Rolling element bearing remaining useful life estimation based on a convolutional long-short-term memory network

Based on the recent success of deep neural networks in various artificial intelligence domains, we propose an end-to-end deep framework for RUL estimation based on convolutional and long-short-term memory (LSTM) recurrent units. First the neural network extracts the local features directly from sensor data using the convolutional layer, then an LSTM layer is introduced to capture the degradation process, finally the RUL is estimated using the LSTM outputs and the prediction time value. Experiments are conducted on the ball bearing data provided by FEMTO-ST Institute. The results demonstrate the efficiency of our approach.