Simultaneous-Fault Diagnosis of Satellite Power System Based on Fuzzy Neighborhood ζ -Decision-Theoretic Rough Set

Laifa Tao, Chao Wang, Yuan Jia, Ruzhi Zhou, Tong Zhang, Yiling Chen, Chen Lu and Mingliang Suo ()
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Laifa Tao: Institute of Reliability Engineering, Beihang University, Beijing 100191, China
Chao Wang: Institute of Reliability Engineering, Beihang University, Beijing 100191, China
Yuan Jia: Beijing Institute of Radio Metrology and Measurement, China Aerospace Science and Industry Corporation Limited, Beijing 100039, China
Ruzhi Zhou: Shanghai Institute of Satellite Engineering, China Aerospace Science and Technology Corporation, Shanghai 201109, China
Tong Zhang: Marine Design and Research Institute of China, China State Shipbuilding Corporation Limited, Shanghai 200011, China
Yiling Chen: Institute of Reliability Engineering, Beihang University, Beijing 100191, China
Chen Lu: Institute of Reliability Engineering, Beihang University, Beijing 100191, China
Mingliang Suo: Institute of Reliability Engineering, Beihang University, Beijing 100191, China

Mathematics, 2022, vol. 10, issue 19, 1-22

Abstract: Due to the increasing complexity of the entire satellite system and the deteriorating orbital environment, multiple independent single faults may occur simultaneously in the satellite power system. However, two stumbling blocks hinder the effective diagnosis of simultaneous-fault, namely, the difficulty of obtaining the simultaneous-fault data and the extremely complicated mapping of the simultaneous-fault modes to the sensor data. To tackle the challenges, a fault diagnosis strategy based on a novel rough set model is proposed. Specifically, a novel rough set model named FN ζ DTRS by introducing a concise loss function matrix and fuzzy neighborhood relationship is proposed to accurately mine and characterize the relationship between fault and data. Furthermore, an attribute rule-based fault matching strategy is designed without using simultaneous-fault data as training samples. The numerical experiments demonstrate the effectiveness of the FN ζ DTRS model, and the diagnosis experiments performed on a satellite power system illustrate the superiority of the proposed approach.

Keywords: simultaneous-fault diagnosis; rough set; attribute reduction; satellite power system (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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