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

Gu, Nong, Cao, Zhiqiang, Xie, Liangjun, Creighton, Douglas, Tan, Min and Nahavandi, Saeid 2013, Identification of concurrent control chart patterns with singular spectrum analysis and learning vector quantization, Journal of intelligent manufacturing, vol. 24, no. 6, pp. 1241-1252, doi: 10.1007/s10845-012-0659-0.

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Title Identification of concurrent control chart patterns with singular spectrum analysis and learning vector quantization
Author(s) Gu, Nong
Cao, Zhiqiang
Xie, Liangjun
Creighton, DouglasORCID iD for Creighton, Douglas orcid.org/0000-0002-9217-1231
Tan, Min
Nahavandi, SaeidORCID iD for Nahavandi, Saeid orcid.org/0000-0002-0360-5270
Journal name Journal of intelligent manufacturing
Volume number 24
Issue number 6
Start page 1241
End page 1252
Total pages 12
Publisher Chapman and Hall
Place of publication London, England
Publication date 2013-12
ISSN 0956-5515
Keyword(s) aluminium smelting
concurrent patterns
control charts
learning vector quantization networks
singular spectrum analysis
Summary 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.
Language eng
DOI 10.1007/s10845-012-0659-0
Field of Research 091006 Manufacturing Processes and Technologies (excl Textiles)
Socio Economic Objective 861101 Basic Aluminium Products
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2012, Springer Science+Business Media, LLC
Persistent URL http://hdl.handle.net/10536/DRO/DU:30047069

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