Predictive Maintenance of Lead-Acid Batteries with Sparse Vehicle Operational Data
Predictive maintenance aims to predict failures in components of a system, a heavy-duty vehicle in this work, and do maintenance before any actual fault occurs. Predictive maintenance is increasingly important in the automotive industry due to the development of new services and autonomous vehicles...
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| Format: | Article |
| Language: | English |
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The Prognostics and Health Management Society
2020-01-01
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| Series: | International Journal of Prognostics and Health Management |
| Subjects: | |
| Online Access: | https://papers.phmsociety.org/index.php/ijphm/article/view/2608 |
| _version_ | 1829492879302590464 |
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| author | Sergii Voronov Mattias Krysander Erik Frisk |
| author_facet | Sergii Voronov Mattias Krysander Erik Frisk |
| author_sort | Sergii Voronov |
| collection | DOAJ |
| description | Predictive maintenance aims to predict failures in components of a system, a heavy-duty vehicle in this work, and do maintenance before any actual fault occurs. Predictive maintenance is increasingly important in the automotive industry due to the development of new services and autonomous vehicles with no driver who can notice first signs of a component problem. The lead-acid battery in a heavy vehicle is mostly used during engine starts, but also for heating and cooling the cockpit, and is an important part of the electrical system that is essential for reliable operation. This paper develops and evaluates two machine-learning based methods for battery prognostics, one based on Long Short-Term Memory (LSTM) neural networks and one on Random Survival Forest (RSF). The objective is to estimate time of battery failure based on sparse and non-equidistant vehicle operational data, obtained from workshop visits or over-the-air readouts. The dataset has three characteristics: 1) no sensor measurements are directly related to battery health, 2) the number of data readouts vary from one vehicle to another, and 3) readouts are collected at different time periods. Missing data is common and is addressed by comparing different imputation techniques. RSF- and LSTM-based models are proposed and evaluated for the case of sparse multiple readouts. How to measure model performance is discussed and how the amount of vehicle information influences performance. |
| first_indexed | 2024-12-16T06:23:17Z |
| format | Article |
| id | doaj.art-79b09193edb34141aa4185545fa3c12d |
| institution | Directory Open Access Journal |
| issn | 2153-2648 2153-2648 |
| language | English |
| last_indexed | 2024-12-16T06:23:17Z |
| publishDate | 2020-01-01 |
| publisher | The Prognostics and Health Management Society |
| record_format | Article |
| series | International Journal of Prognostics and Health Management |
| spelling | doaj.art-79b09193edb34141aa4185545fa3c12d2022-12-21T22:41:04ZengThe Prognostics and Health Management SocietyInternational Journal of Prognostics and Health Management2153-26482153-26482020-01-01111doi:10.36001/ijphm.2020.v11i1.2608Predictive Maintenance of Lead-Acid Batteries with Sparse Vehicle Operational DataSergii Voronov0Mattias Krysander1Erik Frisk2Department of Electrical Engineering, Linköping University, Linköping, S-581 83, SwedenDepartment of Electrical Engineering, Linköping University, Linköping, S-581 83, SwedenDepartment of Electrical Engineering, Linköping University, Linköping, S-581 83, SwedenPredictive maintenance aims to predict failures in components of a system, a heavy-duty vehicle in this work, and do maintenance before any actual fault occurs. Predictive maintenance is increasingly important in the automotive industry due to the development of new services and autonomous vehicles with no driver who can notice first signs of a component problem. The lead-acid battery in a heavy vehicle is mostly used during engine starts, but also for heating and cooling the cockpit, and is an important part of the electrical system that is essential for reliable operation. This paper develops and evaluates two machine-learning based methods for battery prognostics, one based on Long Short-Term Memory (LSTM) neural networks and one on Random Survival Forest (RSF). The objective is to estimate time of battery failure based on sparse and non-equidistant vehicle operational data, obtained from workshop visits or over-the-air readouts. The dataset has three characteristics: 1) no sensor measurements are directly related to battery health, 2) the number of data readouts vary from one vehicle to another, and 3) readouts are collected at different time periods. Missing data is common and is addressed by comparing different imputation techniques. RSF- and LSTM-based models are proposed and evaluated for the case of sparse multiple readouts. How to measure model performance is discussed and how the amount of vehicle information influences performance.https://papers.phmsociety.org/index.php/ijphm/article/view/2608data-drivenrandom survival forestslong short term memorybattery lifetime prognostics |
| spellingShingle | Sergii Voronov Mattias Krysander Erik Frisk Predictive Maintenance of Lead-Acid Batteries with Sparse Vehicle Operational Data International Journal of Prognostics and Health Management data-driven random survival forests long short term memory battery lifetime prognostics |
| title | Predictive Maintenance of Lead-Acid Batteries with Sparse Vehicle Operational Data |
| title_full | Predictive Maintenance of Lead-Acid Batteries with Sparse Vehicle Operational Data |
| title_fullStr | Predictive Maintenance of Lead-Acid Batteries with Sparse Vehicle Operational Data |
| title_full_unstemmed | Predictive Maintenance of Lead-Acid Batteries with Sparse Vehicle Operational Data |
| title_short | Predictive Maintenance of Lead-Acid Batteries with Sparse Vehicle Operational Data |
| title_sort | predictive maintenance of lead acid batteries with sparse vehicle operational data |
| topic | data-driven random survival forests long short term memory battery lifetime prognostics |
| url | https://papers.phmsociety.org/index.php/ijphm/article/view/2608 |
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