In order to grasp the degradation of rolling bearings and prevent the failure of mechanical equipment, a remaining useful life (RUL) prediction method of rolling bearings based on degradation detection and deep bidirectional long short-term memory networks (BiLSTM) was proposed, considering the incomplete degradation feature extraction and low prediction accuracy of existing methods. By extracting the characteristics of time domain, frequency domain, and time-frequency domain of the full-life bearing vibration signal, the monotonicity, trend, and robustness measurement indexes of each feature were calculated. The best feature set that can fully reflect the degradation information was constructed by ranking the weighted comprehensive indexes of the features. A degradation detection strategy was used to determine the degradation starting time for setting piecewise linear network label. The RUL prediction model based on deep BiLSTM was established and optimized through Dropout technology and piecewise learning rate. The model was verified by the full-life data set of rolling bearings. The results showed that compared with the support vector machine (SVM), the traditional recurrent neural network (RNN), the single-layer BiLSTM, and long short-term memory networks (LSTM) model without Dropout, the proposed method fitted the degradation trend best, and the root mean square error (RMSE) was the smallest and only 0.0281, which improved the accuracy of RUL prediction of rolling bearings, helped prevent bearing failure, and ensured the safe and reliable operation of rotating machinery.
Citation: Shuang Cai, Jiwang Zhang, Cong Li, Zequn He, Zhimin Wang. A RUL prediction method of rolling bearings based on degradation detection and deep BiLSTM[J]. Electronic Research Archive, 2024, 32(5): 3145-3161. doi: 10.3934/era.2024144
In order to grasp the degradation of rolling bearings and prevent the failure of mechanical equipment, a remaining useful life (RUL) prediction method of rolling bearings based on degradation detection and deep bidirectional long short-term memory networks (BiLSTM) was proposed, considering the incomplete degradation feature extraction and low prediction accuracy of existing methods. By extracting the characteristics of time domain, frequency domain, and time-frequency domain of the full-life bearing vibration signal, the monotonicity, trend, and robustness measurement indexes of each feature were calculated. The best feature set that can fully reflect the degradation information was constructed by ranking the weighted comprehensive indexes of the features. A degradation detection strategy was used to determine the degradation starting time for setting piecewise linear network label. The RUL prediction model based on deep BiLSTM was established and optimized through Dropout technology and piecewise learning rate. The model was verified by the full-life data set of rolling bearings. The results showed that compared with the support vector machine (SVM), the traditional recurrent neural network (RNN), the single-layer BiLSTM, and long short-term memory networks (LSTM) model without Dropout, the proposed method fitted the degradation trend best, and the root mean square error (RMSE) was the smallest and only 0.0281, which improved the accuracy of RUL prediction of rolling bearings, helped prevent bearing failure, and ensured the safe and reliable operation of rotating machinery.
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Shuang Cai, Jiwang Zhang, Cong Li, Zequn He, Zhimin Wang. A RUL prediction method of rolling bearings based on degradation detection and deep BiLSTM[J]. Electronic Research Archive, 2024, 32(5): 3145-3161. doi: 10.3934/era.2024144
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