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Raposo, H., Torres Farinha, J., Fonseca, I. & Galar, D. (2019). Predicting condition based on oil analysis: A case study. Tribology International, 135, 65-74
Open this publication in new window or tab >>Predicting condition based on oil analysis: A case study
2019 (English)In: Tribology International, ISSN 0301-679X, E-ISSN 1879-2464, Vol. 135, p. 65-74Article in journal (Refereed) Published
Abstract [en]

The paper presents and discusses a model for condition monitoring. Using data from the oil in the Diesel engines of a fleet of urban buses, it studies the evolution of degradation and develops a predictive maintenance policy for oil replacement. Based on the analysis of the oil condition, the intervals of oil replacement can be expanded, allowing increased availability. The paper links time series forecasting with the statistical behavior of some oil effluents, like soot. This exercise can be expanded to include other variables, and the model has the potential to be applied to other physical assets to achieve the best availability based on a condition monitoring policy.

Place, publisher, year, edition, pages
Elsevier, 2019
Keywords
Condition monitoring, Predictive maintenance, Oil analysis, Time series, t-Student, Diesel engines
National Category
Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-73083 (URN)10.1016/j.triboint.2019.01.041 (DOI)2-s2.0-85062372547 (Scopus ID)
Note

Validerad;2019;Nivå 2;2019-03-13 (johcin)

Available from: 2019-03-01 Created: 2019-03-01 Last updated: 2019-03-13Bibliographically approved
González-González, A., Jimenez Cortadi, A., Galar, D. & Ciani, L. (2018). Condition Monitoring of Wind Turbine Pitch Controller: A Maintenance Approach. Measurement, 123, 80-93
Open this publication in new window or tab >>Condition Monitoring of Wind Turbine Pitch Controller: A Maintenance Approach
2018 (English)In: Measurement, ISSN 0263-2241, E-ISSN 1873-412X, Vol. 123, p. 80-93Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
Elsevier, 2018
National Category
Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-68106 (URN)10.1016/j.measurement.2018.01.047 (DOI)000432748600011 ()2-s2.0-85044546969 (Scopus ID)
Note

Validerad;2018;Nivå 2;2018-04-03 (andbra)

Available from: 2018-03-29 Created: 2018-03-29 Last updated: 2018-06-07Bibliographically approved
Diez-Olivan, A., Del Ser, J., Galar, D. & Sierra, B. (2018). Data Fusion and Machine Learning for Industrial Prognosis: Trends and Perspectives towards Industry 4.0. Information Fusion, 50, 92-111
Open this publication in new window or tab >>Data Fusion and Machine Learning for Industrial Prognosis: Trends and Perspectives towards Industry 4.0
2018 (English)In: Information Fusion, ISSN 1566-2535, E-ISSN 1872-6305, Vol. 50, p. 92-111Article in journal (Refereed) Published
Abstract [en]

The so-called “smartization” of manufacturing industries has been conceived as the fourth industrial revolution or Industry 4.0, a paradigm shift propelled by the upsurge and progressive maturity of new Information and Communication Technologies (ICT) applied to industrial processes and products. From a data science perspective, this paradigm shift allows extracting relevant knowledge from monitored assets through the adoption of intelligent monitoring and data fusion strategies, as well as by the application of machine learning and optimization methods. One of the main goals of data science in this context is to effectively predict abnormal behaviors in industrial machinery, tools and processes so as to anticipate critical events and damage, eventually causing important economical losses and safety issues. In this context, data-driven prognosis is gradually gaining attention in different industrial sectors. This paper provides a comprehensive survey of the recent developments in data fusion and machine learning for industrial prognosis, placing an emphasis on the identification of research trends, niches of opportunity and unexplored challenges. To this end, a principled categorization of the utilized feature extraction techniques and machine learning methods will be provided on the basis of its intended purpose: analyze what caused the failure (descriptive), determine when the monitored asset will fail (predictive) or decide what to do so as to minimize its impact on the industry at hand (prescriptive). This threefold analysis, along with a discussion on its hardware and software implications, intends to serve as a stepping stone for future researchers and practitioners to join the community investigating on this vibrant field.

