Aerospace Equipment Fault Diagnosis Method Based on Fuzzy Fault Tree Analysis and Interpretable Interval Belief Rule Base

Mingxian Long, Hailong Zhu (), Guangling Zhang and Wei He
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Mingxian Long: School of Computer Science and Information Engineering, Harbin Normal University, Harbin 150025, China
Hailong Zhu: School of Computer Science and Information Engineering, Harbin Normal University, Harbin 150025, China
Guangling Zhang: School of Computer Science and Information Engineering, Harbin Normal University, Harbin 150025, China
Wei He: School of Computer Science and Information Engineering, Harbin Normal University, Harbin 150025, China

Mathematics, 2024, vol. 12, issue 23, 1-24

Abstract: The stable operation of aerospace equipment is important for space safety, and the fault diagnosis of aerospace equipment is of practical significance. A fault diagnosis system needs to establish clear causal relationships and provide interpretable determination results. Fuzzy fault tree analysis (FFTA) is a flexible and powerful fault diagnosis method, which can deeply understand causes and fault mechanisms. The interval belief rule base (IBRB) can describe uncertainty. In this paper, an interpretable fault diagnosis model (FFDI) for aerospace equipment based on FFTA and the IBRB is presented for the first time. Firstly, the initial FFDI is constructed with the assistance of FFTA. Second, a model inference is implemented based on an evidential reasoning (ER) parsing algorithm. Then, a projection covariance matrix adaptive evolutionary strategy algorithm with an interpretability constraints (IP-CMA-ES) optimization algorithm is used for optimization. Finally, the effectiveness of the FFDI is verified by a flywheel dataset. This method ensures the completeness of the rule base and the interpretability of the model, avoids the problem of exploding certain combinations of rules, and is suitable for the fault diagnosis of aerospace equipment.

Keywords: aerospace equipment; fault diagnosis; belief rule base; fuzzy fault tree analysis; interpretable model (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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