Condition-Based Maintenance for Normal Behaviour Characterisation of Railway Car-Body Acceleration Applying Neural Networks

Recently, passenger comfort and user experience are becoming increasingly relevant for the railway operators and, therefore, for railway manufacturers as well. The main reason for this to happen is that comfort is a clear differential value considered by passengers as final customers. Passengers’ co...

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Autores principales:	Pablo Garrido Martínez-Llop, Juan de Dios Sanz Bobi, Álvaro Solano Jiménez, Jorge Gutiérrez Sánchez
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	condition-based maintenance machine learning deep learning predictive maintenance comfort railway dynamics Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350
Acceso en línea:	https://doaj.org/article/ac27a29432f54902bca077c14de4a9c2
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spelling	oai:doaj.org-article:ac27a29432f54902bca077c14de4a9c22021-11-11T19:49:34ZCondition-Based Maintenance for Normal Behaviour Characterisation of Railway Car-Body Acceleration Applying Neural Networks10.3390/su1321122652071-1050https://doaj.org/article/ac27a29432f54902bca077c14de4a9c22021-11-01T00:00:00Zhttps://www.mdpi.com/2071-1050/13/21/12265https://doaj.org/toc/2071-1050Recently, passenger comfort and user experience are becoming increasingly relevant for the railway operators and, therefore, for railway manufacturers as well. The main reason for this to happen is that comfort is a clear differential value considered by passengers as final customers. Passengers’ comfort is directly related to the accelerations received through the car-body of the train. For this reason, suspension and damping components must be maintained in perfect condition, assuring high levels of comfort quality. An early detection of any potential failure in these systems derives in a better maintenance inspections’ planification and in a more sustainable approach to the whole train maintenance strategy. In this paper, an optimized model based on neural networks is trained in order to predict lateral car-body accelerations. Comparing these predictions to the values measured on the train, a normal characterisation of the lateral dynamic behaviour can be determined. Any deviation from this normal characterisation will imply a comfort loss or a potential degradation of the suspension and damping components. This model has been trained with a dataset from a specific train unit, containing variables recorded every second during the year 2017, including lateral and vertical car-body accelerations, among others. A minimum average error of 0.034 m/s<sup>2</sup> is obtained in the prediction of lateral car-body accelerations. This means that the average error is approximately 2.27% of the typical maximum estimated values for accelerations in vehicle body reflected in the EN14363 for the passenger coaches (1.5 m/s<sup>2</sup>). Thus, a successful model is achieved. In addition, the model is evaluated based on a real situation in which a passenger noticed a lack of comfort, achieving excellent results in the detection of atypical accelerations. Therefore, as it is possible to measure acceleration deviations from the standard behaviour causing lack of comfort in passengers, an alert can be sent to the operator or the maintainer for a non-programmed intervention at depot (predictive maintenance) or on board (prescriptive maintenance). As a result, a condition-based maintenance (CBM) methodology is proposed to avoid comfort degradation that could end in passenger complaints or speed limitation due to safety reasons for excessive acceleration. This methodology highlights a sustainable maintenance concept and an energy efficiency strategy.Pablo Garrido Martínez-LlopJuan de Dios Sanz BobiÁlvaro Solano JiménezJorge Gutiérrez SánchezMDPI AGarticlecondition-based maintenancemachine learningdeep learningpredictive maintenancecomfortrailway dynamicsEnvironmental effects of industries and plantsTD194-195Renewable energy sourcesTJ807-830Environmental sciencesGE1-350ENSustainability, Vol 13, Iss 12265, p 12265 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	condition-based maintenance machine learning deep learning predictive maintenance comfort railway dynamics Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350
spellingShingle	condition-based maintenance machine learning deep learning predictive maintenance comfort railway dynamics Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350 Pablo Garrido Martínez-Llop Juan de Dios Sanz Bobi Álvaro Solano Jiménez Jorge Gutiérrez Sánchez Condition-Based Maintenance for Normal Behaviour Characterisation of Railway Car-Body Acceleration Applying Neural Networks
