Intelligent machine fault diagnosis based on deep transfer convolutional neural network and extreme learning machine

With the in-depth study of researchers in the field of fault diagnosis, many machine fault diagnosis methods based on deep learning have been proposed. These methods have achieved remarkable results, but some practical issues still should be solved, such as lack of sufficient training data with labels and long training time. In this research, a machine fault diagnosis method using deep transfer convolutional neural network (DTCNN) and extreme learning machine (ELM) is proposed. Firstly, continuous wavelet transform (CWT) is adopted to transform vibration signals into 2D time-frequency images. Then, the optimal DTCNN pre-trained by ImageNet dataset is selected to extract high-level features of time-frequency images. The extracted high-level features further are input to the ELM classifier for fault classification. Finally, the extracted high-level features further are input to the ELM classifier for fault classification. The effectiveness and efficiency of the proposed method are verified on two well-known datasets, including the Case Western Reserve University (CWRU) motor bearing dataset and the KAt bearing dataset of Paderborn University. The experimental results show that the proposed method can greatly reduce the computational time of the model while ensuring high accuracy of diagnosis, and DTCNN-ELM outperforms other state-of-the-art methods.