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Timed failure propagation graph construction with supremal language guided Tree-LSTM and its application to interpretable fault diagnosis

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Abstract

Timed failure propagation graphs (TFPGs) perform fault diagnosis in a transparent way. However, accurate TFPGs depend on experts’ knowledge or accurate model of the system, which is hard to obtain for complex systems. This paper presents a data-driven TFPG construction approach for fault diagnosis, which finds spectral-timed failure propagation graphs (sTFPG) directly from data. The sTFPG construction problem is transformed into a spectral-temporal logic inference problem and solved with a tree-structured long short-term memory (LSTM) network. Therefore, no expert or accurate model is needed to construct the TFPG. Moreover, the training process is guided and sped up by the supremacy property of the spectral-temporal logic, which focuses on the structure information of the signals and incorporates the physical meanings in the learning process. Experimental results on real rolling element bearing data sets illustrate that the performance of the proposed fault diagnosis method is comparable with state-of-the-art machine learning methods in fault diagnosis accuracy, and outperforms the logic-based method in computational efficiency. Additionally, fault diagnosis with TFPG can be understood by humans and reveal the fault mechanism.

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  1. Shien-Ming Wu School of Intelligent Engineering, South China University of Technology, Guangzhou, 511442, China

    Gang Chen

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Chen, G. Timed failure propagation graph construction with supremal language guided Tree-LSTM and its application to interpretable fault diagnosis. Appl Intell 52, 12990–13005 (2022). https://doi.org/10.1007/s10489-021-03107-6

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