Fault diagnosis of chemical process based on factor analysis and improved K-neighbor algorithm

被引：0
|
作者：
Jin, Manman [1 ]
Guo, Hongyue [2 ]
机构：
[1] Qingdao Univ Sci & Technol, Coll Chem Engn, Qingdao 266042, Peoples R China
[2] ZHONGKEHUALIAN New Mat Co Ltd, Qingdao 266042, Peoples R China
来源：
2017 29TH CHINESE CONTROL AND DECISION CONFERENCE (CCDC) | 2017年
关键词：
fault diagnosis; data driven; improved K-neighbor algorithm; factor analysis; NEAREST; CLASSIFICATION;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
It has been more and more difficult to just depend on the mechanism models to diagnose the faults of the increasingly complex chemical system. This paper proposes an improved K-neighbor algorithm based on data driven method for process fault diagnosis. For training data, the fault diagnosis accuracy rate is seen as the standard to optimize the value of K. The choice purpose of this improved algorithm is to eliminate the value of K's effect on consequence. In order to improve the fault diagnosis accuracy and reduce the calculation time, this paper integrates factor analysis and improved K-neighbor algorithm. Through TE process, this paper verifies the effectiveness of the proposed method. The fault diagnosis accuracy is increased from 78.7% to 86.7% and the calculation time is reduced 52%.
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页码：7347 / 7353
页数：7
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