A comparative study of sensor fault diagnosis methods based on observer for ECAS system. (15th March 2017)

• Record Type: Journal Article
• Authors: Xu, Xing; Wang, Wei; Zou, Nannan; Chen, Long; Cui, Xiaoli
• Is Part Of: Mechanical systems and signal processing. Volume 87:Part B(2017)
• Page Start: 169
• Page End: 183
• Publication Date: 2017-03-15
• Subjects: Electronically controlled air suspension (ECAS) -- Sensor faults -- Fault detection and isolation (FDI) -- Algorithms comparison; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2016.06.037
• Languages: English
• ISSNs: 0888-3270

Abstract

Abstract: The performance and practicality of electronically controlled air suspension (ECAS) system are highly dependent on the state information supplied by kinds of sensors, but faults of sensors occur frequently. Based on a non-linearized 3-DOF 1/4 vehicle model, different methods of fault detection and isolation (FDI) are used to diagnose the sensor faults for ECAS system. The considered approaches include an extended Kalman filter (EKF) with concise algorithm, a strong tracking filter (STF) with robust tracking ability, and the cubature Kalman filter (CKF) with numerical precision. We propose three filters of EKF, STF, and CKF to design a state observer of ECAS system under typical sensor faults and noise. Results show that three approaches can successfully detect and isolate faults respectively despite of the existence of environmental noise, FDI time delay and fault sensitivity of different algorithms are different, meanwhile, compared with EKF and STF, CKF method has best performing FDI of sensor faults for ECAS system.