NDLI: Process disturbance identification using ICA-based image reconstruction scheme with neural network

Content Provider	IEEE Xplore Digital Library
Author	Shien-Ping Huang Chih-Chou Chiu Cook, D.F. Chi-Jie Lu
Copyright Year	2007
Description	Author affiliation: Taipei Coll. of Maritime Technol., Taipei (Shien-Ping Huang)
Abstract	Process monitoring and control of a production line are often used in industry to maintain high-quality production and to facilitate high levels of efficiency in the process. However, current process control techniques, such as statistical process control (SPC) and engineering process control (EPC), may not effectively detect abnormalities, especially when autocorrelation is present in the process. This paper proposes an independent component analysis (ICA)-based image reconstruction scheme with a neural network approach to identify disturbances and recognize shifts in the correlated process parameters. The resulting image can effectively remove the textual pattern and preserve disturbances distinctly. We illustrate our approach using two most commonly encountered disturbances, the step-change disturbance and the linear disturbance, in a manufacturing process. The experimental results reveal that the proposed method is effective and efficient for disturbance identification in correlated process parameters when disturbance is significant. Additionally, the identification rate made by the proposed method is slightly influenced by the data correlation.
Starting Page	1103
Ending Page	1109
File Size	345032
Page Count	7
File Format	PDF
ISBN	9781424415281
DOI	10.1109/IEEM.2007.4419363
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-12-02
Publisher Place	Singapore
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image recognition Industrial control Process control ICA Independent component analysis neural networks SPC Maintenance engineering Image reconstruction Neural networks Production EPC Autocorrelation Monitoring
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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