An enhanced non-local weakly supervised fault diagnosis method for rotating machinery. (15th February 2022)

• Record Type: Journal Article
• Authors: Ruan, Hulin; Wang, Yi; Li, Xiaomeng; Qin, Yi; Tang, Baoping
• Is Part Of: Measurement. Volume 189(2022)
• Publication Date: 2022-02-15
• Subjects: Weakly supervised learning -- Non-local feature extraction -- Rolling bearing -- Bevel gear -- Fault diagnosis; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2021.110433
• Languages: English
• ISSNs: 0263-2241

Abstract

Highlights: A weakly supervised rotating machinery fault diagnosis method is proposed. The weakly supervised fault diagnosis model is built upon non-local feature extraction and few-shot learning strategy. The proposed method is effective for fault diagnosis when few labeled training samples are available. The experimental results indicate the proposed method outperforms conventional fault diagnosis method. 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.