Fatigue monitoring and maneuver identification for vehicle fleets using a virtual sensing approach
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2023
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| _version_ | 1866912204026019840 |
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| author | Heindel, Leonhard Hantschke, Peter Kästner, Markus |
| author_facet | Heindel, Leonhard Hantschke, Peter Kästner, Markus |
| contents | Extensive monitoring comes at a prohibitive cost, limiting Predictive Maintenance strategies for vehicle fleets. This paper presents a measurement-based virtual sensing technique where local strain gauges are only required for few reference vehicles, while the remaining fleet relies exclusively on accelerometers. The scattering transform is used to perform feature extraction, while principal component analysis provides a reduced, low dimensional data representation. This enables direct fatigue damage regression, parameterized from unlabeled usage data. Identification measurements allow for a physical interpretation of the reduced representation. The approach is demonstrated using experimental data from a sensor equipped eBike, which is made publicly available. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2302_02737 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Fatigue monitoring and maneuver identification for vehicle fleets using a virtual sensing approach Heindel, Leonhard Hantschke, Peter Kästner, Markus Signal Processing 74H45 Extensive monitoring comes at a prohibitive cost, limiting Predictive Maintenance strategies for vehicle fleets. This paper presents a measurement-based virtual sensing technique where local strain gauges are only required for few reference vehicles, while the remaining fleet relies exclusively on accelerometers. The scattering transform is used to perform feature extraction, while principal component analysis provides a reduced, low dimensional data representation. This enables direct fatigue damage regression, parameterized from unlabeled usage data. Identification measurements allow for a physical interpretation of the reduced representation. The approach is demonstrated using experimental data from a sensor equipped eBike, which is made publicly available. |
| title | Fatigue monitoring and maneuver identification for vehicle fleets using a virtual sensing approach |
| topic | Signal Processing 74H45 |
| url | https://arxiv.org/abs/2302.02737 |