driven and model-based. Each of these approaches has its own advantages and disadvantages, and consequently, they are often used in combination in many applications called hybrid. A hybrid model could combine some or all of model types (data-driven, and phenomenological), so that more complete information allows for more accurate recognition of the fault state. This approach is especially relevant in railway where the maintainer and operator know some of the failure mechanisms, but the complexity of the infrastructure and rolling stock is huge so no way to develop a complete model based approach that is why development of hybrid models becomes necessary to estimate RUL of railway systems in a more accurate way. The paper address this process of data aggregation into the hybrid model in order to get RUL values within logical confidence intervals so railway assets life cycle can be managed and optimized.

  • 61.
    Galar, Diego
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Wandt, Karina
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Karim, Ramin
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Berges, Luis
    Department of Design Engineering and Manufacturing, University of Zaragoza.
    The evolution from e(lectronic)Maintenance to i(ntelligent)Maintenance2012Inngår i: The Ninth International Conference on Condition Monitoring and Machinery Failure Prevention Technologies, 2012, Vol. 1, s. 203-216Konferansepaper (Fagfellevurdert)
    Abstract [en]

    iMaintenance stands for integrated, intelligent and immediate maintenance. It integrates various maintenance functions and connects these to all devices, using advanced communication technologies. The main challenge is to integrate the disparate systems and capabilities developed under current eMaintenance models and to make them immediately accessible through intelligent computing technologies. iMaintenance systems are computer-based, able to evolve with the system that they monitor and control, and they can be embedded in the system’s components, providing the ability to integrate new functionality with no downtime. This article will show how iMaintenance systems can provide decision-making support, thereby going beyond merely connecting various maintenance systems.

  • 62.
    Galar, Diego
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Wandt, Karina
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Karim, Ramin
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Berges, Luis
    Department of Design Engineering and Manufacturing, University of Zaragoza.
    The evolution from e(lectronic)Maintenance to i(ntelligent)Maintenance2012Inngår i: Insight (Northampton), ISSN 1354-2575, E-ISSN 1754-4904, Vol. 54, nr 8, s. 446-450Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    iMaintenance stands for integrated, intelligent and immediate maintenance. It integrates various maintenance functions and connects these to all devices using advanced communication technologies. The main challenge is to integrate the disparate systems and capabilities developed under current eMaintenance models and to make them immediately accessible through intelligent computing technologies. iMaintenance systems are computer-based, able to evolve with the system that they monitor and control and they can be embedded in the system's components, providing the ability to integrate new functionality with no downtime. This article will show how iMaintenance systems can provide decision-making support, thereby going beyond merely connecting various maintenance systems.

  • 63.
    Gandhi, Kanika
    et al.
    Bharatiya Vidya Bhavan’s Usha & Lakshmi Mittal Institute of Mangement, Copernicus Lane, K. G. Marg, New Delhi.
    Jha, P.C
    Department of Operational Research, Faculty of Mathematical Sciences, University of Delhi.
    Govindan, Kannan
    Department of Business and Economics, University of Southern Denmark, Odense.
    Galar, Diego
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Three Echelon Supply Chain Design with Supplier Evaluation2014Inngår i: Proceedings of the Third International Conference on Soft Computing for Problem Solving: SocProS 2013 / [ed] Millie Pant; Kusum Deep; Atulya Nagar; Jagdish Chand Bansal, Berlin: Springer-Verlag GmbH , 2014, Vol. 2, s. 867-881Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Effective supply chain management (SCM), which facilitates companies to react to changing demand by swiftly communicating those needs to their supplier, is at the root of successful manufacturing. Optimizing a supply chain (SC) performance is a key factor for success in long term SC relationships. Much information like price, delivery time percentage and acceptance percentage are discussed in the process. A factor as imprecise demand is added in the same process that fuzzifies coordination between buyer and supplier. The paper considers nondeterministic conditions in the environment of business, coordination in procurement and distribution in a supplier selection problem that was proposed and a fuzzy model with two objectives was defined. The proposed model is a “fuzzy bi-objective mixed integer nonlinear” problem. A “fuzzy solution and fuzzy goal programming method” is used to convert the model into crisp form and solved using differential evolution

  • 64.
    Gerdes, Mike
    et al.
    Department of Automotive and Aeronautical Engineering, HAW Hamburg.
    Galar, Diego
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Fuzzy condition monitoring of recirculation fans and filters2016Inngår i: International Journal of Systems Assurance Engineering and Management, ISSN 0975-6809, E-ISSN 0976-4348, Vol. 7, nr 4, s. 469-479Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    A reliable condition monitoring is needed to be able to predict faults. Pattern recognition technologies are often used for finding patterns in complex systems. Condition monitoring can also benefit from pattern recognition. Many pattern recognition technologies however only output the classification of the data sample but do not output any information about classes that are also very similar to the input vector. This paper presents a concept for pattern recognition that outputs similarity values for decision trees. Experiments confirmed that the method works and showed good classification results. Different fuzzy functions were evaluated to show how the method can be adapted to different problems. The concept can be used on top of any normal decision tree algorithms and is independent of the learning algorithm. The goal is to have the probabilities of a sample belonging to each class. Performed experiments showed that the concept is reliable and it also works with decision tree forests (which is shown during this paper) to increase the classification accuracy. Overall the presented concept has the same classification accuracy than a normal decision tree but it offers the user more information about how certain the classification is.

  • 65.
    Gerdes, Mike
    et al.
    Department of Automotive and Aeronautical Engineering, HAW Hamburg.
    Galar, Diego
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Scholz, Dieter
    Hamburg University of Applied Sciences, Aero - Aircraft Design and Systems Group.
    Automated parameter optimization for feature extraction for condition monitoring2016Inngår i: 14th IMEKO TC10 Workshop on Technical Diagnostics 2016: New Perspectives in Measurements, Tools and Techniques for Systems Reliability, Maintainability and Safety, Milan, Italy, 27-28 June 2016, 2016, s. 452-457Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Pattern recognition and signal analysis can be used to support and simplify the monitoring of complex aircraft systems. For this purpose, information must be extracted from the gathered data in a proper way. The parameters of the signal analysis need to be chosen specifically for the monitored system to get the best pattern recognition accuracy. An optimization process to find a good parameter set for the signal analysis has been developed by the means of global heuristic search and optimization. The computed parameters deliver slightly (one to three percent) better results than the ones found by hand. In addition it is shown that not a full set of data samples is needed. It is also concluded that genetic optimization shows the best performance