Place, publisher, year, edition, pages
Elsevier, 2018
Keywords
Data-driven prognosis, Data Fusion, Machine learning, Industry 4.0
National Category
Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-71259 (URN)10.1016/j.inffus.2018.10.005 (DOI)2-s2.0-85055203735 (Scopus ID)
Note

Validerad;2018;Nivå 2;2018-10-31 (svasva)

Available from: 2018-10-18 Created: 2018-10-18 Last updated: 2018-10-31Bibliographically approved
D'Emilia, G., Gaspari, A. & Galar, D. (2018). Improvement of measurement contribution for asset characterization in complex engineering systems by an iterative methodology. International Journal of Service Science, Management, Engineering, and Technology, 9(2), 85-103, Article ID 4.
Open this publication in new window or tab >>Improvement of measurement contribution for asset characterization in complex engineering systems by an iterative methodology
2018 (English)In: International Journal of Service Science, Management, Engineering, and Technology, ISSN 1947-959X, Vol. 9, no 2, p. 85-103, article id 4Article in journal (Refereed) Published
Abstract [en]

The evolution of systems based on the integration of Internet of Things (IoT) and Cloud computing technologies requires resolute and trustable management approaches, to let the industrial assets thrive and avoid losses in efficiency and, thus, profitability. In this article, a methodology based on the evaluation of the measurement uncertainty is proposed, which is able to suggest possible improvement paths and reliable decisions. The approach is based on the identification of subsequent tasks that should be fulfilled, also in a recursive way. Its application in the field, for the identification of the vibration and acoustic emission signatures of highly-performance machining tools, allows directing future actions to increase the potentiality of proper management of the information provided by measurements. In a complex scenario, characterized by many devices and instruments, the compliance with the procedures for measurement accuracy has proven to be a useful support.

Place, publisher, year, edition, pages
IGI Global, 2018
Keywords
Centerless Grinding, Condition Monitoring, Internet of Things (IoT), Measurement Uncertainty
National Category
Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-72818 (URN)10.4018/IJSSMET.2018040104 (DOI)2-s2.0-85060732461 (Scopus ID)
Available from: 2019-02-07 Created: 2019-02-07 Last updated: 2019-02-07Bibliographically approved
Kumar, U. & Galar, D. (2018). Maintenance in the Era of Industry 4.0: Issues and Challenges. In: Kapur P., Kumar U., Verma A. (Ed.), Quality, IT and Business Operations: Modeling and Optimization (pp. 231-250). Singapore: Springer
Open this publication in new window or tab >>Maintenance in the Era of Industry 4.0: Issues and Challenges
2018 (English)In: Quality, IT and Business Operations: Modeling and Optimization / [ed] Kapur P., Kumar U., Verma A., Singapore: Springer, 2018, p. 231-250Chapter in book (Refereed)
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.

Place, publisher, year, edition, pages
Singapore: Springer, 2018
Series
Springer Proceedings in Business and Economics, ISSN 2198-7246
National Category
Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-65958 (URN)10.1007/978-981-10-5577-5_19 (DOI)978-981-10-5576-8 (ISBN)978-981-10-5577-5 (ISBN)
Available from: 2017-10-04 Created: 2017-10-04 Last updated: 2017-11-24Bibliographically approved
Raposo, H., Farinha, J. T., Ferreira, L. A. & Galar, D. (2017). An integrated econometric model for bus replacement and determination of reserve fleet size based on predictive maintenance. Eksploatacja i Niezawodnosc, 19(3), 358-368
Open this publication in new window or tab >>An integrated econometric model for bus replacement and determination of reserve fleet size based on predictive maintenance
2017 (English)In: Eksploatacja i Niezawodnosc, ISSN 1507-2711, Vol. 19, no 3, p. 358-368Article in journal (Refereed) Published
Abstract [en]

Maintenance policies influence equipment availability and, thus, they affect a company’s capacity for productivity and competitiveness. It is important to optimize the Life Cycle Cost (LCC) of assets, in this case, passenger bus fleets. The paper presents a predictive condition monitoring maintenance approach based on engine oil analysis, to assess the potential impact of this variable on the availability of buses. The approach has implications on maintenance costs during the life of a bus and, consequently, on the determination of the best time for bus replacement. The paper provides an overview of economic replacement models through a global model, with an emphasis on availability and its dependence on maintenance and maintenance costs. These factors help to determine the size of the reserve fleet and guarantee availability

