Fault diagnosis and prognosis of steer-by-wire system based on finite state machine and extreme learning machine

• Published in: Neural computing & applications Vol. 34; no. 7; pp. 5081 - 5095
• Main Authors: Lan, Dun, Yu, Ming, Huang, Yunzhi, Ping, Zhaowu, Zhang, Jie
• Format: Journal Article
• Language: English
• Published: London Springer London 01.04.2022; Springer Nature B.V
• Subjects: Artificial Intelligence; Artificial neural networks; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Condition monitoring; Data Mining and Knowledge Discovery; Degrees of freedom; Drive by wire; Failure prevention; Fault detection; Fault diagnosis; Finite state machines; Image Processing and Computer Vision; Life prediction; Machine learning; Probability and Statistics in Computer Science; Prognosis; Redundancy; Self alignment; Special Issue on Computational Intelligence-based Control and Estimation in Mechatronic Systems; State space models; Integrated condition monitoring; Extreme learning machine; Finite state machine; Steer-by-wire system
• ISSN: 0941-0643; 1433-3058
• DOI: 10.1007/s00521-021-06028-0

In this paper, an integrated condition monitoring method combining model-based fault diagnosis and data-driven prognosis is proposed for steer-by-wire (SBW) system. First, the SBW system is modeled by bond graph (BG) technique and a two-degree-of-freedom (2-DOF) state-space model of the vehicle is built. Based on the 2-DOF model, the estimated self-aligning torque is used for the control of feedback motor. The fault detection is carried out by evaluating the analytical redundancy relations derived from the BG model. Since the fault isolation performance is essential to subsequent fault estimation process, a new fault isolation method based on finite state machine is developed to improve the isolation ability by combining the dependent and independent analytical redundancy relations, where the number of potential faults could be decreased. In order to refine the possible fault set to determine the true fault, a cuckoo search (CS)–particle filter is developed for fault estimation. Based on the estimated true fault, prognosis can be implemented which is important to achieve failure prevention and prolong system lifespan. To this end, an optimized extreme learning machine (OELM) is proposed where the input weights and hidden layer biases are optimized by CS. Based on data representing fault values obtained from the fault identification, the OELM model is trained for remaining useful life prediction of failing component. Finally, the proposed methodologies are validated by simulations.