  • 66.
    Gerdes, Mike
    et al.
    Department of Automotive and Aeronautical Engineering, HAW Hamburg.
    Galar, Diego
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Scholz, Dieter
    Hamburg University of Applied Sciences, Aero - Aircraft Design and Systems Group.
    Decision trees and the effects of feature extraction parameters for robust sensor network design2017Inngår i: Eksploatacja i Niezawodnosc - Maintenance and Reliability, ISSN 1507-2711, Vol. 19, nr 1, s. 31-42Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Reliable sensors and information are required for reliable condition monitoring. Complex systems are commonly monitored by many sensors for health assessment and operation purposes. When one of the sensors fails, the current state of the system cannot be calculated in same reliable way or the information about the current state will not be complete. Condition monitoring can still be used with an incomplete state, but the results may not represent the true condition of the system. This is especially true if the failed sensor monitors an important system parameter. There are two possibilities to handle sensor failure. One is to make the monitoring more complex by enabling it to work better with incomplete data; the other is to introduce hard or software redundancy. Sensor reliability is a critical part of a system. Not all sensors can be made redundant because of space, cost or environmental constraints. Sensors delivering significant information about the system state need to be redundant, but an error of less important sensors is acceptable. This paper shows how to calculate the significance of the information that a sensor gives about a system by using signal processing and decision trees. It also shows how signal processing parameters influence the classification rate of a decision tree and, thus, the information. Decision trees are used to calculate and order the features based on the information gain of each feature. During the method validation, they are used for failure classification to show the influence of different features on the classification performance. The paper concludes by analysing the results of experiments showing how the method can classify different errors with a 75% probability and how different feature extraction options influence the information gain

  • 67.
    Gerdes, Mike
    et al.
    Department of Automotive and Aeronautical Engineering, HAW Hamburg.
    Galar, Diego
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Scholz, Dieter
    Hamburg University of Applied Sciences, Aero - Aircraft Design and Systems Group.
    Genetic algorithms and decision trees for condition monitoring and prognosis of A320 aircraft air conditioning2017Inngår i: Insight (Northampton), ISSN 1354-2575, E-ISSN 1754-4904, Vol. 59, nr 8, s. 424-433Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Unscheduled maintenance is a large cost driver for airlines, but condition monitoring and prognosis can reduce the number of unscheduled maintenance actions. This paper discusses how condition monitoring can be introduced into most systems by adopting a data-driven approach and using existing data sources. The goal is to forecast the remaining useful life (RUL) of a system based on various sensor inputs. Decision trees are used to learn the characteristics of a system. The data for the decision tree training and classification are processed by a generic parametric signal analysis. To obtain the best classification results for the decision tree, the parameters are optimised by a genetic algorithm. A forest of three different decision trees with different signal analysis parameters is used as a classifier. The proposed method is validated with data from an A320 aircraft from Etihad Airways. Validation shows that condition monitoring can classify the sample data into ten predetermined categories, representing the total useful life (TUL) in 10% steps. This is used to predict the RUL. There are 350 false classifications out of 850 samples. Noise reduction reduces the outliers to nearly zero, making it possible to correctly predict condition. It is also possible to use the classification output to detect a maintenance action in the validation data.

  • 68.
    Gerdes, Mike
    et al.
    Department of Automotive and Aeronautical Engineering, HAW Hamburg.
    Scholz, Dieter
    Department of Automotive and Aeronautical Engineering, HAW Hamburg.
    Galar, Diego
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Effects of condition-based maintenance on costs caused by unscheduled maintenance of aircraft2016Inngår i: Journal of Quality in Maintenance Engineering, ISSN 1355-2511, E-ISSN 1758-7832, Vol. 22, nr 4, s. 394-417Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    PurposeThis paper analyses the effects of condition-based maintenance based on unscheduled maintenance delays that were caused by ATA chapter 21 (air conditioning). The goal is to show the introduction of condition monitoring in aircraft systemsDesign/methodology/approachThe research was done using the Airbus In-Service database to analyse the delay causes, delay length and to check if they are easy to detect via condition monitoring or not. These results were then combined with delay costs.FindingsAnalysis shows that about 80% of the maintenance actions that cause departure delays can be prevented when additional sensors are introduced. With already existing sensors it is possible to avoid about 20% of the delay causing maintenance actions.Research limitations/implicationsThe research is limited on the data of the Airbus In-Service Database and on ATA chapter 21 (air conditioning).Practical implicationsThe research shows that delays can be prevented by using existing sensors in the air-conditioning system for condition monitoring. More delays can be prevented by installing new sensors.Originality/valueThe research focuses on the effect of the air-conditioning system of an aircraft on the delay effects and the impact of condition monitoring on delays

  • 69.
    Ghodrati, Behzad
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Ahmadi, Alireza
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Galar, Diego
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Reliability analysis of switches and crossings: a case study in Swedish railway2013Konferansepaper (Fagfellevurdert)
    Abstract [en]

    It is reported that switches and crossings (S&C) are one of the subsystems that cause the most delays on Swedish Railways while accounting for at least 13% of maintenance costs [6]. It is the main reason why we chose to base our study on this subsystem.Intelligent data processing allows understanding the real reliability characteristics of the assets to be maintained. The first objective of this research is to determine the S&C reliability characteristics based on field data collection. Because field failure data are typically strongly censored, an especial statistics software package was developed to process field failure data, as commercial packages have not been found satisfactory in that respect. The resulting software, named RDAT® (Reliability Data Analysis Tool) has been relied upon for this study: it is especially adapted to statistical failure data analysis.In the next step the availability of studied switches and crossings is estimated based on the reliability characteristics founded in the first step.

  • 70.
    Ghodrati, Behzad
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Ahmadi, Alireza
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Galar, Diego
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Reliability Analysis of Switches and Crossings: A Case Study in Swedish Railway2017Inngår i: International Journal of Railway Research, ISSN 2361-5376, Vol. 4, nr 1, s. 1-12Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    It is reported that switches and crossings (S&C) are one of the subsystems that cause the most delays on Swedish Railways while accounting for at least 13% of maintenance costs [6]. It is the main reason why we chose to base our study on this subsystem.

    Intelligent data processing allows understanding the real reliability characteristics of the assets to be maintained. The first objective of this research is to determine the S&C reliability characteristics based on field data collection. Because field failure data are typically strongly censored, an especial statistics software package was developed to process field failure data, as commercial packages have not been found satisfactory in that respect. The resulting software, named RDAT® (Reliability Data Analysis Tool) has been relied upon for this study: it is especially adapted to statistical failure data analysis.

