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.

Review of research on signal decomposition and fault diagnosis of rolling bearing based on vibration signal

“…Azim et al [221] adopted the self-improved salp swarm optimization model to optimize the weights of LSTM and ANN for fine diagnosis of bearing faults. Zhu et al [222] proposed a deep learning framework combining wide kernel deep CNN and LSTM, which had good identification accuracy and applicability. Ahsan et al [223] proposed a deep convolution neural network (DCNN)-LSTM model with SoftMax classifier, which could automatically extract features from vibration signal.…”