Combination bidirectional long short-term memory and capsule network for rotating machinery fault diagnosis

For the application of deep learning in the field of fault diagnosis, its recognition accuracy is limited by the size and quality of the training samples, such as small size samples, low signal-to-noise ratio and different working conditions. In order to solve above problems, one novel method for fault classification is proposed based on a Bidirectional Long Short-Term Memory (Bi-LSTM) and a Capsule Network with convolutional neural network (BLC-CNN). The Bi-LSTM is utilized to achieve the feature denoising and fusion, which is extracted by CNN. The fault diagnosis with insufficient training samples is carried out by the capsule network. The influence of sample size on the method is discussed emphatically. The effectiveness and superiority of the proposed method are validated through analyzing the data of bearings and gears under different working conditions with different noise. The results indicate that the proposed method has good performance and immunity to noise.