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  • 1.
    Galar, Diego
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Thaduri, Adithya
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Simon, Victor
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    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 Tools2016In: Measurement, ISSN 1536-6367, E-ISSN 1536-6359Article in journal (Other (popular science, discussion, etc.))
    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.

  • 2.
    Johansson, Carl-Anders
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Simon, Victor
    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.
    Aggregation of electric current consumption features for extraction of maintenance KPIs2014In: 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, p. 157-162Conference paper (Refereed)
    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.

  • 3.
    Johansson, Carl-Anders
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Simon, Victor
    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.
    Context Driven Remaining Useful Life Estimation2014In: Procedia CIRP, ISSN 2212-8271, E-ISSN 2212-8271, Vol. 22, p. 181-185Article in journal (Refereed)
    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.

  • 4.
    Rodriguez, Emilio
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Simon, Victor
    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.
    Berges, Luis
    Department Design engineering and manufacturing, University of Zaragoza.
    Tamarit, Jaime
    CEDEX, Centro de estudios y experimentación de obras públicas.
    El impacto de la complejidad de la electrónica en la seguridad del sistema ferroviario2014In: Mantenimiento, ISSN 0214-4344, no 280, p. 17-23Article in journal (Refereed)
    Abstract [es]

    La complejidad del sistema ferroviario aumenta cuando se utiliza más electrónica. Cuando se introducen nuevos trenes equipados con componentes electrónicos importantes en una infraestructura ferroviaria que no ha sido renovada en los últimos años, como la sueca, puede surgir uno de los problemas más relevantes de ferrocarril: el sistema de parada de emergencia activa los frenos del material rodante, causando largos retrasos con efectos en cascada. La causa es que el TCC (centro de control de trenes) puede detectar señales inesperadas debido a los campos electromagnéticos transitorios que puedan interferir en los circuitos de señalización y control.

  • 5.
    Rodriguez, Emilio
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Simon, Victor
    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.
    Niska, Stefan
    Trafikverket.
    Safety Issues of Track Circuits: A Hybrid Approach2014In: Communications in Dependability and Quality Management, ISSN 1450-7196, Vol. 17, no 2, p. 15-26, article id 209636620Article in journal (Refereed)
    Abstract [en]

    The study of railway electromagnetic interference (EMI) seeks to determine the source of the interference or to ensure the correct operation of the equipment within adverse conditions. The complexity of railway system increases when more electronics are used. However a simple DC track circuit is still used in train detection systems in many countries, including Sweden, our case study. Most of the failures reported in the Swedish railway infrastructure are related to the detection system, making this research of interest to the railway community. By searching the Swedish failures report database, 0FELIA, for the most repetitive and probable causes of failures, they were identified three worst case scenarios: low resistance between the rails, external interference as a lightning and iron-powder-bridges in the insulated joint. They were simulated using the software CST STUDIO SUITE® (Computer Simulation Technology Studio Suite), supported by real measurements on site. Measurements followed the current EMC standards and were used to tune and validate the models, resulting in simulations very close to the real measures.

  • 6.
    Simon, Victor
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Baglee, David
    Institute for Automotive Manufacturing and Advanced Practices, University of Sunderland, Department of Computing, Engineering and Technology, Institute for Automotive and Manufacturing Advanced Practise, University of Sunderland, School of Computing and Technology, University of Sunderland.
    Garfield, Sheila
    University of Sunderland.
    Galar, Diego
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    The Development of an Advanced Maintenance training programme utilizing Augmented Reality2014Conference paper (Refereed)
    Abstract [en]

    Maintenance engineering represents an area of great opportunity to reduce cost, improve productivity, and increase profitability for manufacturing companies. There are examples of best practice that can be classed as World Class Maintenance which deliver great benefits. Unfortunately very few companies, and especially small and medium sized companies, remotely approach this level. Research has shown that savings of around 10% are achievable by improving asset management techniques through adopting modern maintenance practices, tools, and techniques. One area that is often overlooked is the development of an appropriate training programme in which the skills and knowledge are retained and used to develop the skills of young apprentices or new staff using specific technologies. Augmented Reality (AR) has been identified as a technology offering a promising approach to training which combines a number of disciplines including engineering, computing, and psychology. Augmented Reality (AR) enables users to view, through the use of see-through displays, virtual objects superimposed dynamically, and merged seamlessly, with real world objects in a real environment via a range of devices such as Ipad or Tablet, so that the virtual objects and real world images appear to exist at the same time in the same place providing real-time interaction. Therefore, this approach expands the surrounding real world environment by superimposing computer-generated information. It presents the information more intuitively than legacy interfaces such as paper-based instruction manuals enabling the users to interact directly with the information and use their natural spatial processing ability.This paper will identify augmented reality tools and techniques with the potential to support efficient training systems for maintenance and assembly skills that accelerate the technicians’ acquisition of new maintenance procedures. A platform for multimodal Augmented Reality based training will be proposed which could allow small to medium sized companies to develop and implement appropriate maintenance tasks based upon cost effective and efficient training systems. Such systems would give technicians’ the opportunity for practical training, that is, the possibility to “learn by doing” in the workplace; provide information when and where needed, thus reducing the technicians’ information search time; and potentially reduce errors due to violations in procedure, misinterpretation of facts, or insufficient training.A detailed bibliography on these topics is also provided.

  • 7.
    Simon, Victor
    et al.
    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.
    Prognostic Hybrid Model From Data Fusion on Machine Tools2015In: Proceedings of the XXI IMEKO World Congress: Prague, Czech Republic, 2015, Prague: IMEKO , 2015Conference paper (Refereed)
    Abstract [en]

    This paper proposes an enhancement of 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 aphysical model describing the degradation behaviour is available. Fusing the aforementioned techniques outputs a hybrid model for RUL estimation

  • 8.
    Villarejo, Roberto
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Johansson, Carl-anders
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Urko, Leturiondo
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics. IK4 Ikerlan, P J Ma Arizmendiarrieta 2, Arrasate Mondragon 20500, Spain.
    Simon, Victor
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Seneviratne, Dammika
    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.
    Bottom to Top Approach for Railway KPI Generation2017In: Management Systems in Production Engineering, ISSN 2299-0461, Vol. 25, no 3, p. 191-198, article id 28Article in journal (Refereed)
    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.

1 - 8 of 8
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  • ieee
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