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Journal of Intelligent Manufacturing

Erschienen in:

27.03.2014

A multi-stage control chart pattern recognition scheme based on independent component analysis and support vector machine

verfasst von: Ling-Jing Kao, Tian-Shyug Lee, Chi-Jie Lu

Erschienen in: Journal of Intelligent Manufacturing | Ausgabe 3/2016

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Abstract

Recognition of unnatural control chart patterns (CCPs) is an important issue because the unnatural CCPs can be associated with specific assignable causes negatively affecting the manufacturing process. By assuming that an unnatural CCP is a combination of normal pattern and process disturbance, a multi-stage control chart pattern recognition scheme which integrates independent component analysis (ICA) and support vector machine (SVM) is proposed in this study. The proposed multi-stage ICA-SVM scheme first uses ICA to extract independent components (ICs) from the monitoring process data containing CCPs. The normal pattern and process disturbance hidden in the process data can be discovered in the ICs. Then, the IC representing the process disturbance can be identified. Finally, the identified IC and the data of the monitoring process are used as input variables to develop three different SVM models for CCP recognition. The simulation results show that the proposed multi-stage ICA-SVM scheme not only produces accurate and stable recognition results but also has better classification accuracy than four competing models.

Vorheriger Artikel Automated thermal fuse inspection using machine vision and artificial neural networks

Nächster Artikel Improved prediction of stability lobes in milling process using time series analysis

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Titel: A multi-stage control chart pattern recognition scheme based on independent component analysis and support vector machine
verfasst von: Ling-Jing Kao
Tian-Shyug Lee
Chi-Jie Lu
Publikationsdatum: 27.03.2014
Verlag: Springer US
Erschienen in: Journal of Intelligent Manufacturing / Ausgabe 3/2016
Print ISSN: 0956-5515
Elektronische ISSN: 1572-8145
DOI: https://doi.org/10.1007/s10845-014-0903-x

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