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  • 1.
    Anandika, Rayendra
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Stenström, Christer
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Lundberg, Jan
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Non-destructive measurement of artificial near-surface cracks for railhead inspection2019In: Insight (Northampton), ISSN 1354-2575, E-ISSN 1754-4904, Vol. 61, no 7, p. 373-379Article in journal (Refereed)
    Abstract [en]

    This paper delivers a study involving the inspection of artificial surface cracks with depths ranging from 0.25-2.5 mm from the surface and with a crack angle of 30°, which is a typical angle for surface cracks in railheads. The inspections were conducted using three different techniques: phased array ultrasonics, single-element ultrasonics and alternating current potential drop (ACPD). For the ultrasonic techniques, the study focused on employing either longitudinal or shear wave signals. In the railway industry, shallow surface cracks in railheads are caused by rolling contact fatigue (RCF). In this study, artificial defects were made, allowing the authors to explore the extent to which the ultrasonic measurement techniques can detect such defects. The negative effect of a dead zone near to the surface in the ultrasonic tests was reduced by using a wedge attachment. A discussion on the extent to which the techniques can be used in field tests was also provided. The most important result is that shallow cracks ranging from 0.25-2.5 mm were successfully characterised with acceptable accuracy. The 2.5 mm-deep crack can be measured with an accuracy of 0.8% using a 20 MHz single-element probe and with an accuracy of 3.5% using a 5 MHz phased array (64 elements, 0.6 mm pitch). The characterisations were performed using a filtering method that was developed in this study.

    1675605

  • 2.
    Bäckström, Mikael
    et al.
    Luleå tekniska universitet.
    Wiklund, Håkan
    Development of a multi-tooth approach to tool condition monitoring in milling1998In: Insight (Northampton), ISSN 1354-2575, E-ISSN 1754-4904, Vol. 40, no 8, p. 548-552Article in journal (Refereed)
    Abstract [en]

    The need to develop reliable, adequate and cost-efficient methods for tool condition monitoring in milling has been emphasized by industry for a long time. The development of such methods has to deal with many difficulties such as complex machining conditions and a large number of available process variables. The paper presents the development of a multi-tooth approach to tool condition monitoring applied to milling where the inherent methods have been evaluated in experimental studies. Non-traditional methods such as multivariate techniques have been used to handle the large amount of process information that become accessible during machining. The developed methods are applied on different aspects of tool condition monitoring where the results obtained create opportunities for future research.

  • 3.
    Galar, Diego
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Palo, Mikael
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Horenbeek, Adriaan Van
    Centre for Industrial management, KU Leuven.
    Printelon, Liliane M.
    Centre for Industrial management, KU Leuven.
    Integration of disparate data sources to perform maintenance prognosis and optimal decision making2012In: Insight (Northampton), ISSN 1354-2575, E-ISSN 1754-4904, Vol. 54, no 8, p. 440-445Article in journal (Refereed)
    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 systematised. 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 (for example reduce costs and maximise 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 organisations to streamline business processes. It is necessary to integrate management data from computer maintenance management systems (CMMS) with condition monitoring (CM) systems and finally supervisory control and data acquisition (SCADA) and other control systems, widely used in production but seldom with a 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.

  • 4.
    Galar, Diego
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Wandt, Karina
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Karim, Ramin
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Berges, Luis
    Department of Design Engineering and Manufacturing, University of Zaragoza.
    The evolution from e(lectronic)Maintenance to i(ntelligent)Maintenance2012In: Insight (Northampton), ISSN 1354-2575, E-ISSN 1754-4904, Vol. 54, no 8, p. 446-450Article in journal (Refereed)
    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.

  • 5.
    Gerdes, Mike
    et al.
    Department of Automotive and Aeronautical Engineering, HAW Hamburg.
    Galar, Diego
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    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 conditioning2017In: Insight (Northampton), ISSN 1354-2575, E-ISSN 1754-4904, Vol. 59, no 8, p. 424-433Article in journal (Refereed)
    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.

  • 6.
    Leturiondo, Urko
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics. IK4-Ikerlan.
    Mishra, Madhav
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Galar, Diego
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Salgado, Oscar
    IK4-Ikerlan.
    Synthetic data generation in hybrid modelling of rolling element bearings2015In: Insight (Northampton), ISSN 1354-2575, E-ISSN 1754-4904, Vol. 57, no 7, p. 395-400Article in journal (Refereed)
    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.

