Deep Learning-based Motor Prognostics for Electric Power Steering Systems

Abstract

The electric power steering (EPS) system is a fundamental unit for modern automotive vehicles, providing motor assistance and various control functions to aid the driver's manual maneuvering. Preventing loss of assist (LoA) in advance is critical for EPS systems to mitigate fatal accidents and 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 and maintenance strategies. However, while related works in the field have shown promising results, they are limited to proof-of-concept studies for detecting and isolating artificially injected faults using simulation and test-bed data, which lacks relevance and poses limitations for practical in-vehicle implementation. Herein, we present a deep learning (DL)-based method to predict the motor degradation level in an EPS system using in-vehicle data acquired from the controller area network (CAN) bus. Our approach proposes a novel multivariate transformer (MVT)-based neural twin model of the EPS system, which is trained using an adversarial learning strategy by utilizing only the normal data. Our method can detect degradation levels down to ten percent from normal working conditions based on an anomaly detection mechanism that outperforms baseline methods in quantitative and qualitative measures.