Unbalanced Bearing Fault Diagnosis Under Various Speeds Based on Spectrum Alignment and Deep Transfer Convolution Neural Network

Bearing fault diagnosis plays a pivotal role in the safe and reliable operation of modern mechanical systems. However, the existing fault diagnosis methods rarely deal with the problem of category imbalance and various speeds concurrently, which cannot work effectively in practical scenarios. Considering the underlying similarities of data in frequency domain, data mining under various speeds can help to reduce the deviation of domain distribution. Therefore, a novel fault diagnosis method based on spectrum alignment (SA) and deep transfer convolution neural network (DTCNN) is proposed, where the SA and data augmentation module are designed to extract SA features from the unbalanced bearing data. The DTCNN model based on joint distribution adaptation is built to facilitate learning reliable domain-invariant features. Different from the existing studies, a more general transfer task with time-varying speed is considered, even with complex faults. For 14 transfer tasks in two unbalanced fault diagnosis cases under variable speed, the average accuracy, F1-score, and area under curve of the proposed method can reach more than 97.76%, 97.57%, and 98.75%, respectively. The results show that this method has superior diagnostic effect and better generalization ability than various state-of-the-art methods.