A wavelet packet spectral subtraction and convolutional neural network based method for diagnosis of system health
- Authors
- Van Huan Pham; Han, Soonyoung; Minh Duc Do; Choi, Hae-Jin
- Issue Date
- Dec-2019
- Publisher
- KOREAN SOC MECHANICAL ENGINEERS
- Keywords
- Diagnosis; Convolutional neural network; Wavelet packet decomposition; Vibration signal; Spectral subtraction; Prognosis health management
- Citation
- JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY, v.33, no.12, pp 5683 - 5687
- Pages
- 5
- Journal Title
- JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY
- Volume
- 33
- Number
- 12
- Start Page
- 5683
- End Page
- 5687
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/38612
- DOI
- 10.1007/s12206-019-1111-6
- ISSN
- 1738-494X
1976-3824
- Abstract
- Health monitoring systems play a key role inside smart factories. To enhance the real-time capability and reliability of health monitoring systems, we propose a fully automatic method for machine diagnosis. Firstly, acquired vibration signals are converted into high-resolution images by wavelet packet spectral subtraction. Next, a trained convolutional neural network (CNN) automatically extracts important features and determines the current health of the machine. The performance of the proposed method is demonstrated by employing a diagnosis problem of a bearing system. The result shows an outstanding classification accuracy of 99.64 % even with a small amount of training data (5 % of the data).
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Collections - College of Engineering > School of Mechanical Engineering > 1. Journal Articles
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