Manufacturing State Recognition of Machine Center Based on Revised WPT and PCA
Article No.: 104, Pages 1 - 5
Abstract
The abnormal manufacturing state recognition of machine center is of great significance to reduce downtime and ensure quality of productions. A revised wavelet packet transform and principal component analysis method has been developed to extract features from vibration signals of machine center. The revised wavelet packet transform employs local discriminant bases method to select optimal wavelet packet nodes. The principal component analysis is conducted for features dimensionality reduction to obtain the final features. A BP neural network is used to classify manufacturing states based on final features of three different manufacturing processes. The comparison result indicates that the revised WPT and PCA method is an efficient feature extraction method for manufacturing stage recognition of machine tools.
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Index Terms
- Manufacturing State Recognition of Machine Center Based on Revised WPT and PCA
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Published In
October 2020
1038 pages
ISBN:9781450377720
DOI:10.1145/3424978
- Conference Chair:
- Ali Emrouznejad,
- Program Chair:
- Jui-Sheng Rayson Chou
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 20 October 2020
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- Research-article
- Research
- Refereed limited
Funding Sources
- Science and Technology Research Program of Chongqing Municipal Education Commission
- Graduate Student Innovation Program of Chongqing University of Technology
- Chongqing Science and Technology Commission
- National Key Research and Development Project
Conference
CSAE 2020
CSAE 2020: The 4th International Conference on Computer Science and Application Engineering
October 20 - 22, 2020
Sanya, China
Acceptance Rates
CSAE '20 Paper Acceptance Rate 179 of 387 submissions, 46%;
Overall Acceptance Rate 368 of 770 submissions, 48%
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