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

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

Volume: 12 Issue: 1

Author(s): Liu Jingzhong*

Affiliation:

• Hunan Institute of Technology, School of Mathematics, Physics and Energy Engineering, Hengyang, Hunan, 421101,China

Keywords: Square root unscented kalman filter, state and parameter joint filter, failure detection, chi-square test, Fault diagnosis, algorithm.

Abstract: 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 was proposed to address hidden failure in nonlinear system.

Methods: This method was based on the combination of strong tracking square root unscented Kalman filter (STSRUKF) and sequential residual χ2 (chi-square) test. After discussing some related patents and methods, the χ2 test was introduced to check the STSRUKF abnormal residual, and this test solved the problem of inability to locate fault for SRUKF. Firstly a χ2 test was used to locate faulty parameters via detecting STSRUKF residual outputs generated by all the potential faulty models. Secondly, the located faulty 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 experiment results indicated that the proposed algorithm could locate faulty parameter accurately, and the STSRUKF estimation accuracy was higher than square root unscented Kalman filter (SRUKF) and strong tracking unscented Kalman filter (STUKF).

Conclusion: The method provides references for hidden fault detection and parameter estimation.

Cite this article as:

Jingzhong Liu *, Fault Diagnosis Based on Strong Tracking SRUKF and Residual Chi-square Test, Recent Patents on Engineering 2018; 12 (1) . https://dx.doi.org/10.2174/1872212111666170329102749