A CNN‐BiLSTM‐Bootstrap integrated method for remaining useful life prediction of rolling bearings

Abstract Rolling bearings, an essential fundamental component in machinery and equipment, have been widely used. Predicting the remaining useful life (RUL) of rolling bearings helps maintain the reliability of mechanical systems. Accurate prediction of RUL requires extracting deep features in complex non‐linear vibration signals, the prediction results often vary widely. This paper proposes a RUL prediction method based on convolutional neural network (CNN), bi‐directional long‐short term memory (BiLSTM), and bootstrap method (CNN‐BiLSTM‐Bootstrap) to model the forecasting uncertainty. The first step is to extract the first prediction time (FPT) of the degradation phase of rolling bearings using an adaptive method for the 3σ intervals of rolling bearing vibration signal kurtosis. The model extracts the spatio‐temporal features through CNN and BiLSTM, and combines the bootstrap method to quantify the RUL prediction interval (PI) of rolling bearings. The comparison with existing models verified the effectiveness and generalization of the proposed model.