Abstract
Electric power steering (EPS) is a fundamental part of an automotive system. Deviations from its anticipated operation have an impact on vehicle driving performance and handling, and could cause severe safety concerns. Reliable and efficient fault detection and diagnosis (FDD) methods are needed for EPS to guarantee the safe operation of the vehicle and for improved repairability of faulty components. In this paper, we develop an integrated model-based and data-driven FDD approach for the EPS system. Specifically, we develop a physics-based model of the EPS system and conduct a number of fault injection experiments to derive fault-sensor measurement dependencies. Then, we investigate various FDD schemes to detect and isolate the faults with special emphasis on rough set-theory-based fault classification. Finally, we compare its fault-classification accuracies with those from traditional classification methods. We demonstrate that the rough set-theory-based FDD approach is robust to missing data.
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