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. 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.