An interpretable method for inertial platform fault diagnosis based on combination belief rule base

• Published in: Measurement : journal of the International Measurement Confederation Vol. 217; p. 112960
• Main Authors: Chen, Manlin, Zhou, Zhijie, Han, Xiaoxia, Feng, Zhichao, Cao, You
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.08.2023
• Subjects: Belief rule base; Fault diagnosis; Inertial platform; Interpretability; Reliability; Fault diagnosis; Belief rule base; Reliability; Interpretability; Inertial platform
• ISSN: 0263-2241; 1873-412X
• DOI: 10.1016/j.measurement.2023.112960

•An interpretable fault diagnosis method for inertial platform is proposed.•The qualitative data and quantitative data are used in a unified way.•The interpretability of the BRB is quantified. As a key component of navigation equipment, the inertial platform needs high reliability, so an accurate and interpretable method for fault diagnosis is necessary. The belief rule base (BRB) has strong interpretability and its ability to process semi-quantitative information. However, the high complexity of inertial platforms leads to the existence of multiple indicators for use in fault diagnosis, which leads to the combinatorial explosion of rules problem in BRB. Considering this problem, an interpretable method for inertial platform fault diagnosis based on combination BRB is proposed here. This paper provides two main contributions: 1) A BRB optimization method considering the correlation coefficient is proposed, which maintains the interpretability of sub-BRBs in the combination BRB, and 2) a method is proposed to calculate the sub-BRB weights based on the precision of the sub-BRBs and the occurrence times of repeated indicators, thus ensuring that the fusion process is explainable when the ER algorithm is used for the fusion of sub-BRBs. The proposed method ensures the interpretability of the final result during fault diagnosis while maintaining the reliability of the inertial platform. Finally, the effectiveness of the proposed method is verified against state-of-the-art methods using actual operation data from an inertial platform.