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

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 levels of damages of rotating machinery were analysed, which refers to temporal association characteristics of vibration signal. Then Periodization long short-term memory (P-LSTM) fault diagnosis method was proposed. The method extracts features from periodization data and uses memory factors to forget some information that is 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. © 2022, Editorial Office of Journal of Vibration and Shock. All right reserved.