  • 7.
    Mishra, Madhav
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Odelius, Johan
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Rantatalo, Matti
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Johnsson, Roger
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Larsson, Jan-Olof
    SKF Sweden.
    Bellander, Magnus
    SKF Sweden.
    Niemi, Ingemar
    Billerud Karlsborg AB.
    Simulations and measurements of the dynamic response of a paper machine roller2016In: Insight (Northampton), ISSN 1354-2575, E-ISSN 1754-4904, Vol. 58, no 4, p. 210-212Article in journal (Refereed)
    Abstract [en]

    The paper industry is a highly automated industry that includes many different production steps, in which a variety of machine components are used. In a paper machine, where the pulp is being transformed into paper, rotating components such as bearing-mounted rollers play an important part in driving the wire with the pulp through the process. In this type of industry with a serial layout, the failure of a single roller or bearing could lead to the stoppage of several production steps, with costly consequences as a result. To ensure and optimise asset availability, a condition-based maintenance (CBM) strategy could be implemented. However, CBM is dependent on an appropriate condition monitoring (CM) technique to detect a physical phenomenon that defines the state of critical components or systems. For the development of CM techniques, it is therefore important to understand and model the physical behaviour of the system in question. In this paper, the behaviour of a roller in a paper machine is analysed using the finite element method (FEM). The physical model was compared with vibration measurements collected from an online monitoring system and an experimental modal analysis.

  • 8.
    Mohammed, Omar D.
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Rantatalo, Matti
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Gear tooth crack detection using dynamic response analysis2013In: Insight (Northampton), ISSN 1354-2575, E-ISSN 1754-4904, Vol. 55, no 8, p. 417-421Article in journal (Refereed)
    Abstract [en]

    Efficient non-destructive fault detection and severity assessment can be performed using vibration analysis. This paper studies gear tooth crack detection through investigating the natural frequencies of the studied gear model. The gear mesh stiffness was obtainedanalytically, and dynamic simulation was performed. Moreover, the frequency response functions (FRFs) were calculated for healthy and faulty gears. A change in the eigenfrequencies could be observed with increasing crack size, and thus the dynamic response could give an indication of gear tooth cracks.

  • 9.
    Palo, Mikael
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Schunnesson, Håkan
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Mining and Geotechnical Engineering.
    Kumar, Uday
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Larsson-Kråik, Per-Olof
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Galar, Diego
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Rolling stock condition monitoring using wheel/rail forces2012In: Insight (Northampton), ISSN 1354-2575, E-ISSN 1754-4904, Vol. 54, no 8, p. 451-455Article in journal (Refereed)
    Abstract [en]

    Railway vehicles are efficient because of the low resistance in the contact zone between wheel and rail. In order to remain efficient, train operators and infrastructure owners need to keep rails, wheels and vehicles in an acceptable condition. Wheel wear affects the dynamic characteristics of vehicles and the dynamic force impact on the rail. The shape of the wheel profile affects the performance of railway vehicles in different ways. Wheel condition has historically been managed by identifying and removing wheels from service when they exceed an impact threshold. Condition monitoring of railway vehicles is mainly performed using wheel impact load detectors and truck performance detectors. These systems use either forces or stress on the rail to interpret the condition. This paper will show measurements taken at the research station outside Luleå in northern Sweden. The station measures the wheel/rail forces, both lateral and vertical, at the point of contact in a curve with a 484 m radius at speeds of up to 100 km/h. Data are analysed to show differences for various wheel positions and to determine the robustness of the system.

  • 10.
    Saari, Juhamatti
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Lundberg, Jan
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Odelius, Johan
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Rantatalo, Matti
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Selection of features for fault diagnosis on rotating machines using random forest and wavelet analysis2018In: Insight (Northampton), ISSN 1354-2575, E-ISSN 1754-4904, Vol. 60, no 8, p. 434-442Article in journal (Refereed)
    Abstract [en]

    Identification of component faults using automated condition monitoring methods has a huge potential to improve the prediction of machine failures. The ongoing development of the Internet of Things (IoT) will support and benefit feature selection and improve preventative maintenance decision making. However, there may be problems with the selection of features that best describe a specific fault and remain valid even when the operation mode is changing (for example different levels of load). In this study, features were extracted from vibration signals using wavelet analysis; a feature subset was selected using the random forest ensemble technique. Three different datasets were created where the load of the system was changing while the rotational speed remained the same. The tests were repeated five times by first recording the nominal condition and then introducing four faults: angular misalignment; offset misalignment; partially broken gear tooth failure; and macro-pitting of the gear. To improve previous feature selection techniques, a method is proposed where, before training a classifier, the most promising features are compared at different degrees of torsional load. The results indicate that the proposed method of using random forests to select top variables can help to choose good features that may not have been considered in manual feature selection or in individual load zones.

  • 11.
    Tormos, B.
    et al.
    University of Politecn Valencia, CMT Motores Term.
    Olmeda, P.
    University of Politecn Valencia, CMT Motores Term.
    Gomez, Y.
    University of Politecn Valencia, CMT Motores Term.
    Galar, Diego
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Monitoring and analysing oil condition to generate maintenance savings: a case study in a CNG engine powered urban transport fleet2013In: Insight (Northampton), ISSN 1354-2575, E-ISSN 1754-4904, Vol. 55, no 2, p. 84-87Article in journal (Refereed)
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