    In the next step the availability of studied switches and crossings is estimated based on the reliability characteristics founded in the first step.

  • 71.
    Ghodrati, Behzad
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Ahmadi, Alireza
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Galar, Diego
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Spare parts estimation for machine availability improvement addressing its reliability and operating environment: case study2013Inngår i: International Journal of Reliability, Quality and Safety Engineering (IJRQSE), ISSN 0218-5393, Vol. 20, nr 3Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Industrial operation cost analysis shows that, in general, maintenance represents a significant proportion of the overall operating costs. Therefore, the improvement of maintenance follows the final goal of any company, namely, to maximize profit. This paper studies spare parts availability, an issue of the maintenance process, which is an important way to improve production through increased availability of functional machinery and subsequent minimization of the total production cost. Spare parts estimation based on machine reliability characteristics and operating environment is performed. The study uses an improved statistical-reliability (S-R) approach which incorporates the system/machine operating environment information in systems reliability analysis. For this purpose, two methods of Poisson process and renewal process are introduced and discussed. The renewal process model uses a multiple regression type of analysis based on Cox’s proportional hazards modeling (PHM). The parametric approaches with baseline Weibull hazard functions and time independent covariates are considered, and the influence of operating environment factors on this model is analyzed. The outputs represent a significant difference in the required spare parts estimation when considering or ignoring the influence of the relevant system operating environment. The difference is significant in the sense of spare parts forecasting and inventory management which can enhance the parts and consequently machine availability, leading to economical operation and savings.

  • 72.
    González-González, Asier
    et al.
    Tecnalia Research and Innovation, Industry and Transport Division, Miñano.
    Galar, Diego
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Condition monitoring of wind turbine pitch controller: A maintenance approach2017Inngår i: 15th IMEKO TC10 Workshop on Technical Diagnostics 2017: "Technical Diagnostics in Cyber-Physical Era", 2017, s. 200-206Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Due to the wind power capacity energy grow exponential, interest in operation maintenance is increasing. A proper pitch controller must be designed to extend the life cycle of some wind turbine (WT) components such as blades or tower. The pitch control system has two main, but conflicting, objectives. On the one hand, it seeks to maximize the wind energy captured and converted into electrical energy. On the other hand, it seeks to minimize fatigue and mechanical load. Various metrics are proposed to achieve a compromise solution that balances these objectives. A WT of 100 kW is used to validate pitch control strategies

  • 73.
    González-González, Asier
    et al.
    Tecnalia Research and Innovation, Industry and Transport Division, Miñano.
    Jimenez Cortadi, Alberto
    Tecnalia Research and Innovation, Industry and Transport Division, Miñano.
    Galar, Diego
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik. Tecnalia Research and Innovation, Industry and Transport Division, Miñano .
    Ciani, Lorenzo
    University of Florence, Department of Information Engineering.
    Condition Monitoring of Wind Turbine Pitch Controller: A Maintenance Approach2018Inngår i: Measurement, ISSN 0263-2241, E-ISSN 1873-412X, Vol. 123, s. 80-93Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    With the increase of wind power capacity worldwide, researchers are focusing their attention on the operation and maintenance of wind turbines. A proper pitch controller must be designed to extend the life cycle of a wind turbine’s blades and tower. The pitch control system has two main, but conflicting, objectives: to maximize the wind energy captured and converted into electrical energy and to minimize fatigue and mechanical load. Four metrics have been proposed to balance these two objectives. Also, diverse pitch controller strategies are proposed in this paper to evaluate these objectives. This paper proposes a novel metrics approach to achieve the conflicting objectives with a maintenance focus. It uses a 100 kW wind turbine as a case study to simulate the proposed pitch control strategies and evaluate with the metrics proposed. The results are showed in two tables due to two different wind models are used.

  • 74.
    Gustafson, Anna
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Galar, Diego
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Schunnesson, Håkan
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Geoteknologi.
    Heyns, Stephan
    Division of Structural Mechanics, Pretoria.
    Fusion of production, operation and maintenance data for underground mobile mining equipment2012Inngår i: The Ninth International Conference on Condition Monitoring and Machinery Failure Prevention Technologies, 2012, Vol. 2, s. 783-791Konferansepaper (Fagfellevurdert)
    Abstract [en]

    For integration purposes, a data collection and distribution system based on the concept of cloud computing could be possible to use for collection of data or information pertaining from various sources of data. From a maintenance point of view, the benefit of cloud computing is that information or data may be collected on the health, variability, performance or utilization of the asset. It is especially useful in data mining where different types of data of different quality must be integrated. This paper discusses the concept and presents one example from the underground mining industry.

  • 75.
    Gustafson, Anna
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Geoteknologi.
    Lipsett, Michael
    Mechanical Engineering, University of Alberta, Edmonton.
    Schunnesson, Håkan
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Geoteknologi.
    Galar, Diego
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Kumar, Uday
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Development of a Markov model for production performance optimisation: Application for semi-automatic and manual LHD machines in underground mines2014Inngår i: International Journal of Mining, Reclamation and Environment, ISSN 1748-0930, E-ISSN 1748-0949, Vol. 28, nr 5, s. 342-355Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    This paper compares three ways to operate a load haul dump (LHD) machine, manual operation, automatic operation (fleet operation) and semi-automatic operation, to find the best operating mode. In a fault tree analysis, different failures are classified and analysed, but the way to recover from certain states is not accounted for, which is something a Markov model can handle. The paper is based on the analysis of real data from an underground mine. A Markov model has been built for mining application and it is shown that a semi-automatic LHD has the highest probability of being in a productive state since it has the advantage of changing operating modes (manual and automatic) depending on the need and situation. Hence, the semi-automatic LHD is the best choice from an operational point of view. The paper fills a gap in the literature on manual vs. automatically operated LHDs by providing a new way of evaluating the operating mode of LHDs using Markov modelling, while considering the operating environment.

