Associated Fault Diagnosis of Power Supply Systems Based on Graph Matching: A Knowledge and Data Fusion Approach

Laifa Tao, Haifei Liu, Jiqing Zhang, Xuanyuan Su, Shangyu Li, Jie Hao, Chen Lu, Mingliang Suo and Chao Wang ()
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Laifa Tao: Institute of Reliability Engineering, Beihang University, Beijing 100191, China
Haifei Liu: Institute of Reliability Engineering, Beihang University, Beijing 100191, China
Jiqing Zhang: China International Engineering Consulting Corporation, Beijing 100048, China
Xuanyuan Su: Institute of Reliability Engineering, Beihang University, Beijing 100191, China
Shangyu Li: Institute of Reliability Engineering, Beihang University, Beijing 100191, China
Jie Hao: AVIC Xi’an Flight Automatic Control Research Institute, Xi’an 710076, China
Chen Lu: Institute of Reliability Engineering, Beihang University, Beijing 100191, China
Mingliang Suo: Institute of Reliability Engineering, Beihang University, Beijing 100191, China
Chao Wang: Institute of Reliability Engineering, Beihang University, Beijing 100191, China

Mathematics, 2022, vol. 10, issue 22, 1-28

Abstract: With the rapid development of more-electric and all-electric aircraft, the role of power supply systems in aircraft is becoming increasingly prominent. However, due to the complex coupling within the power supply system, a fault in one component often leads to parameter abnormalities in multiple components within the system, which are termed associated faults. Compared with conventional faults, the diagnosis of associated faults is difficult because the fault source is hard to trace and the fault mode is difficult to identify accurately. To this end, this paper proposes a graph-matching approach for the associated fault diagnosis of power supply systems based on a deep residual shrinkage network. The core of the proposed approach involves supplementing the incomplete prior fault knowledge with monitoring data to obtain a complete cluster of associated fault graphs. The association graph model of the power supply system is first constructed based on a topology with characteristic signal propagation and the associated measurements of typical components. Furthermore, fault propagation paths are backtracked based on the Warshall algorithm, and abnormal components are set to update and enhance the association relationship, establishing a complete cluster of typical associated fault mode graphs and realizing the organic combination and structured storage of knowledge and data. Finally, a deep residual shrinkage network is used to diagnose the associated faults via graph matching between the current state graph and the historical graph cluster. The comparative experiments conducted on the simulation model of an aircraft power supply system demonstrate that the proposed method can achieve high-precision associated fault diagnosis, even under circumstances where there are an insufficient number of samples and missing parameters.

Keywords: deep residual shrinkage network; association graph model; knowledge and data fusion; Warshall algorithm; power supply 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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