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Data-driven identification model for associated fault propagation path
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DOI：10.1016/j.measurement.2021.110628.png)
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摘要
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In this paper, a data-driven identification model of the associated fault propagation path is proposed. Different from traditional fault diagnosis methods, the alternative approach is focused more on identifying the fault propagation path. Firstly, the KPCA method is used for fault detection. Then, a new transfer entropy method is proposed to construct the causality diagram. Finally, a kernel extreme learning machine-based search approach is proposed for fault propagation path identification. To demonstrate the effectiveness and applicability, the proposed model is applied to the Tennessee-Eastman Process and the real telemetry data of the satellite in orbit. Experimental results show that the number of causal edges obtained by the proposed method is only 12.62% of the traditional transfer entropy method, and 24.45% of the multivariate transfer entropy method.
Keyword:
Associated fault
Fault propagation path
Transfer entropy
Kernel extreme learning machine
期刊
IF:
5.6
论文数:
2.0W
被引数:
5.4W
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