  • 76.
    Gustafson, Anna
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Schunnesson, Håkan
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Galar, Diego
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Maintenance indicators for underground mining equipment: a case study of automatically versus manually operated LHD machines2011Inngår i: Proceedings of the 24th International Congress on Condition Monitoring and Diagnosis Engineering Management: COMADEM 2011 / [ed] Maneesh Singh; Raj B.K.N. Rao; J.P. Liyanage, Stavanger: COMADEM International, 2011, s. 1205-1214Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Key Performance Indicators (KPIs) are performance measures directly related to the overall goals of the company and some of them depend on the maintenance function. In mining companies top managers use the maintenance cost per unit versus budget as one of the KPIs. However many other technical, organizational and economical parameters in a company can be helpful during the decision making process.In this paper the productivity of Load-Haul-Dump machines (LHDs), that is obtained when manual and/or automatic mode are used, are being analysed. The correlation between the productivity and the maintenance KPIs as well as the issues related to the acquisition of data will be shown in this paper highlighting the complexity of getting accurate decision process parameters. It is recognized that the data for some of the components and failure modes originating from different sources are not compatible. This situation must be considered when compiling the data, especially to permit comparison the data should be made compatible. The problem of incompatibility is most severe when dealing with demand related failures. The philosophy and mechanisms of demand related failures as well as the methods used to denote the time and demand related failures in common form have to be taken into account.

  • 77.
    Gustafson, Anna
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Schunnesson, Håkan
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Geoteknologi.
    Galar, Diego
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    TPM framework for underground mobile mining equipment: a case study2011Konferansepaper (Fagfellevurdert)
    Abstract [en]

    In underground mines, mobile mining equipment is critical to the production system. Drill rigs for development and production, vehicles for charging holes, LHDs for loading and transportation, scaling rigs and rigs for reinforcement and cable bolting are all important units in the process to generate a continuous ore flow. For today’s mining companies, high equipment availability is essential to reduce operational and capital costs and to maintain high production. High and controllable reliability is also important especially in attempts to automate the production equipment. This paper compares existing maintenance work in a Swedish and a Tanzanian mine. The various maintenance procedures are identified and evaluated based on a TPM framework.

  • 78.
    Gustafson, Anna
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Schunnesson, Håkan
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser.
    Galar, Diego
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Kumar, Uday
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Production and maintenance performance analysis: manual versus semi-automatic LHDs2013Inngår i: Journal of Quality in Maintenance Engineering, ISSN 1355-2511, E-ISSN 1758-7832, Vol. 19, nr 1, s. 74-88Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Purpose – The purpose of this paper is to evaluate and analyse the production and maintenance performance of a manual and a semi-automatic Load Haul Dump (LHD machine to find similarities and differences.Design/methodology/approach – Real time process-, operational- and maintenance data, from an underground mine in Sweden, have been refined and aggregated into KPIs in order to make the comparison between the LHDs.Findings – The main finding is the demonstration of how production and maintenance data can be improved through information fusion, showing some unexpected result for maintenance of automatic and semi-automatic LHDs in the mining industry. It was found that up to one third of the manually entered workshop data are not consistent with the automatically recorded production times. It is found that there are similarities in utilization and filling rate but differences in produced tonnes/machine hour between the two machines.Originality/value – The originality in this paper is the information fusion between automatically produced production data and maintenance data which increases the accuracy of reliability analysis data. Combining the production indicator and the maintenance indicator gives a common tool to the production and maintenance departments. This paper shows the difference in both maintenance and production performance between a manual and semi-automatic LHD.

  • 79.
    Gustafson, Anna
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Schunnesson, Håkan
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser.
    Galar, Diego
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Kumar, Uday
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    The influence of the operating environment on manual and automated load-haul-dump machines: a fault tree analysis2013Inngår i: International Journal of Mining, Reclamation and Environment, ISSN 1748-0930, E-ISSN 1748-0949, Vol. 27, nr 2, s. 75-87Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    The automated load-haul-dump (LHD) machines have the potential to increaseproductivity and improve safety, but there are many issues to be considered when optimising the operation of LHDs. Today’s focus on improved equipment reliability is part of the problem, and another difficult issue is the special conditions and constraints of the operating environment. For automated LHDs, the latter issue is even more important, as humans have been removed from the production area and are not close by to solve the problems. The purpose of this paper is to find the causes of LHD idle time and to study their impact on the operation of LHDs. In this study, real-time process data and maintenance data from an underground mine in Sweden have been refined and integrated. The study takes into account the complexity of the mine environment, discusses the factors to be considered when optimising and automating the operation and uses fault tree analysis (FTA) to analyse the idle time.

  • 80.
    Hernandez, Angel
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Galar, Diego
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Techniques of Prognostics for Condition-Based Maintenance in Different Types of Assets2014Rapport (Fagfellevurdert)
  • 81.
    Hernandez, Angel
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Galar, Diego
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Perales, Numan
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Procedure for rul estimation in industrial assets2014Inngår i: Proceedings of the 3rd international workshop and congress on eMaintenance: June 17-18 Luleå, Sweden : eMaintenance, Trends in technologies & methodologies, challenges, possibilites and applications / [ed] Uday Kumar; Ramin Karim; Aditya Parida; Philip Tretten, Luleå: Luleå tekniska universitet, 2014, s. 145-149Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Today, prognosis is recognized as a key element of maintenance.However, the implementation of an efficient prognosis tool can becomplicated in industrial and academic sectors, when we speakabout academic sector , we refer to the research centers atuniversities who study the progress and new technologies relatedto prognosis, these centers are very important as they help theimprove maintenance management in the industries. Since it isdifficult to create effective models for different industrial assets.In this context, our general objective is to propose a procedure forimplementing prognosis, from selecting the system or componentto be analyzed to obtaining the estimation of the remaining usefullife. We also explain different approaches to forecasting toestimate remaining useful life, the main objective of prognosis.

