Abstract
Intelligent maintenance strategies based on effective Remaining Useful Life (RUL) prediction can significantly reduce the waste of maintenance resources. In recent years, RUL prediction of equipment has been a hot topic and a huge challenge for many experts. In this paper, a prediction method of RUL for equipment based on health index is proposed. Firstly, the health factors of the equipment were obtained through feature screening and Principal Component Analysis (PCA) dimension reduction, and the health status of the equipment was evaluated. Then, based on the input data and labels constructed by Health Index (HI), the prediction model is obtained through Long Short Time Memory (LSTM) network training. The vibration signals from accelerated degradation tests of rolling bearings are used to verify the proposed method. Compared with the existing literature, the proposed method is proved to be effective in predicting RUL of equipment.
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