A novel RSG-based intelligent bearing fault diagnosis method for motors in high-noise industrial environment. (April 2022)

• Record Type: Journal Article
• Authors: Lyu, Pin; Zhang, Kewei; Yu, Wenbing; Wang, Baicun; Liu, Chao
• Is Part Of: Advanced engineering informatics. Volume 52(2022)
• Publication Date: 2022-04
• Subjects: Fault diagnosis -- Deep residual unit -- Soft thresholds -- Global context -- Deep learning; Computer-aided engineering -- Periodicals; Engineering -- Data processing -- Periodicals; 620.00285
• DOI: 10.1016/j.aei.2022.101564
• Languages: English
• ISSNs: 1474-0346

Abstract

Abstract: Bearing fault diagnosis is a critical and challenging task for prognostics and health management of motors. The ability to efficiently and accurately classify the fault categories based on sensor signals is the key to successful bearing fault diagnosis. Although various data-driven methods have been developed for fault diagnosis in recent years, automatic and effective extraction of discriminative fault features from high-noise vibration signals generated in the real-world industrial environment remains a challenging task. To tackle this challenge, this paper proposes a novel deep learning method based on the combination of r esidual building Unit, s oft thresholding and g lobal context, called RSG, to solve the complex mapping relationship between vibration signals and different types of bearing faults. The proposed RSG integrates the working mechanisms of soft threshold and global context to achieve effective noise reduction and feature extraction. A comparative analysis is performed to demonstrate the advantages of the proposed method. Furthermore, the proposed method is tested on a faulty motor dataset collected by our developed intelligent motor test platform based on Industrial Internet of Things. Experimental results show that our method can achieve an average fault diagnosis accuracy of 98%. Thus, the proposed method proves to be an efficient solution for intelligent bearing fault diagnosis for motors in a high-noise industrial environment.