A novel intelligent fault diagnosis method based on variant sparse filtering and back-propagation. (March 2022)

• Record Type: Journal Article
• Authors: Chan, Lifeng; Cheng, Chun
• Is Part Of: Noise & vibration worldwide. Volume 53:Number 3(2022)
• Page Start: 133
• Page End: 141
• Publication Date: 2022-03
• Subjects: fault diagnosis -- rotating machinery -- feature learning -- variant sparse filtering; Noise control -- Periodicals; Damping (Mechanics) -- Periodicals; Soundproofing -- Periodicals; Damping (Mechanics); Noise control; Soundproofing; Periodicals; 620.205
• DOI: 10.1177/09574565211055786
• Languages: English
• ISSNs: 0957-4565

Abstract

Detecting the mechanical faults of rotating machinery in time plays a key role in avoiding accidents. With the coming of the big data era, intelligent fault diagnosis methods based on machine learning models have become promising tools. To improve the feature learning ability, an unsupervised sparse feature learning method called variant sparse filtering is developed. Then, a fault diagnosis method combining variant sparse filtering with a back-propagation algorithm is presented. The involvement of the back-propagation algorithm can further optimize the weight matrix of variant sparse filtering using label data. At last, the developed diagnosis method is validated by rolling bearing and planetary gearbox experiments. The experiment results indicate that the developed method can achieve high accuracy and good stability in rotating machinery fault diagnosis.