Concurrent control chart patterns recognition with singular spectrum analysis and support vector machine
文献类型：期刊论文
| 作者 | Xie, Liangjun1; Gu, Nong2; Li, Dalong3; Cao, Zhiqiang4 ; Tan, Min4; Nahavandi, Saeid2
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| 刊名 | COMPUTERS & INDUSTRIAL ENGINEERING
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| 出版日期 | 2013 |
| 卷号 | 64期号:1页码:280-289 |
| 关键词 | Control charts Concurrent patterns Singular spectrum analysis Support vector machine |
| 英文摘要 | 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. |
| WOS标题词 | Science & Technology ; Technology |
| 类目[WOS] | Computer Science, Interdisciplinary Applications ; Engineering, Industrial |
| 研究领域[WOS] | Computer Science ; Engineering |
| 关键词[WOS] | NEURAL-NETWORK ; MULTICLASS CLASSIFICATION ; BLIND-EQUALIZATION ; CRITERION ; SELECTION ; MODEL ; IDENTIFICATION ; ALGORITHM ; VARIANCE ; FEATURES |
| 收录类别 | SCI |
| 语种 | 英语 |
| WOS记录号 | WOS:000315309300026 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/3471]  |
| 专题 | 自动化研究所_复杂系统管理与控制国家重点实验室_先进机器人控制团队 |
| 作者单位 | 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 |
| 推荐引用方式 GB/T 7714 | Xie, Liangjun,Gu, Nong,Li, Dalong,et al. Concurrent control chart patterns recognition with singular spectrum analysis and support vector machine[J]. COMPUTERS & INDUSTRIAL ENGINEERING,2013,64(1):280-289. |
| APA | Xie, Liangjun,Gu, Nong,Li, Dalong,Cao, Zhiqiang,Tan, Min,&Nahavandi, Saeid.(2013).Concurrent control chart patterns recognition with singular spectrum analysis and support vector machine.COMPUTERS & INDUSTRIAL ENGINEERING,64(1),280-289. |
| MLA | Xie, Liangjun,et al."Concurrent control chart patterns recognition with singular spectrum analysis and support vector machine".COMPUTERS & INDUSTRIAL ENGINEERING 64.1(2013):280-289. |
入库方式： OAI收割
来源：自动化研究所
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