Signal Pre-Selection for Monitoring and Prediction of Vehicle Powertrain Component Aging
Predictive maintenance has become important for avoiding unplanned downtime of modern vehicles. With increasing functionality the exchanged data between Electronic Control Units (ECU) grows simultaneously rapidly. A large number of in-vehicle signals are provided for monitoring an aging process. Var...
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| Format: | Article |
| Language: | Russian |
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Belarusian National Technical University
2019-12-01
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| Series: | Наука и техника |
| Subjects: | |
| Online Access: | https://sat.bntu.by/jour/article/view/2231 |
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| _version_ | 1846145160160739328 |
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| author | A. Udo Sass E. Esatbeyoglu T. Iwwerks |
| author_facet | A. Udo Sass E. Esatbeyoglu T. Iwwerks |
| author_sort | A. Udo Sass |
| collection | DOAJ |
| description | Predictive maintenance has become important for avoiding unplanned downtime of modern vehicles. With increasing functionality the exchanged data between Electronic Control Units (ECU) grows simultaneously rapidly. A large number of in-vehicle signals are provided for monitoring an aging process. Various components of a vehicle age due to their usage. This component aging is only visible in a certain number of in-vehicle signals. In this work, we present a signal selection method for in-vehicle signals in order to determine relevant signals to monitor and predict powertrain component aging of vehicles. Our application considers the aging of powertrain components with respect to clogging of structural components. We measure the component aging process in certain time intervals. Owing to this, unevenly spaced time series data is preprocessed to generate comparable in-vehicle data. First, we aggregate the data in certain intervals. Thus, the dynamic in-vehicle database is reduced which enables us to analyze the signals more efficiently. Secondly, we implement machine learning algorithms to generate a digital model of the measured aging process. With the help of Local Interpretable Model-Agnostic Explanations (LIME) the model gets interpretable. This allows us to extract the most relevant signals and to reduce the amount of processed data. Our results show that a certain number of in-vehicle signals are sufficient for predicting the aging process of the considered structural component. Consequently, our approach allows to reduce data transmission of in-vehicle signals with the goal of predictive maintenance. |
| format | Article |
| id | doaj-art-bfbefe78b22f4aca89484b076ec596a8 |
| institution | Kabale University |
| issn | 2227-1031 2414-0392 |
| language | Russian |
| publishDate | 2019-12-01 |
| publisher | Belarusian National Technical University |
| record_format | Article |
| series | Наука и техника |
| spelling | doaj-art-bfbefe78b22f4aca89484b076ec596a82024-12-02T06:20:03ZrusBelarusian National Technical UniversityНаука и техника2227-10312414-03922019-12-0118651952410.21122/2227-1031-2019-18-6-519-5242000Signal Pre-Selection for Monitoring and Prediction of Vehicle Powertrain Component AgingA. Udo Sass0E. Esatbeyoglu1T. Iwwerks2Volkswagen AGVolkswagen AGVolkswagen AGPredictive maintenance has become important for avoiding unplanned downtime of modern vehicles. With increasing functionality the exchanged data between Electronic Control Units (ECU) grows simultaneously rapidly. A large number of in-vehicle signals are provided for monitoring an aging process. Various components of a vehicle age due to their usage. This component aging is only visible in a certain number of in-vehicle signals. In this work, we present a signal selection method for in-vehicle signals in order to determine relevant signals to monitor and predict powertrain component aging of vehicles. Our application considers the aging of powertrain components with respect to clogging of structural components. We measure the component aging process in certain time intervals. Owing to this, unevenly spaced time series data is preprocessed to generate comparable in-vehicle data. First, we aggregate the data in certain intervals. Thus, the dynamic in-vehicle database is reduced which enables us to analyze the signals more efficiently. Secondly, we implement machine learning algorithms to generate a digital model of the measured aging process. With the help of Local Interpretable Model-Agnostic Explanations (LIME) the model gets interpretable. This allows us to extract the most relevant signals and to reduce the amount of processed data. Our results show that a certain number of in-vehicle signals are sufficient for predicting the aging process of the considered structural component. Consequently, our approach allows to reduce data transmission of in-vehicle signals with the goal of predictive maintenance.https://sat.bntu.by/jour/article/view/2231predictive maintenancefeature extractionsignal selectiontime seriesmachine learningmodel explanation |
| spellingShingle | A. Udo Sass E. Esatbeyoglu T. Iwwerks Signal Pre-Selection for Monitoring and Prediction of Vehicle Powertrain Component Aging Наука и техника predictive maintenance feature extraction signal selection time series machine learning model explanation |
| title | Signal Pre-Selection for Monitoring and Prediction of Vehicle Powertrain Component Aging |
| title_full | Signal Pre-Selection for Monitoring and Prediction of Vehicle Powertrain Component Aging |
| title_fullStr | Signal Pre-Selection for Monitoring and Prediction of Vehicle Powertrain Component Aging |
| title_full_unstemmed | Signal Pre-Selection for Monitoring and Prediction of Vehicle Powertrain Component Aging |
| title_short | Signal Pre-Selection for Monitoring and Prediction of Vehicle Powertrain Component Aging |
| title_sort | signal pre selection for monitoring and prediction of vehicle powertrain component aging |
| topic | predictive maintenance feature extraction signal selection time series machine learning model explanation |
| url | https://sat.bntu.by/jour/article/view/2231 |
| work_keys_str_mv | AT audosass signalpreselectionformonitoringandpredictionofvehiclepowertraincomponentaging AT eesatbeyoglu signalpreselectionformonitoringandpredictionofvehiclepowertraincomponentaging AT tiwwerks signalpreselectionformonitoringandpredictionofvehiclepowertraincomponentaging |