Abstract [pl]

Polityka konserwacji wpływa na gotowość sprzętu, a tym samym na wydajność i konkurencyjność przedsiębiorstwa. Ważne jestoptymalizowanie kosztów cyklu życia (LCC) aktywów, w tym przypadku taboru autobusowego. W artykule przedstawiono metodęutrzymania ruchu polegającą na predykcyjnym monitorowaniu stanu w oparciu o analizę oleju silnikowego w celu oceny potencjalnegowpływu tej zmiennej na gotowość autobusów. Podejście to ma praktyczne konsekwencje jeśli chodzi o koszty utrzymaniaw trakcie eksploatacji autobusu, a także pozwala na ustalenie najlepszego czasu na wymianę pojazdów taboru. W pracy przedstawionoprzegląd ekonomicznych modeli wymiany oraz opracowano model globalny integrujący te modele, ze szczególnymuwzględnieniem gotowości oraz jej zależności od konserwacji oraz kosztów utrzymania ruchu. Czynniki te pomagają określićwielkość floty rezerwowej i zapewnić gotowość taboru.

Place, publisher, year, edition, pages
Polish Academy of Sciences Branch, 2017
National Category
Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-64691 (URN)10.17531/ein.2017.3.6 (DOI)000403344800006 ()2-s2.0-85020500047 (Scopus ID)
Note

Validerad;2017;Nivå 2;2017-06-30 (andbra)

Available from: 2017-06-30 Created: 2017-06-30 Last updated: 2018-07-10Bibliographically approved
Leturiondo, U., Salgado, O., Ciani, L., Galar, D. & Catelani, M. (2017). Architecture for hybrid modelling and its application to diagnosis and prognosis with missing data. Measurement, 108, 152-162
Open this publication in new window or tab >>Architecture for hybrid modelling and its application to diagnosis and prognosis with missing data
Show others...
2017 (English)In: Measurement, ISSN 0263-2241, E-ISSN 1873-412X, Vol. 108, p. 152-162Article in journal (Refereed) Published
Abstract [en]

The advances in technology involving internet of things, cloud computing and big data mean a new perspective in the calculation of reliability, maintainability, availability and safety by combining physics-based modelling with data-driven modelling. This paper proposes an architecture to implement hybrid modelling based on the fusion of real data and synthetic data obtained in simulations using a physics-based model. This architecture has two levels of analysis: an online process carried out locally and virtual commissioning performed in the cloud. The former results in failure detection analysis to avoid upcoming failures whereas the latter leads to both diagnosis and prognosis. The proposed hybrid modelling architecture is validated in the field of rotating machinery using time-domain and frequency-domain analysis. A multi-body model and a semi-supervised learning algorithm are used to perform the hybrid modelling. The state of a rolling element bearing is analysed and accurate results for fault detection, localisation and quantification are obtained. The contextual information increases the accuracy of the results; the results obtained by the model can help improve maintenance decision making and production scheduling. Future work includes a prescriptive analysis approach.

Place, publisher, year, edition, pages
Elsevier, 2017
National Category
Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-61838 (URN)10.1016/j.measurement.2017.02.003 (DOI)000404201400016 ()2-s2.0-85012901785 (Scopus ID)
Note

Validerad;2017;Nivå 2;2017-06-07 (rokbeg)

Available from: 2017-02-06 Created: 2017-02-06 Last updated: 2018-07-10Bibliographically approved
Villarejo, R., Johansson, C.-a., Urko, L., Simon, V., Seneviratne, D. & Galar, D. (2017). Bottom to Top Approach for Railway KPI Generation. Management Systems in Production Engineering, 25(3), 191-198, Article ID 28.
Open this publication in new window or tab >>Bottom to Top Approach for Railway KPI Generation
Show others...
2017 (English)In: Management Systems in Production Engineering, ISSN 2299-0461, Vol. 25, no 3, p. 191-198, article id 28Article in journal (Refereed) Published
Abstract [en]

