Braking Sensor and Actuator Fault Diagnosis With Combined Model-Based and Data-Driven Pressure Estimation Methods

Abstract

The braking system is significant for intelligent vehicles, which influences vehicle safety directly. However, under harsh working conditions, the inevitable health degradation and even failure of the braking system may cause severe safety issues. This paper proposes a novel braking actuator and sensor fault diagnosis scheme with combined model-based and data-driven pressure estimation methods. The model-based wheel cylinder pressure (WCP) estimator is established first based on the mathematical model of the hydraulic control unit (HCU) from the perspective of cause. The data-driven WCP estimator is then proposed based on the vehicle dynamics multivariate time series (MTS) model with the gated recurrent unit (GRU) from the perspective of effect. However, acquiring a large dataset is a practical challenge for real vehicle tests, so a novel data augmentation (DA) method called shifting is presented to enhance the model generalization ability. Next, fault detection, isolation, and identification are realized by comparing the threshold and the cumulative sum (CUSUM) of the residuals generated by the combined WCP estimation methods. The validation results show that the proposed data-driven method outperforms the traditional multilayer perceptron (MLP), long short-term memory (LSTM), and Transformer regarding accuracy and generalization. Vehicle tests simulating sensor and actuator faults validate the proposed fault diagnosis scheme.