NDLI: Identification of concurrent control chart patterns with singular spectrum analysis and learning vector quantization

Content Provider	Springer Nature Link
Author	Gu, ng Cao, Zhiqiang Xie, Liangjun Creighton, Douglas Tan, Min Nahavandi, Saeid
Copyright Year	2012
Abstract	Identification of unnatural control chart patterns (CCPs) from manufacturing process measurements is a critical task in quality control as these patterns indicate that the manufacturing process is out-of-control. Recently, there have been numerous efforts in developing pattern recognition and classification methods based on artificial neural network to automatically recognize unnatural patterns. Most of them assume that a single type of unnatural pattern exists in process data. Due to this restrictive assumption, severe performance degradations are observed in these methods when unnatural concurrent CCPs present in process data. To address this problem, this paper proposes a novel approach based on singular spectrum analysis (SSA) and learning vector quantization network to identify concurrent CCPs. The main advantage of the proposed method is that it can be applied to the identification of concurrent CCPs in univariate manufacturing processes. Moreover, there are no permutation and scaling ambiguities in the CCPs recovered by the SSA. These desirable features make the proposed algorithm an attractive alternative for the identification of concurrent CCPs. Computer simulations and a real application for aluminium smelting processes confirm the superior performance of proposed algorithm for sets of typical concurrent CCPs.
Starting Page	1241
Ending Page	1252
Page Count	12
File Format	PDF
ISSN	09565515
Journal	Journal of Intelligent Manufacturing
Volume Number	24
Issue Number	6
e-ISSN	15728145
Language	English
Publisher	Springer US
Publisher Date	2012-05-27
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Control charts Concurrent patterns Singular spectrum analysis Learning vector quantization networks Aluminium smelting Production/Logistics/Supply Chain Manufacturing, Machines, Tools Control, Robotics, Mechatronics
Content Type	Text
Resource Type	Article
Subject	Industrial and Manufacturing Engineering Artificial Intelligence Software

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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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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