Rolling bearing fault diagnosis based on Attention LSTM

Long Short-Term Memory (LSTM) is well-suited for handling time-series data due to its ability to recognize dependencies between consecutive time points. In recent years, diagnosing rolling bearing faults using LSTM has garnered considerable interest. However, the memory bottleneck issue arises when LSTM is applied to long sequences, leading to the propagation of irrelevant information within the network. In this study, we propose an Attention LSTM fault diagnosis method. By incorporating the attention mechanism, this method concentrates on varying amounts of information in different LSTM gates. This approach aims to enhance the network's feature extraction capability, minimize the spread of unnecessary information, and address LSTM's memory bottleneck problem. The proposed method's effectiveness is confirmed through experiments on benchmark data.