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Dependency Matrix-Based Fast Fault Diagnosis Using Search Algorithm
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AI
DOI：10.1109/TIM.2023.3259042.png)
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摘要
En 中文
The dependency matrix is widely used in fault diagnosis as a valid diagnostic model. However, the conventional dependency matrix-based diagnosis method pays more attention to the extension and improvement of the dependency matrix, and the timeliness is rarely discussed. To solve the problem and improve the diagnosis efficiency, fast diagnosis method using two search algorithms is proposed. The hash function is introduced, and the dependency matrix is transformed into the key-value pairs. The binary search algorithm is brought in to locate the desired failure mode by splitting the dependency matrix. Thus, the fault diagnosis is converted into a search process, which can improve the diagnosis efficiency by making full use of the advantages of the search algorithms. Finally, a numerical simulation is utilized to verify the proposed method. The proposed methods are applied to the matrices of different sizes. The results show that the hash-based diagnosis method has significantly better efficiency, making it more suitable for onboard diagnosis. On the other hand, the binary search-based method is advantageous from a memory perspective only when the matrix size is small.
Keyword:
Dependency matrix
fault diagnosis
search algorithm
testability
期刊
IF:
5.9
论文数:
2.0W
被引数:
5.8W
机构
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