A rolling bearing fault diagnosis method under insufficient samples condition based on MSLSTM transfer learning

It usually affects the accuracy and reliability of deep learning based intelligent diagnosis methods under the condition of insufficient samples. Existing methods for handling insufficient samples often have problems such as requiring rich expert experience or consuming a lot of time. To solve the above problems, a rolling bearing fault diagnosis method under insufficient samples condition based on multi-scale long-term and short-term memory network (MSLSTM) transfer learning is proposed, which mainly consists of an improved long-term and short-term memory network named as MSLSTM and transfer learning. By introducing multi-scale convolution operation into the traditional LSTM to improve its drawback that only extracts single type of fault feature information, which leads to poor diagnostic performance in noisy environments. Besides, the pooling layer and global average pooling layer in traditional LSTM are replaced with convolution operation to avoid the problem of information loss. Subsequently, the MSLSTM is combined with transfer learning, and a rolling bearing fault diagnosis method under insufficient samples condition based on MSLSTM transfer learning is proposed, which fine tunes the model parameters using a small amount of target domain data. Feasibility of the proposed method is verified through two kinds of experiments. The proposed method has stronger feature extraction ability and training efficiency compared with other models.