  • 82.
    Horenbeek, Adriaan Van
    et al.
    Centre for Industrial management, KU Leuven.
    Pintelon, Liliane
    Centre for Industrial management, KU Leuven.
    Galar, Diego
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Integration of disparate data sources to perform maintenance prognosis and optimal decision making2012Inngår i: The Ninth International Conference on Condition Monitoring and Machinery Failure Prevention Technologies, 2012, Vol. 1, s. 386-397Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Prognosis can be defined as the course of predicting a failure of equipment or a component in advance, whereas prognostication refers to the act of prediction. The three main branches of condition based maintenance are diagnosis, prognosis, and treatment-prognosis, however prognosis is admittedly the most difficult. Also, this area has been the least described in literature and the knowledge about it in a maintenance management context is still poorly systematized. To this day, formal professional attention to prognosis, in the field of maintenance management and engineering in the everyday care of machinery, is often relegated to a secondary status although the availability of prognostic information can considerably improve (e.g. reduce costs, maximize uptime) the performance of machinery and maintenance processes. Ideally, assessment of a prognosis of remaining useful life should be deliberate and explicit. In order to support the maintenance crew in the achievement of this objective an increasing amount of prognostic information is available. Over the last decade, system integration has grown in popularity as it allows organizations to streamline business processes. It is necessary to integrate management data from CMMS (Computer Maintenance Management Systems) with CM (Condition Monitoring) systems and finally SCADA (Supervisory Control And Data Acquisition) and other control systems, widely used in production but with a seldom usage in asset diagnosis and prognosis. The most obvious obstacle in the integration of these data is the disparate nature of the data types involved, moreover several attempts to remedy this problem have fizzled out. Although there have been many recent efforts to collect and maintain large repositories of these types of data, there have been relatively few studies to identify the ways these datasets could be related and linked for prognosis and maintenance decision making. After identifying what and how to predict incipient failures and developing a corresponding prognosis, maintenance engineers must consider how to communicate the prediction. In this activity once again, technicians' psychosocial attributes and values may influence how they discuss prognoses with asset managers. Regardless of whether prognostic assessments are subjective or objective, however, technicians should consider two major points. Firstly, the maintenance crew should clarify in their own minds the link, if any, between their prognostic assessment and their consequent decision making. Secondly, they should consider the ways that they and their assets might benefit from explicitly discussing how the prognostic assessment is linked with diagnostics and preventive maintenance recommendations. These and other steps that maintenance engineers should take in incorporating prognostic information into their decision making are discussed in this paper. The objective is to give an overview of how the integration of disparate data sources, commonly available in industry, can be achieved for maintenance prognosis and optimal decision making.

  • 83.
    Hoseinie, Hadi
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Ghodrati, Behzad
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Galar, Diego
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Juuso, Esko
    University of Oulu, Control Engineering Group, Faculty of Technology, University of Oulu.
    Optimal Preventive Maintenance Planning for Water Spray System of Drum Shearer2015Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Water spray system is one of the most important parts of rock cutting machines, especially the drum shearer. Field data shows that the maintenance of this system is time-consuming and causes major downtimes in the coal mines’ production process. Therefore, it is essential to find an optimum preventive maintenance task and intervals, to reduce the downtime and minimize the associated costs of the machine. In this paper, in order to suggest an optimum preventive maintenance plan, a parametric failure and reliability analysis was done on available data from an Iranian longwall coal mine over the two years. A reliability-based cost modelling was implemented to identify the optimum maintenance interval and frequencies of restoration for the water spray system. In the study, a cost rate function was introduced in which an as-good-as-new effectiveness for restoration actions is considered. The results of the analysis showed that the minimum maintenance cost per unit of time for the studied machine, $19.54/hour, will be achieved within a range of intervals i.e. T=136 hours to T=142 hours.

  • 84.
    Johansson, Carl-Anders
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Galar, Diego
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Villarejo, Roberto
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Monnin, Maxime
    PREDICT.
    Green Condition based Maintenance - an integrated system approach for health assessment and energy optimization of manufacturing machines.2013Inngår i: 10th International Conference on Condition Monitoring and Machinery Failure Prevention Technologies 2013, CM 2013 and MFPT 2013, 2013, Vol. 2, s. 1069-1084Konferansepaper (Fagfellevurdert)
    Abstract [en]

    The normal strategy to keep production systems in good conditions is to apply preventive maintenance practices, with a supportive workforce "reactive" in the case of clearly detected malfunctions. This impact on quality, cost and in general, productivity. Added to this, the uncertainty of machine reliability at any given time, also impacts on product/production delivery times. It is known also that a worn-out mechanism can have higher energy consumption. The use of intelligent predictive technologies could contribute to improve the situation, but these techniques are not widely used in the production environment. Often sensors and monitors required for the production environment are non-standard and require costly implementations. Monitoring and profiling the electric current consumption in combination with operational data is an easy to implement Green Condition based Maintenance (Green CBM) technique to improve the overall business effectiveness, under a triple perspective: • Optimizing maintenance strategies based on the prediction of potential failures and schedule maintenance operations in convenient periods and avoid unexpected breakdowns • Operation: Managing energy as a production resource and reduce its consumption • Product reliability: Providing the machine tool builder with real data about the behaviour of the product and their critical components This also opens for new business models for maintenance and service providers. The described Green CBM technique can be applied in many types of machines. In machine tools, focusing on spindles and linear guides, as responsible for the most common and cost-intensive downtimes

  • 85.
    Johansson, Carl-Anders
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Simon, Victor
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Galar, Diego
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Aggregation of electric current consumption features for extraction of maintenance KPIs2014Inngår i: Proceedings of Maintenance Performance Measurement and Management (MPMM) Conference 2014 / [ed] José Torres Farinha; Diego Galar, Coimbra: Faculdade de Ciências e Tecnologia da Universidade de Coimbra, Departamento de Engenharia Mecânica , 2014, s. 157-162Konferansepaper (Fagfellevurdert)
    Abstract [en]

    For all electric powered machines there is apossibility of extracting information and calculating KeyPerformance Indicators (KPIs) from the electric current signal.Depending on the time window, sampling frequency and type ofanalysis, different indicators from the micro to macro level canbe calculated for such aspects as maintenance, production,energy consumption etc.On the micro-level, the indicators are generally used forcondition monitoring and diagnostics and are normally based ona short time window and a high sampling frequency. The macroindicators are normally based on a longer time window with aslower sampling frequency and are used as indicators for overallperformance, cost or consumption.The indicators can be calculated directly from the currentsignal but can also be based on a combination of informationfrom the current signal and operational data like rpm, positionetc.One or several of those indicators can be used for predictionand prognostics of a machine’s future behaviour.This paper uses this technique to calculate indicators formaintenance and energy optimisation in electric poweredmachines and fleet of machines, especially machine tools.

