Deep Transfer Learning Remaining Useful Life Prediction of Different Bearings

Due to less degradation data and the inconsistent data distribution of different bearings, remaining useful life (RUL) prediction methods based on deep learning still do not yield satisfactory predictive results. Using RUL prediction model trained with one bearing sample but tested with another bearing sample is challenging. To solve this problem, in this paper a new deep transfer learning-based RUL prediction method (DTL-RULPM) is proposed. We adopt min-max normalization to normalize the original vibration data of bearing. A three-layer sparse autoencoder is designed to extract the deep features of the source domain. Random data with standard normal distribution is generated with the consistent dimension of the high-dimensional features of the source domain. Maximum mean discrepancy (MMD) is used to minimize the probability distribution distance between the features of the source domain and the randomly generated data, such that the model can learn domain-invariant features of different bearings. Then we adopt a bi-directional long and short-term memory (Bi-LSTM) network to predict the RUL of the bearing. We use the IEEE PHM Challenge 2012 dataset to verify the proposed method. The results demonstrate that the proposed method improves the RUL prediction accuracy and robustness of different bearings.