A wavelet convolutional capsule network with modified super resolution generative adversarial network for fault diagnosis and classification

The study of fault diagnosis and classification has gained tremendous attention in various aspects of modern industry. However, the performance of traditional fault diagnosis technique solely depends on handcrafted features based on expert knowledge which is difficult to pre-design and has failed in several applications. Deep learning (DL) has achieved remarkable performance in hierarchical feature extraction and learning distinctive feature of dataset from related distribution. However, the challenge associated with DL models is that max-pooling operation usually leads to loss of spatial details during high-level feature extraction. Another concern is the low quality characteristics of 2D time-frequency image which is mostly caused by the presence of noise and poor resolution. This paper proposes a modified wavelet convolutional capsule network with modified enhanced super resolution generative adversarial network plus for fault diagnosis and classification. It uses continuous wavelet transform to convert raw data signals to 2D time-frequency images and applies super resolution generative adversarial technique to enhance the quality of the time-frequency images and finally, the convolutional capsule network learns the extracted high-level features without loss of spatial details for the diagnosis and classification of faults. We validated our proposed model on the famous motor bearing dataset from the Case Western Reserve University. The experimental results show that our proposed fault diagnostic model obtains higher diagnosis accuracy of 99.84% outweighing most traditional deep learning models including state-of-the-art methods.