  • 86.
    Johansson, Carl-Anders
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Simon, Victor
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Galar, Diego
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Context Driven Remaining Useful Life Estimation2014Inngår i: Procedia CIRP, ISSN 2212-8271, E-ISSN 2212-8271, Vol. 22, s. 181-185Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    In the context of maintenance activities maintainers rely on machine information, their past breakdowns, adequate repair methods and guidelines as well as new research results in the area. They usually get access to information and knowledge by using information systems (nondestructive testing (NDT) or condition monitoring.), local databases, e-resources or traditional print media. Basically it can be assumed that, the amount of available information affects the quality of maintenance decision making and acting positively. Machine health information retrieval is the application of information retrieval concepts and techniques to the operation and maintenance domain. Retrieving Contextual information, describing the operational conditions for the machine, is a subarea of information retrieval that incorporates context features in the search process towards its improvement. Both areas have been gaining interest from the research community in order to perform more accurate prognostics according to specific scenarios and happening circumstances. Context is a broad term and in this paper the operational conditions and the way the machine has been used is seen as the context and is represented by operational data collected over time. This paper intends to investigate the effects of the interaction of context features on machine tools health information. This interaction between context and health assessment is bidirectional in the sense that health information seeking behavior can also be used to predict context features that can be used, without disturbing the operational environment and creating production disruptions.The extraction of multiple features from multiple sensors, already deployed in this type of machinery, may constitute snapshots of the current health of certain machine components. The mutation status (the way they have changed) of these snapshots, hereafter called Fingerprints, has been proposed as prognostic marker in machine tools problems. Of them, in this work so far only the spindle fingerprint mutation has been validated independently as prognostic for overall survival and survival after relapse, while the prognostic value of rest of components mutation is still under validation. In this scenario, the prognostic value of spindle fingerprint mutations can be investigated in various contexts defined by stratifications of the machine population.

  • 87.
    Juuso, Esko
    et al.
    University of Oulu, Control Engineering, University of Oulu.
    Galar, Diego
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Intelligent Real-Time Risk Analysis for Machines and Process Devices2016Inngår i: Current Trends in Reliability, Availability, Maintainability and Safety: An Industry Perspective / [ed] Uday Kumar; Alireza Ahmadi; Ajit Kumar Verma; Prabhakar Varde, Encyclopedia of Global Archaeology/Springer Verlag, 2016, s. 229-240Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Automatic fault detection with condition and stress indices enables reliable condition monitoring to be combined with process control. Useful information on different faults can be obtained by selecting suitable features. Generalised norms can be defined by the order of derivation, the order of the moment and sample time. These norms have the same dimensions as the corresponding signals. The nonlinear scaling used in the linguistic equation approach extends the idea of dimensionless indices to nonlinear systems. The Wöhler curve is represented by a linguistic equation (LE) model. The contribution of the stress is calculated in each sample time, which is taken as a fraction of the cycle time. The cumulative sum of the contributions indicates the degrading of condition and the simulated sums can be used to predict failure time. To avoid high stress situations, the statistical process control (SPC) is extended to nonlinear and non-Gaussian data: the new generalised SPC is suitable for a large set of statistical distributions. It operates without interruptions in short run cases and adapts to the changing process requirements. The scaling functions are updated recursively, which is triggered by a fast increase of the deviation indices. The higher levels, which are rough estimates in the beginning, are gradually refined.

  • 88.
    Kans, Mirka
    et al.
    Linnaeus University.
    Galar, Diego
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Thaduri, Adithya
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Maintenance 4.0 in railway transportation industry2015Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Transportation systems are complex with respect to technology and operations with involvement in a wide range of human actors, organizations and technical solutions. For the operations and control of such complex environments, a viable solution is to apply intelligent computerized systems, such as computerized traffic control systems for coordinating airline transportation, or advanced monitoring and diagnostic systems in vehicles. Moreover, transportation assets cannot compromise the safety of the passengers by applying operation and maintenance activities. Indeed safety becomes a more difficult goal to achieve using traditional maintenance strategies and computerized solutions come into the picture as the only option to deal with complex systems interacting among them trying to balance the growth in technical complexity together with stable and acceptable dependability indexes. Industry 4.0 is a term that describes the fourth generation of industrial activity which is enabled by smart systems and Internet-based solutions. Two of the characteristic features of Industry 4.0 are computerization by utilizing cyber-physical systems and intelligent factories that are based on the concept of "internet of things". Maintenance is one of the application areas, referred to as maintenance 4.0, in form of self-learning and smart systems that predicts failure, makes diagnosis and triggers maintenance by making use of “internet of things”. This paper discusses the possibilities that lie within applying the maintenance 4.0 concept in the railway transportation industry. This paper also discusses the positive effects on technology; organisation and operations from a systems perspective.

  • 89.
    Kans, Mirka
    et al.
    Linnaeus University.
    Galar, Diego
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Thaduri, Adithya
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Maintenance 4.0 in Railway Transportation Industry2016Inngår i: Proceedings of the 10th World Congress on Engineering Asset Management (WCEAM 2015) / [ed] Kari T. Koskinen; Helena Kortelainen; Jussi Aaltonen; Teuvo Uusitalo; Kari Komonen; Joseph Mathew; Jouko Laitinen, Encyclopedia of Global Archaeology/Springer Verlag, 2016, s. 317-331Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Transportation systems are complex with respect to technology and operations with involvement in a wide range of human actors, organisations and technical solutions. For the operations and control of such complex environments, a viable solution is to apply intelligent computerised systems, such as computerised traffic control systems for coordinating airline transportation, or advanced monitoring and diagnostic systems in vehicles. Moreover, transportation assets cannot compromise the safety of the passengers by applying operation and maintenance activities. Indeed safety becomes a more difficult goal to achieve using traditional maintenance strategies and computerised solutions come into the picture as the only option to deal with complex systems interacting among them trying to balance the growth in technical complexity together with stable and acceptable dependability indexes. Industry 4.0 is a term that describes the fourth generation of industrial activity which is enabled by smart systems and Internet-based solutions. Two of the characteristic features of Industry 4.0 are computerization by utilising cyber-physical systems and intelligent factories that are based on the concept of “internet of things”. Maintenance is one of the application areas, referred to as maintenance 4.0, in form of self-learning and smart systems that predicts failure, makes diagnosis and triggers maintenance by making use of “internet of things”. This paper discusses the possibilities that lie within applying the maintenance 4.0 concept in the railway transportation industry and the positive effects on technology, organisation and operations from a systems perspective.

