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

Abstract

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.