基于近邻指标累积和的过程微小故障检测

doi:10.16182/j.issn1004731x.joss.19-0663

摘要/Abstract

摘要： 针对工业过程微小故障以及工业过程数据普遍存在的非线性问题,提出基于近邻指标累积和(Cumulative Sum of Neighbor Statistic,CUSUM-NS)的故障检测方法。使用互信息主元分析(Mutual Information Principal Component Analysis,MIPCA)对训练数据降维,提取互信息主元构成新样本空间,对降维后的新样本空间计算k近邻(k-Nearest-Neighbor,kNN)距离平方和指标,充分提取过程数据的非线性特征。利用累积和(CUSUM)方法对近邻距离平方和进行累积,捕捉过程数据的微小变化。通过TE(Tennessee Eastman)过程进行仿真,结果验证了所提方法有效性。

关键词: 互信息主元分析, k近邻, 累积和, 微小故障

Abstract: Aiming at the small faults and the common data non-linear problems of industrial process, a fault detection method based oncumulative sum of neighbor statistic (CUSUM-NS) is proposed. Mutual information principal component analysis (MIPCA) is used to reduce the dimension of training data, and the principal components based on mutual information are extracted to construct a new sample space. For the new sample space after dimensionality reduction, the nonlinear features of the process data can be fully extracted through the distance square sum statistics of k nearest neighbors. Cumulative summation(CUSUM) method is used to accumulate the sum of squares of neighboring distances to capture the small changes in process data. The simulation is conducted through the Tennessee Eastman (TE) process, and the results verify the effectiveness of the proposed method.

Key words: PCA based on mutual information, k-Nearest Neighbor, cumulative sum, small faults

中图分类号:

TP277

郭小萍, 高嘉俊, 郭建斌, 李元. 基于近邻指标累积和的过程微小故障检测[J]. 系统仿真学报, 2021, 33(4): 792-800.

Guo Xiaoping, Gao Jiajun, Guo Jianbin, Li Yuan. Small Fault Detection Based on Cumulative Sum of Neighbor Statistic[J]. Journal of System Simulation, 2021, 33(4): 792-800.

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