A Data-driven Approach for Fault Detection in the Alternator Unit of Automotive Systems

被引：0

作者：

Vijayan, Arunkumar ^{[1
]}

Tahoori, Mehdi B. ^{[1
]}

Kintzli, Ewald ^{[2
]}

Lohmann, Timm ^{[2
]}

Handl, Juergen Hans ^{[2
]}

机构：

[1] Karlsruhe Inst Technol KIT, Dept Comp Sci, Karlsruhe, Germany

[2] SEG Automot Germany GmbH, Stuttgart, Germany

来源：

2022 IEEE EUROPEAN TEST SYMPOSIUM (ETS 2022) | 2022年

关键词：

D O I：

10.1109/ETS54262.2022.9810432

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Functional safety is considered as a prominent dependability attribute in today's automotive world. It is extremely important to ensure safe operation of different automotive parts. An alternator unit is an electric generator used in modern automobiles to charge the battery and to power the electrical system when its engine is running Therefore, its correct operation is crucial for the overall automobile safety. In this work, we predict the health of an alternator on-the-fly using machine learning approaches for efficient yet accurate failure detection. We make use of inexpensive time domain features of alternator voltage waveform to achieve 97% prediction accuracy with no false positives. The correctness and usability of the proposed approach has been validated using realistic testing environment.

引用

页数：4

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