description	Recently, passenger comfort and user experience are becoming increasingly relevant for the railway operators and, therefore, for railway manufacturers as well. The main reason for this to happen is that comfort is a clear differential value considered by passengers as final customers. Passengers’ comfort is directly related to the accelerations received through the car-body of the train. For this reason, suspension and damping components must be maintained in perfect condition, assuring high levels of comfort quality. An early detection of any potential failure in these systems derives in a better maintenance inspections’ planification and in a more sustainable approach to the whole train maintenance strategy. In this paper, an optimized model based on neural networks is trained in order to predict lateral car-body accelerations. Comparing these predictions to the values measured on the train, a normal characterisation of the lateral dynamic behaviour can be determined. Any deviation from this normal characterisation will imply a comfort loss or a potential degradation of the suspension and damping components. This model has been trained with a dataset from a specific train unit, containing variables recorded every second during the year 2017, including lateral and vertical car-body accelerations, among others. A minimum average error of 0.034 m/s<sup>2</sup> is obtained in the prediction of lateral car-body accelerations. This means that the average error is approximately 2.27% of the typical maximum estimated values for accelerations in vehicle body reflected in the EN14363 for the passenger coaches (1.5 m/s<sup>2</sup>). Thus, a successful model is achieved. In addition, the model is evaluated based on a real situation in which a passenger noticed a lack of comfort, achieving excellent results in the detection of atypical accelerations. Therefore, as it is possible to measure acceleration deviations from the standard behaviour causing lack of comfort in passengers, an alert can be sent to the operator or the maintainer for a non-programmed intervention at depot (predictive maintenance) or on board (prescriptive maintenance). As a result, a condition-based maintenance (CBM) methodology is proposed to avoid comfort degradation that could end in passenger complaints or speed limitation due to safety reasons for excessive acceleration. This methodology highlights a sustainable maintenance concept and an energy efficiency strategy.
format	article
author	Pablo Garrido Martínez-Llop Juan de Dios Sanz Bobi Álvaro Solano Jiménez Jorge Gutiérrez Sánchez
author_facet	Pablo Garrido Martínez-Llop Juan de Dios Sanz Bobi Álvaro Solano Jiménez Jorge Gutiérrez Sánchez
author_sort	Pablo Garrido Martínez-Llop
title	Condition-Based Maintenance for Normal Behaviour Characterisation of Railway Car-Body Acceleration Applying Neural Networks
title_short	Condition-Based Maintenance for Normal Behaviour Characterisation of Railway Car-Body Acceleration Applying Neural Networks
title_full	Condition-Based Maintenance for Normal Behaviour Characterisation of Railway Car-Body Acceleration Applying Neural Networks
title_fullStr	Condition-Based Maintenance for Normal Behaviour Characterisation of Railway Car-Body Acceleration Applying Neural Networks
title_full_unstemmed	Condition-Based Maintenance for Normal Behaviour Characterisation of Railway Car-Body Acceleration Applying Neural Networks
title_sort	condition-based maintenance for normal behaviour characterisation of railway car-body acceleration applying neural networks
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/ac27a29432f54902bca077c14de4a9c2
work_keys_str_mv	AT pablogarridomartinezllop conditionbasedmaintenancefornormalbehaviourcharacterisationofrailwaycarbodyaccelerationapplyingneuralnetworks AT juandediossanzbobi conditionbasedmaintenancefornormalbehaviourcharacterisationofrailwaycarbodyaccelerationapplyingneuralnetworks AT alvarosolanojimenez conditionbasedmaintenancefornormalbehaviourcharacterisationofrailwaycarbodyaccelerationapplyingneuralnetworks AT jorgegutierrezsanchez conditionbasedmaintenancefornormalbehaviourcharacterisationofrailwaycarbodyaccelerationapplyingneuralnetworks
_version_	1718431427591143424

Condition-Based Maintenance for Normal Behaviour Characterisation of Railway Car-Body Acceleration Applying Neural Networks

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