A 2-D Long Short-Term Memory Fusion Networks for Bearing Remaining Useful Life Prediction

Remaining useful life (RUL) prediction plays a significant role in prognostic and health management (PHM), and it can reduce the cost of unwanted failures and improve the reliability of industrial equipment and systems. In recent years, deep learning and sensor technology have boosted fault detection accuracy. This article proposes a two-stage prediction method based on 2-D long short-term memory (2D-LSTM) fusion networks with multisensor data for RUL prediction. This method first uses the Wilson Amplitude (WAMP) feature to automatically detect the fault occurrence time (FOT) and divide the bearing's degradation process into two stages: health and degradation state. Then a 2D-LSTM fusion network is employed to predict the RUL of bearings, including multiple subnetworks. In each subnetwork, deep temporal features of a single sensor's data are extracted by 2D-LSTM, which can capture both vertical and horizontal dependencies of data. Furthermore, an information fusion unit (IFU) is created to help the model incorporate features captured from each 2D-LSTM subnetwork. Experiments on two real-world bearing datasets show that our model's effectiveness is comparable to that of other existing methods. In addition, ablation studies are performed to verify the requirement and efficacy of each component of our proposed model.