  • 90.
    Kapur, P.K.
    et al.
    Amity University.
    Srividya, A.
    University College, Haugesund.
    Galar, Diego
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Foreword2013Inngår i: International Journal of Reliability, Quality and Safety Engineering (IJRQSE), ISSN 0218-5393, Vol. 20, nr 3, s. 1-2Artikkel i tidsskrift (Annet vitenskapelig)
  • 91.
    Karadimou, Eva
    et al.
    York EMC Services.
    Armstrong, Robert A.
    York EMC Services.
    Adin, Iñigo
    CEIT.
    Deniau, Virgine
    IFSTTAR.
    Rodriguez, Joseba P.
    CAF-ID.
    Galar, Diego
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Niska, Stefan
    Trafikverket.
    Tamarit, Jaime
    CEDEX, Centro de estudios y experimentación de obras públicas.
    An EMC study on the interopability of the European railway network2015Inngår i: IEEE International Symposium on Electromagnetic Compatibility (EMC), 2015 [joint conference with] EMC Europe: 16-22 Aug. 2015, Dresden, Piscataway, NJ: IEEE Communications Society, 2015, s. 428-433, artikkel-id 7256200Konferansepaper (Fagfellevurdert)
    Abstract [en]

    The research presented here deals with the electromagnetic compatibility in the railway environment. In particular it focuses on four research areas: the spot signalling systems, the track circuits, the GSM-R and the broadcasting services. A review of the current railway standards is followed by a research on the immunity limits, the worst case scenarios and cross acceptance EMC tests for the four areas

  • 92.
    Karim, Ramin
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Westerberg, Jesper
    eMaintenance365 AB.
    Kumar, Uday
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Galar, Diego
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Maintenance Analytics: The New Know in Maintenance2016Inngår i: IFAC-PapersOnLine, ISSN 2405-8963, Vol. 49, nr 28, s. 214-219Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Decision-making in maintenance has to be augmented to instantly understand and efficiently act, i.e. the new know. The new know in maintenance needs to focus on two aspects of knowing: 1) what can be known and 2) what must be known, in order to enable the maintenance decision-makers to take appropriate actions. Hence, the purpose of this paper is to propose a concept for knowledge discovery in maintenance with focus on Big Data and analytics. The concept is called Maintenance Analytics (MA). MA focuses in the new knowledge discovery in maintenance. MA addresses the process of discovery, understanding, and communication of maintenance data from four time-related perspectives, i.e. 1) “Maintenance Descriptive Analytics (monitoring)”; 2) “Maintenance Diagnostic Analytics”; 3) “Maintenance Predictive Analytics”; and 4) “Maintenance Prescriptive analytics”.

  • 93.
    Kumar, Uday
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Galar, Diego
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Maintenance in the Era of Industry 4.0: Issues and Challenges2018Inngår i: Quality, IT and Business Operations: Modeling and Optimization / [ed] Kapur P., Kumar U., Verma A., Singapore: Springer, 2018, s. 231-250Kapittel i bok, del av antologi (Fagfellevurdert)
    Abstract [en]

    The fourth generation of industrial activity enabled by smart systems and Internet-based solutions is known as Industry 4.0. Two most important characteristic features of Industry 4.0 are computerization using cyber-physical systems and the concept of “Internet of Things” adopted to produce intelligent factories. As more and more devices are instrumented, interconnected and automated to meet this vision, the strategic thinking of modern-day industry has been focused on deployment of maintenance technologies to ensure failure-free operation and delivery of services as planned.

    Maintenance is one of the application areas, referred to as Maintenance 4.0, in the form of self-learning and smart system that predicts failure, makes diagnosis and triggers maintenance. The paper addresses the new trends in manufacturing technology based on the capability of instrumentation, interconnection and intelligence together with the associated maintenance challenges in the era of collaborative machine community and big data environment.

    The paper briefly introduces the concept of Industry 4.0 and presents maintenance solutions aligned to the need of the next generation of manufacturing technologies and processes being deployed to realize the vision of Industry 4.0.The suggested maintenance approach to deal with new challenges due to the implementation of industry 4.0 is captured within the framework of eMaintenance solutions developed using maintenance analytics. The paper is exploratory in nature and is based on literature review and study of the current development in maintenance practices aligned to industry 4.0.

  • 94.
    Kumar, Uday
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Galar, Diego
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Parida, Aditya
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Stenström, Christer
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Maintenance audits using balanced scorecard and maturity model2011Inngår i: Maintworld, ISSN 1798-7024, E-ISSN 1799-8670, nr 3, s. 34-40Artikkel i tidsskrift (Annet vitenskapelig)
    Abstract [en]

    There is increasing interest in the use of maintenance performance measurement (MPM) and the possibility of using the maintenance audits for benchmarking metrics. This article proposes a methodology for simple measurement, one that accepts the indicators used on a scorecard with four perspectives and is hierarchized according to organizational level. The maintenance audit will evaluate the degree of fulfillment of objectives and the degree of satisfaction obtained from each of those perspectives. It will provide a clear picture of the current status of maintenance organization and the success of implemented policies taking into account the maintenance maturity model, i.e, the logical evolution of the maintenance function in the company.

  • 95.
    Kumar, Uday
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Galar, Diego
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Parida, Aditya
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Stenström, Christer
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Berges, L.
    University of Zaragoza.
    Maintenance performance metrics: a state of the art review2011Inngår i: MPMM 2011: Maintenance Performance Measurement & Management: Conference Proceedings / [ed] Diego Galar; Aditya Parida; Håkan Schunnesson; Uday Kumar, Luleå: Luleå tekniska universitet, 2011, s. 3-34Konferansepaper (Fagfellevurdert)
    Abstract [en]

    This paper provides an overview of research and developments in the measurement of maintenance performance. It considers the problems of various measuring parameters and comments on the lack of structure in, and references for, the measurement of maintenance performance.The main focus is to determine how value can be created for organizations by measuring maintenance performance, looking at such maintenance strategies as condition based maintenance, reliability centered maintenance, e-maintenance etc. In other words, the objectives are to find frameworks or models that can be used to evaluate different maintenance strategies and determine the value of these frameworks for an organization.The paper asks the following research questions:- What approaches and techniques are used for Maintenance Performance Measurement (MPM) and which MPM techniques are optimal for evaluating maintenance strategies?- In general, how can MPM create value for organizations, and more specifically, which system of measurement is best for which maintenance strategy?The body of knowledge on maintenance performance is both quantitative and qualitative based. Quantitative approaches include economic and technical ratios, value-based and balanced scorecards, system audits, composite formulations, and statistical and partial maintenance productivity indices. Qualitative approaches include human factors, amongst others. Qualitative-based approaches are adopted because of the inherent limitations of effectively measuring a complex function such as maintenance through quantitative models. Maintenance decision makers often come to the best conclusion using heuristics, backed up by qualitative assessment, supported by quantitative measures. Both maintenance performance perspectives are included in this overview.

