Prediction of Residual Life of Rolling Bearing Based on N-BEATS Neural Network
SHI Pei-ming1,SU Shi-min1,MA Hui-zhong1,XU Xue-fang1,HAN Dong-ying2
1. Key Laboratory of Measurement Technology and Instrument of Hebei Province, Yanshan University, Qinhuangdao, Hebei 066004, China
2. School of Vehicles and Energy, Yanshan University, Qinhuangdao, Hebei 066004, China
Abstract：In order to effectively predict the remaining service life of the bearing, and to solve the problem of complex and unstable signal sequences collected during the prediction, resulting in low prediction accuracy and performance, the empirical mode decomposition is used to adaptively decompose the signal sequence, the dynamic time warping algorithm is used to screen the main degradation features, and the trend features of the signal sequence are extracted. The deep neural network N-BEATS with residual principle is used for prediction. For the problem of less prediction history data, the prediction structure combining recursion and direct is used to predict the remaining life in multiple steps. Comparing N-BEATS with long-term and short-term memory neural network and grey prediction model, the results show that the average absolute error of the prediction results of the proposed method is increased by 3.2% and 3.3% respectively compared with LSTM and grey prediction model under different working conditions, the relative root mean square error increased by 3.5% and 3.1% respectively.
时培明,苏世敏,马慧中,许学方,韩东颖. 基于N-BEATS神经网络的滚动轴承剩余寿命预测[J]. 计量学报, 2023, 44(8): 1240-1247.
SHI Pei-ming,SU Shi-min,MA Hui-zhong,XU Xue-fang,HAN Dong-ying. Prediction of Residual Life of Rolling Bearing Based on N-BEATS Neural Network. Acta Metrologica Sinica, 2023, 44(8): 1240-1247.
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2023, Vol. 44 
