An enhanced non-local weakly supervised fault diagnosis method for rotating machinery

Abstract

Despite the considerable success achieved by deep learning-based fault diagnosis methods, a powerful deep learning model must require a substantial set of training data to obtain a strong generalization ability in practical application. Focusing on the fault diagnosis case under small data sets, an enhanced non-local weakly supervised fault diagnosis method is proposed in this paper. In the proposed method, the few-shot learning strategy is presented to perform learning task augmentation to alleviate the overfitting problem caused by scarce training data, and the non-local operation is investigated to further enhance the feature extraction ability of convolutional neural network through capturing the long-range dependency. Two fault diagnosis experiments which were conducted on rolling bearing and bevel gear are carried out. The proposed method obtains 97.23% and 99.76% fault diagnosis accuracy in bearing data sets and gear data sets, which outperforms the traditional fault diagnosis methods.