Neural Fault Observer-Based On-Board Prognostics for Automotive Electric Power Steering Systems
Electric power steering (EPS) systems form a fundamental unit in modern automotive vehicles, providing motor assistance to aid the driver’s manual maneuvering. Preventing loss of assist (LoA) in advance is critical for EPS systems to mitigate fatal accidents and reduce maintenance costs....
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10908410/ |
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| author | Gyuwon Kim Jongick Won Sangjin Ko Jinhwan Lee |
| author_facet | Gyuwon Kim Jongick Won Sangjin Ko Jinhwan Lee |
| author_sort | Gyuwon Kim |
| collection | DOAJ |
| description | Electric power steering (EPS) systems form a fundamental unit in modern automotive vehicles, providing motor assistance to aid the driver’s manual maneuvering. Preventing loss of assist (LoA) in advance is critical for EPS systems to mitigate fatal accidents and reduce maintenance costs. As part of the recent interest in intelligent software-defined vehicles (SDV), data-driven approaches have gained much attention to overcome the limitations of conventional fail-safe mechanisms and provide value-added maintenance strategies. While related works in the field have shown promising results, they are limited to proof-of-concept studies validated under simulation and test-bed environments. Here, we present a novel deep learning (DL)-based method to detect EPS performance degradation using experimental data acquired from a commercial vehicle’ s controller area network (CAN) bus. Our approach initially proposes a neural fault observer model and its adversarial learning scheme to represent the EPS system’s normal operating dynamics. We demonstrate that our proposed model can detect degradation levels down to ten percent from normal conditions under various driving scenarios based on an anomaly detection mechanism that outperforms baseline methods in quantitative and qualitative measures. Furthermore, we provide physically relevant intuitions of our closed-box model’s inference mechanism based on its attention-based saliency map to strengthen the reliability aspect of our data-driven approach. Lastly, we demonstrate that a quantized model can operate in real-time on an automotive electronic control unit (ECU) device. |
| format | Article |
| id | doaj-art-4894bff7a2ff451bb8adfe063813ca08 |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-4894bff7a2ff451bb8adfe063813ca082025-08-20T02:47:38ZengIEEEIEEE Access2169-35362025-01-0113423124232110.1109/ACCESS.2025.354692610908410Neural Fault Observer-Based On-Board Prognostics for Automotive Electric Power Steering SystemsGyuwon Kim0https://orcid.org/0000-0002-7259-099XJongick Won1https://orcid.org/0009-0009-7387-9216Sangjin Ko2Jinhwan Lee3https://orcid.org/0000-0002-6476-5868Global Research and Development Center, HL Mando Corporation, Seongnam, Republic of KoreaGlobal Research and Development Center, HL Mando Corporation, Seongnam, Republic of KoreaGlobal Research and Development Center, HL Mando Corporation, Seongnam, Republic of KoreaGlobal Research and Development Center, HL Mando Corporation, Seongnam, Republic of KoreaElectric power steering (EPS) systems form a fundamental unit in modern automotive vehicles, providing motor assistance to aid the driver’s manual maneuvering. Preventing loss of assist (LoA) in advance is critical for EPS systems to mitigate fatal accidents and reduce maintenance costs. As part of the recent interest in intelligent software-defined vehicles (SDV), data-driven approaches have gained much attention to overcome the limitations of conventional fail-safe mechanisms and provide value-added maintenance strategies. While related works in the field have shown promising results, they are limited to proof-of-concept studies validated under simulation and test-bed environments. Here, we present a novel deep learning (DL)-based method to detect EPS performance degradation using experimental data acquired from a commercial vehicle’ s controller area network (CAN) bus. Our approach initially proposes a neural fault observer model and its adversarial learning scheme to represent the EPS system’s normal operating dynamics. We demonstrate that our proposed model can detect degradation levels down to ten percent from normal conditions under various driving scenarios based on an anomaly detection mechanism that outperforms baseline methods in quantitative and qualitative measures. Furthermore, we provide physically relevant intuitions of our closed-box model’s inference mechanism based on its attention-based saliency map to strengthen the reliability aspect of our data-driven approach. Lastly, we demonstrate that a quantized model can operate in real-time on an automotive electronic control unit (ECU) device.https://ieeexplore.ieee.org/document/10908410/Deep learningelectric power steering (EPS)fault observerprognosticssoftware-defined vehicle (SDV) |
| spellingShingle | Gyuwon Kim Jongick Won Sangjin Ko Jinhwan Lee Neural Fault Observer-Based On-Board Prognostics for Automotive Electric Power Steering Systems IEEE Access Deep learning electric power steering (EPS) fault observer prognostics software-defined vehicle (SDV) |
| title | Neural Fault Observer-Based On-Board Prognostics for Automotive Electric Power Steering Systems |
| title_full | Neural Fault Observer-Based On-Board Prognostics for Automotive Electric Power Steering Systems |
| title_fullStr | Neural Fault Observer-Based On-Board Prognostics for Automotive Electric Power Steering Systems |
| title_full_unstemmed | Neural Fault Observer-Based On-Board Prognostics for Automotive Electric Power Steering Systems |
| title_short | Neural Fault Observer-Based On-Board Prognostics for Automotive Electric Power Steering Systems |
| title_sort | neural fault observer based on board prognostics for automotive electric power steering systems |
| topic | Deep learning electric power steering (EPS) fault observer prognostics software-defined vehicle (SDV) |
| url | https://ieeexplore.ieee.org/document/10908410/ |
| work_keys_str_mv | AT gyuwonkim neuralfaultobserverbasedonboardprognosticsforautomotiveelectricpowersteeringsystems AT jongickwon neuralfaultobserverbasedonboardprognosticsforautomotiveelectricpowersteeringsystems AT sangjinko neuralfaultobserverbasedonboardprognosticsforautomotiveelectricpowersteeringsystems AT jinhwanlee neuralfaultobserverbasedonboardprognosticsforautomotiveelectricpowersteeringsystems |