Abstract:

Bearing is one of the important components in many production equipment, and the study of its remaining useful life is of great value. A prediction method for remaining useful life of bearings based on spatial-temporal dual-cell state self-adaptive network (ST-DCSN) was proposed for the prediction error caused by the degradation state change and timing correlation that are not fully considered in the traditional bearing remaining life prediction in different environments. This paper adopted an embedded convolution of coexisting temporal and spatial states to operate the dual-state recurrent network and introduced spatio-temporal dual-cell state and sub-cell state differential mechanism to realize adaptive perception of bearing attenuation states. This method effectively captures the feature state of the bearing monitoring data in both temporal and spatial dimensions, so as to solve the influence of environmental and timing problems on the prediction performance of bearing remaining life prediction. To investigate the effectiveness of the proposed method and compare it with other state-of-the-art approaches, two real bearing life accelerated degradation datasets, namely FEMTO-ST and XJTU-SY, were used for validation. Both ablation experiments and comparative experiments were conducted, and four evaluation metrics were employed to assess the prediction performance. The ablation results demonstrate that the complete version of ST-DCSN outperforms the experiment groups with removed spatial cell and dynamic and static sub-cell in terms of stability and performance metrics. Compared to other methods, the proposed method achieves superior prediction performance with higher fitness and better stability in the prediction results at the end of life of bearing. This demonstrates that the ST-DCSN method can effectively improve the accuracy of bearing’s remaining useful life prediction.

Key words: spatial-temporal dual cell, adaptive network, remaining useful life prediction, bearing, deep learning