Fault diagnosis of rotating machinery based on time-series correlation analysis

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Journal of Vibration and Shock ›› 2022, Vol. 41 ›› Issue (8) : 171-178.

TAN Shuai1,MA Yao1,SHI Hongbo1,CHANG Yuqing2,GUO Lei1

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Abstract

With the application of large-scale rotating machinery, more and more attention has been paid to fault diagnosis of high-speed rotating machinery. Due to the periodic rotation characteristics of rotating machinery, there is a strong temporal correlation between signals. Fault features will gradually transfer during the week of rotation. In this paper, different types of faults and different level of damages of rotating machinery were analysed, which refer to temporal association characteristics of vibration signal. Then Periodization long short-term memory (Periodization – LSTM,P-LSTM) fault diagnosis method was proposed. The method extract features from periodization data and use memory factors to forget some information that of insignificance. Finally, the performance analysis and test of the proposed method were carried out based on the multi-fault data of rolling bearings, which verified the effectiveness of p-lstm method in learning the time-series correlation characteristics of rotating machinery during the cycle, as well as the accuracy of fault diagnosis.

Key words

Timing correlation / Long short-term memory / Rolling bearing / Feature extraction / Fault diagnosis

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TAN Shuai1,MA Yao1,SHI Hongbo1,CHANG Yuqing2,GUO Lei1. Fault diagnosis of rotating machinery based on time-series correlation analysis[J]. Journal of Vibration and Shock, 2022, 41(8): 171-178

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