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题名: Adaptive Fault Detection for Complex Dynamic Processes Based on JIT Updated Data Set
作者: Li JN(李金娜) ; Li Y(李元) ; Yu HB(于海斌) ; Xie YH(谢彦红) ; Zhang C(张成)
作者部门: 工业控制网络与系统研究室
关键词: PRINCIPAL COMPONENT ANALYSIS ; OBSERVER ; SYSTEMS ; DIAGNOSIS ; FUZZY ; IDENTIFICATION ; PCA
刊名: JOURNAL OF APPLIED MATHEMATICS
ISSN号: 1110-757X
出版日期: 2012
页码: 17 pp.
收录类别: SCI
产权排序: 1
摘要: A novel fault detection technique is proposed to explicitly account for the nonlinear, dynamic, and multimodal problems existed in the practical and complex dynamic processes. Just-in-time (JIT) detection method and k-nearest neighbor (KNN) rule-based statistical process control (SPC) approach are integrated to construct a flexible and adaptive detection scheme for the control process with nonlinear, dynamic, and multimodal cases. Mahalanobis distance, representing the correlation among samples, is used to simplify and update the raw data set, which is the first merit in this paper. Based on it, the control limit is computed in terms of both KNN rule and SPC method, such that we can identify whether the current data is normal or not by online approach. Noted that the control limit obtained changes with updating database such that an adaptive fault detection technique that can effectively eliminate the impact of data drift and shift on the performance of detection process is obtained, which is the second merit in this paper. The efficiency of the developed method is demonstrated by the numerical examples and an industrial case.
语种: 英语
WOS记录号: WOS:000310301500001
WOS标题词: Science & Technology ; Physical Sciences
类目[WOS]: Mathematics, Applied
关键词[WOS]: PRINCIPAL COMPONENT ANALYSIS ; NEAREST-NEIGHBOR RULE ; SYSTEM IDENTIFICATION ; DIAGNOSIS ; OBSERVER ; FUZZY ; PCA
研究领域[WOS]: Mathematics
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内容类型: 期刊论文
URI标识: http://ir.sia.cn/handle/173321/12528
Appears in Collections:工业控制网络与系统研究室_期刊论文

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Recommended Citation:
李金娜; 李元; 于海斌; 谢彦红; 张成.Adaptive Fault Detection for Complex Dynamic Processes Based on JIT Updated Data Set,JOURNAL OF APPLIED MATHEMATICS,2012,():17 pp.
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