Fault Diagnosis Based on Strong Tracking SRUKF and Residual Chi-square Test

摘要

Background: Patents suggest that fault diagnosis is an important technology for maintenance,which is usually employed to avoid system catastrophic damage and ensure reliability.Objective: As traditional diagnosis method was limited in linear systems, a novel diagnosis method wasproposed to address hidden failure in nonlinear system.Methods: This method was based on the combination of strong tracking square root unscented Kalmanfilter (STSRUKF) and sequential residual χ2 (chi-square) test. After discussing some related patents andmethods, the χ2 test was introduced to check the STSRUKF abnormal residual, and this test solved theproblem of inability to locate fault for SRUKF. Firstly a χ2 test was used to locate faulty parameters viadetecting STSRUKF residual outputs generated by all the potential faulty models. Secondly, the locatedfaulty parameters were estimated by STSRUKF to indicate the faulty degree.Results: A simulation case was used to verify the reliability of the presented approach. The experimentresults indicated that the proposed algorithm could locate faulty parameter accurately, and theSTSRUKF estimation accuracy was higher than square root unscented Kalman filter (SRUKF) andstrong tracking unscented Kalman filter (STUKF).Conclusion: The method provides references for hidden fault detection and parameter estimation.