Prediction Method of Remaining Useful Life of Antifriction Bearing Based on EBSA-LSTM Network

In the theoretical aspect, the remaining service life prediction method of rolling bearing is studied, and then it is extended to other components or system level, so as to realize the supplement and perfection of reliability engineering. At the practical level, the remaining service life prediction method of rolling bearing is combined with the maintenance plan to provide guidance for equipment management, minimize the maintenance times and time, and maximize the economic saving. Using the LSTM network, the prediction of rolling bearings is studied. In order to obtain a better residual service life prediction effect. First, we propose the enhanced Beetle Antennae search algorithm and the enhanced LSTM network, then combine the optimal network structure of the enhanced LSTM. Finally, the validity of the method proposed in this chapter is verified by comparing with the existing prediction methods.