A wide kernel CNN-LSTM-based transfer learning method with domain adaptability for rolling bearing fault diagnosis with a small dataset

Abstract

It is difficult to obtain sufficient data for some machines, in addition, different working conditions result in different distributions of training data and test data, which lead to the failure of traditional deep learning methods in engineering applications. To solve these problems, we propose a novel deep learning framework called 1D-WCLT for rolling bearing fault diagnosis that combines wide kernel deep convolutional neural network and long short-term memory (WDCNN-LSTM). In this approach, a wide convolution kernel is utilized for local convolution, because the local convolution receptive field is increased, the fault feature contained in the low-middle frequency component is extracted. It is worth mentioning that the network complexity has not increased. Then, the transfer learning is applied to solve related but different domain problems, the useful knowledge learned by WDCNN-LSTM model under sufficient data conditions is transferred to diagnosis tasks with small dataset. After that, some state-of-the-art methods are applied to compare with the proposed method. At the end, experimental results showed that the proposed approach is an excellent algorithm for fault feature extraction of machinery and has much better identification accuracy and applicability than the other existing techniques.