  • 96.
    Kumar, Uday
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Galar, Diego
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Parida, Aditya
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Stenström, Christer
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Berges, Luis
    University of Zaragoza.
    Maintenance performance metrics: a state-of-the-art review2013Inngår i: Journal of Quality in Maintenance Engineering, ISSN 1355-2511, E-ISSN 1758-7832, Vol. 19, nr 3, s. 233-277Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Purpose - This paper provides an overview of research and development in the measurement of maintenance performance. It considers the problems of various measuring parameters and comments on the lack of structure in and references for the measurement of maintenance performance. The main focus is to determine how value can be created for organizations by measuring maintenance performance, examining such maintenance strategies as condition-based maintenance, reliability-centred maintenance, e-maintenance, etc. In other words, the objectives are to find frameworks or models that can be used to evaluate different maintenance strategies and determine the value of these frameworks for an organization.Design/methodology/approach - A state-of-the-art literature review has been carried out to answer the following two research questions. Firstly, what approaches and techniques are used for maintenance performance measurement (MPM) and which MPM techniques are optimal for evaluating maintenance strategies? Secondly, in general, how can MPM create value for organizations and, more specifically, which system of measurement is best for which maintenance strategy?Findings - The body of knowledge on maintenance performance is both quantitatively and qualitatively based. Quantitative approaches include economic and technical ratios, value-based and balanced scorecards, system audits, composite formulations, and statistical and partial maintenance productivity indices. Qualitative approaches include human factors, amongst other aspects. Qualitatively based approaches are adopted because of the inherent limitations of effectively measuring a complex function such as maintenance through quantitative models. Maintenance decision makers often come to the best conclusion using heuristics, backed up by qualitative assessment, supported by quantitative measures. Both maintenance performance perspectives are included in this overview.Originality/value - A comprehensive review of maintenance performance metrics is offered, aiming to give, in a condensed form, an extensive introduction to MPM and a presentation of the state of the art in this field.

  • 97.
    Lamban, Pilar
    et al.
    Departamento de Ingeniería de Diseño y Fabricación, Universidad de Zaragoza.
    Royo, Jesus
    Departamento de Ingeniería de Diseño y Fabricación, Universidad de Zaragoza.
    Valencia, Javier
    Departamento de Ingeniería de Diseño y Fabricación, Universidad de Zaragoza.
    Berges, Luis
    Departamento de Ingeniería de Diseño y Fabricación, Universidad de Zaragoza.
    Galar, Diego
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Modelo para el cálculo del costo de almacenamiento de un producto: caso de estudio en un entorno logístico2013Inngår i: Dyna, ISSN 0012-7353, Vol. 80, nr 179, s. 23-32Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Several authors have established how important it is for companies to have accurate product cost information, especially in the actual environment of intense global competition. However, it has been shown that traditional systems do not satisfy these business demands, so in recent years new cost methods have been proposed, nevertheless these are still inaccurate. It is because of this situation that this paper presents a new methodology for determining the storage cost of a product that can be extrapolated to all the links of the supply chain. In turn, we propose a new cost driver, the logistics index, which helps to provide more precise information than traditional methods. It concludes by showing a business case where this model is implemented in a Spanish logistics company.

  • 98.
    Lemma, Yonas
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Famurewa, Stephen Mayowa
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Galar, Diego
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Schunnesson, Håkan
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Geoteknologi.
    Fjellner, Jonas
    Boliden AB.
    CMMS benchmarking development in mining industries2012Inngår i: Proceedings of the 2nd International Workshop & Congress on eMaintenance: Dec 12-14 Luleå, Sweden : eMaintenace: trends in technologies and methodologies, challenges, possibilities and applications / [ed] Ramin Karim; Aditya Parida; Uday Kumar, Luleå: Luleå tekniska universitet, 2012, s. 211-218Konferansepaper (Fagfellevurdert)
  • 99.
    Leturiondo, Urko
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik. IK4-Ikerlan.
    Galar, Diego
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Salgado, Oscar
    IK4-Ikerlan.
    Hybrid modelling in condition-based maintenance for smart assets2016Konferansepaper (Fagfellevurdert)
    Abstract [en]

    When it comes to take proper maintenance decisions regarding reliability and safety of a system, there is a need to perform a right health assessment. Thus, acquiring signals from the system in healthy and damaged conditions gives the chance to analyse the effect of the state of the system on its response. However, it is usually hard to perform diagnosis and prognosis using only tests from the real system. The advances in technologies involving internet of things, cloud computing and big data lead to a situation in which this analysis of acquired data can be complemented by the use physics-based modelling. Thus, a combination of both data-driven and physics-based approaches can be implemented thanks to the aforementioned progress. In this paper an architecture to implement hybrid modelling is proposed, based on data fusion between real data and synthetic data obtained by simulations of a physics-based model. This architecture has two analysis levels: an online process carried out in a local basis and virtual commissioning performed in the cloud. The former results in failure detection analysis for avoiding upcoming failures whereas the latter has as aim a further analysis involving both diagnosis and prognosis.

  • 100.
    Leturiondo, Urko
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik. IK4-Ikerlan.
    Mishra, Madhav
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Galar, Diego
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Salgado, Oscar
    IK4-Ikerlan.
    Synthetic data generation in hybrid modelling of rolling element bearings2015Inngår i: Insight (Northampton), ISSN 1354-2575, E-ISSN 1754-4904, Vol. 57, nr 7, s. 395-400Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Diagnosis and prognosis processes are necessary to optimise the dependability of systems and ensure their safe operation. If there is a lack of information, faulty conditions cannot be identified and undesired events cannot be predicted. It is essential to predict such events and mitigate risks, but this is difficult in complex systems.Abnormal or unknown faults cause problems for maintenance decision makers. We therefore propose a methodology that fuses data-driven and model-based approaches. Real data acquired from a real system and synthetic data generated from a physical model can be used together to perform diagnosis and prognosis.As systems have time-varying conditions related to both the operating condi- tions and the healthy or faulty state of systems, the idea behind the proposed methodology is to generate synthetic data in the whole range of conditions in which a system can work. Thus, data related to the context in which the system is operating can be generated.We also take a first step towards implementing this methodology in the field of rolling element bearings. Synthetic data are generated using a physical model that reproduces the dynamics of these machine elements. Condition indicators such as root mean square, kurtosis and shape factor, among others, are calculated from the vibrational response of a bearing and merged with the real features obtained from the data collected from the functioning systemFinally, the merged indicators are used to train SVM classifiers (support vector machines), so that a classification according to the condition of the bearing is made independently of the applied loading conditions even though some of the scenarios have not yet occurred.

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