Self-Attention ConvLSTM and Its Application in RUL Prediction of Rolling Bearings

Traditional long short-term memory (LSTM) neural networks generally face the challenge of low training efficiency and poor prediction accuracy for the remaining useful life (RUL) prediction due to their structure. In this study, a novel model called self-attention ConvLSTM (SA-ConvLSTM) neural network is proposed derived from ConvLSTM and a SA mechanism. First, convolution operators replace the fully connected layers inside the network structure to reduce the redundancy of the network and enhance its nonlinear modeling capability. Subsequently, a SA module is designed and embedded into the interior of the model by adaptively employing the corresponding important information to improve the prediction performance. Extensive experiments on the test rig and the actual wind farm confirmed that the developed SA-ConvLSTM has advantages over other conventional prediction methods in terms of convergence speed and prediction precision.