Multi-scale deep neural network for fault diagnosis method of rotating machinery. Issue 1 (2nd January 2023)

• Record Type: Journal Article
• Authors: Xie, Yining; Liu, Wang; Liu, Xiu; Chen, Deyun; Guan, Guohui; He, Yongjun
• Is Part Of: Ferroelectrics. Volume 602:Issue 1(2023)
• Page Start: 215
• Page End: 230
• Publication Date: 2023-01-02
• Subjects: Multi-scale -- deep learning -- fault diagnosis -- rotating machinery; Ferroelectricity -- Periodicals; Ferroelectric crystals -- Periodicals; 537
• DOI: 10.1080/00150193.2022.2079456
• Languages: English
• ISSNs: 0015-0193

Abstract

Abstract: In recent years, deep learning technology has shown great potential in the fault diagnosis of rotating machinery based on vibration signals. However, the feature extraction and noise robustness still need to be improved. To this end, we propose a multi-scale deep neural network fault diagnosis method. Firstly, multi-scale down sampling of time-domain vibration signals. Next, the attention long short-term memory network and the fully convolutional neural network of the multi-scale convolution kernel are used for feature extraction. Then, a fusion module is utilized to fuze the extracted features. The proposed method is evaluated on the public bearing datasets. Experimental results demonstrate that the proposed method can achieve high accuracy and noise robustness.