Concurrent control chart patterns recognition with singular spectrum analysis and support vector machine

被引:25
作者
Xie, Liangjun [1 ]
Gu, Nong [2 ]
Li, Dalong [3 ]
Cao, Zhiqiang [4 ]
Tan, Min [4 ]
Nahavandi, Saeid [2 ]
机构
[1] Schlumberger Ltd, Houston, TX 77073 USA
[2] Deakin Univ, Ctr Intelligent Syst Res, Waurn Ponds, Vic 3216, Australia
[3] Hewlett Packard Corp, Houston, TX 77070 USA
[4] Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
基金
澳大利亚研究理事会;
关键词
Control charts; Concurrent patterns; Singular spectrum analysis; Support vector machine; BLIND-EQUALIZATION; SELECTION; MODEL; IDENTIFICATION; ALGORITHM; VARIANCE; SYSTEM; SHIFTS;
D O I
10.1016/j.cie.2012.10.009
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Since abnormal control chart patterns (CCPs) are indicators of production processes being out-of-control, it is a critical task to recognize these patterns effectively based on process measurements. Most methods on CCP recognition assume that the process data only suffers from single type of unnatural pattern. In reality, the observed process data could be the combination of several basic patterns, which leads to severe performance degradations in these methods. To address this problem, some independent component analysis (ICA) based schemes have been proposed. However, some limitations are observed in these algorithms, such as lacking of the capability of monitoring univariate processes with only one key measurement, misclassifications caused by the inherent permutation and scaling ambiguities, and inconsistent solution. This paper proposes a novel hybrid approach based on singular spectrum analysis (SSA) and support vector machine (SVM) to identify concurrent CCPs. In the proposed method, the observed data is first separated by SSA into multiple basic components, and then these separated components are classified by SVM for pattern recognition. The scheme is suitable for univariate concurrent CCPs identification, and the results are stable since it does not have shortcomings found in the ICA-based schemes. Furthermore, it has good generalization performance of dealing with the small samples. Superior performance of the proposed algorithm is achieved in simulations. (C) 2012 Elsevier Ltd. All rights reserved.
引用
收藏
页码:280 / 289
页数:10
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