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Journal of Control Science and Engineering
Volume 2018 (2018), Article ID 5205698, 8 pages
Research Article

Time-Varying Fault Diagnosis for Asynchronous Multisensor Systems Based on Augmented IMM and Strong Tracking Filtering

1School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China
2Beijing Key Laboratory of Knowledge Engineering for Materials Science, Beijing 100083, China

Correspondence should be addressed to Kaixiang Peng

Received 29 June 2017; Accepted 12 November 2017; Published 2 January 2018

Academic Editor: Chunhui Zhao

Copyright © 2018 Yanyan Hu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


A fault detection, isolation, and estimation approach is proposed in this paper based on Interactive Multimodel (IMM) fusion filtering and Strong Tracking Filtering (STF) for asynchronous multisensors dynamic systems. Time-varying fault is considered and a candidate fault model is built by augmenting the unknown fault amplitude directly into the system state for each kind of possible fault mode. By doing this, the dilemma of predetermining the fault extent as model design parameters in traditional IMM-based approaches is avoided. After that, the time-varying fault amplitude is estimated based on STF using its strong ability to track abrupt changes and robustness against model uncertainties. Through fusing information from multiple sensors, the performance of fault detection, isolation, and estimation is approved. Finally, a numerical simulation is performed to demonstrate the feasibility and effectiveness of the proposed method.