Railway maintenance especially on infrastructure produces a vast amount of data. However, having data is not synonymous with having information; rather, data must be processed to extract information. In railway maintenance, the development of key performance indicators (KPIs) linked to punctuality or capacity can help planned and scheduled maintenance, thus aligning the maintenance department with corporate objectives. There is a need for an improved method to analyse railway data to find the relevant KPIs. The system should support maintainers, answering such questions as what maintenance should be done, where and when. The system should equip the user with the knowledge of the infrastructure's condition and configuration, and the traffic situation so maintenance resources can be targeted to only those areas needing work. The amount of information is vast, so it must be hierarchized and aggregated; users must filter out the useless indicators. Data are fused by compiling several individual indicators into a single index; the resulting composite indicators measure multidimensional concepts which cannot be captured by a single index. The paper describes a method of monitoring a complex entity. In this scenario, a plurality of use indices and weighting values are used to create a composite and aggregated use index from a combination of lower level use indices and weighting values. The resulting composite and aggregated indicators can be a decision-making tool for asset managers at different hierarchical levels.

Place, publisher, year, edition, pages
WARSAW, POLAND: De Gruyter Open, 2017
Keywords
railway assets, fusion, hierarchy, aggregation, KPI, performance, condition monitoring, CMMS
National Category
Other Engineering and Technologies Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-65609 (URN)10.1515/mspe-2017-0028 (DOI)000408931800008 ()
Note

Validerad;2017;Nivå 2;2017-10-03 (rokbeg)

Available from: 2017-09-12 Created: 2017-09-12 Last updated: 2017-11-24Bibliographically approved
Schmidt, B., Gandhi, K., Wang, L. & Galar, D. (2017). Context preparation for predictive analytics: a case from manufacturing industry. Journal of Quality in Maintenance Engineering, 23(3), 341-354
Open this publication in new window or tab >>Context preparation for predictive analytics: a case from manufacturing industry
2017 (English)In: Journal of Quality in Maintenance Engineering, ISSN 1355-2511, E-ISSN 1758-7832, Vol. 23, no 3, p. 341-354Article in journal (Refereed) Published
Abstract [en]

Purpose

The purpose of this paper is to exemplify and discuss the context aspect for predictive analytics where in parallel condition monitoring measurements data and information related to the context are gathered and analysed.

Design/methodology/approach

This paper is based on an industrial case study, conducted in a manufacturing company. The linear axis of a machine tool has been selected as an object of interest. Available data from different sources have been gathered and a new condition monitoring function has been implemented. Details about performed steps of data acquisition and selection are provided. Among the obtained data, health indicators and context related information have been identified.

Findings

Multiple sources of relevant contextual information has been identified. Performed analysis discovered the deviations in operational conditions when the same machining operation is repeatedly performed.

Originality/value

This paper shows the outcomes from a case study in real word industrial setup. A new visualisation method of gathered data is proposed to support decision-making process

Place, publisher, year, edition, pages
Emerald Group Publishing Limited, 2017
National Category
Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-65119 (URN)10.1108/JQME-10-2016-0050 (DOI)2-s2.0-85027991065 (Scopus ID)
Note

Validerad;2017;Nivå 2;2017-08-28 (andbra)

Available from: 2017-08-16 Created: 2017-08-16 Last updated: 2017-11-24Bibliographically approved
Gerdes, M., Galar, D. & Scholz, D. (2017). Decision trees and the effects of feature extraction parameters for robust sensor network design. Eksploatacja i Niezawodnosc - Maintenance and Reliability, 19(1), 31-42
Open this publication in new window or tab >>Decision trees and the effects of feature extraction parameters for robust sensor network design
2017 (English)In: Eksploatacja i Niezawodnosc - Maintenance and Reliability, ISSN 1507-2711, Vol. 19, no 1, p. 31-42Article in journal (Refereed) Published
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

Place, publisher, year, edition, pages
Polish Maintenance Society, 2017
National Category
Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-61340 (URN)10.17531/ein.2017.1.5 (DOI)000392367100005 ()2-s2.0-85006786154 (Scopus ID)
Note

Validerad; 2017; Nivå 2; 2017-01-09 (andbra); Polsk titel: Wykorzystanie drzew decyzyjnych oraz wpływu parametrów ekstrakcji cech do projektowania odpornych sieci czujników

Available from: 2017-01-09 Created: 2017-01-09 Last updated: 2018-11-19Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-4107-0991

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