An adaptive data-driven fault detection method for monitoring dynamic process

被引：0
|
作者：
Chen, Zhiwen [1 ]
Peng, Tao [1 ]
Yang, Chunhua [1 ]
Li, Fanbiao [1 ]
He, Zhangming [2 ]
机构：
[1] Cent S Univ, Sch Informat Sci & Engn, Changsha, Hunan, Peoples R China
[2] Natl Univ Def Technol, Coll Liberal Arts & Sci, Changsha, Hunan, Peoples R China
来源：
IECON 2018 - 44TH ANNUAL CONFERENCE OF THE IEEE INDUSTRIAL ELECTRONICS SOCIETY | 2018年
基金：
中国国家自然科学基金;
关键词：
Adaptive dynamic process monitoring; Fault detection; Least squares; CANONICAL CORRELATION-ANALYSIS; DIAGNOSIS;
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
This paper presents an adaptive data-driven fault detection method for dynamic processes. In this method, the vector ARX model is used to model the dynamic process in a data-driven fashion. Then, the adaptive method is developed by means of the incremental and decremental algorithms. The performance and effectiveness of the proposed approach are demonstrated with a numerical case study and an experimental continuous stirred tank heater. The detection results show that the effectiveness of the proposed method.
引用
收藏
页码：5353 / 5358
页数：6
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