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- Title
A new fault diagnosis method using deep belief network and compressive sensing.
- Authors
Yunfei Ma; Xisheng Jia; Huajun Bai; Guanglong Wang; Guozeng Liu; Chiming Guo
- Abstract
Compressive sensing provides a new idea for machinery monitoring, which greatly reduces the burden on data transmission. After that, the compressed signal will be used for fault diagnosis by feature extraction and fault classification. However, traditional fault diagnosis heavily depends on the prior knowledge and requires a signal reconstruction which will cost great time consumption. For this problem, a deep belief network (DBN) is used here for fault detection directly on compressed signal. This is the first time DBN is combined with the compressive sensing. The PCA analysis shows that DBN has successfully separated different features. The DBN method which is tested on compressed gearbox signal, achieves 92.5% accuracy for 25% compressed signal. We compare the DBN on both compressed and reconstructed signal, and find that the DBN using compressed signal not only achieves better accuracies, but also costs less time when compression ratio is less than 0.35. Moreover, the results have been compared with other classification methods.
- Subjects
FAULT diagnosis; GEARBOXES; DIAGNOSIS methods; COMPRESSED sensing; MONITORING of machinery; SIGNAL reconstruction; FEATURE extraction
- Publication
Journal of Vibroengineering, 2020, Vol 22, Issue 1, p83
- ISSN
1392-8716
- Publication type
Article
- DOI
10.21595/jve.2019.20850