Steering Actuator Fault Diagnosis for Autonomous Vehicle With an Adaptive Denoising Residual Network
Huiyuan Xiong, Zhijun Wang, Guohui Wu, Yuelong Pan, Zichao Yang, Zhineng Long
Abstract
A steering actuator is the core component of an autonomous vehicle. Because of noise interference, the extraction of useful fault features for an accurate fault diagnosis of steering actuator becomes an arduous task. This paper proposes an adaptive de-noising residual network (AD-ResNet) for fault diagnosis of steering actuator with reduced noise interference and improved accuracy. First, a high-complexity model based on long short-term memory is built to estimate redundant signals, and then fault features are generated by comparing redundant signals with the measured sensor signals. Second, an attention unit is designed to determines the unique set of thresholds for each fault feature. Finally, hard thresholding is embedded in a deep network as a nonlinear transform layer to reduce noise. Simulation experiments were performed at different signal-to-noise ratios, and the results confirmed that the proposed method could adaptively de-noise the high-noise signals. The proposed method was then validated against a fault dataset that was built using road experiments with an autonomous vehicle, and it achieved an accuracy, a sensitivity, a specificity, and an F1-score of 91.66%, 91.66%, 97.22%, and 0.9159, respectively, averaged over all fault states. The proposed method outperformed the comparison methods in the experiments.