Quality-related fault detection based on weighted mutual information principal component analysis

doi:10.11949/j.issn.0438-1157.20171009

Abstract

Abstract:

Quality-related fault detection has been a new research hotspot in recent years. It aims to the higher fault detection rate for quality-related faults and the lower fault detection rate for quality-unrelated faults. The traditional principal component analysis (PCA) will alarm all faults and can't satisfy the above requirements, which will cause lots of downtime and seriously affect the normal production. The quality variables usually are not easy to measure online in actual industrial production. So this paper proposed the weighted mutual information principal component analysis (WMIPCA) to solve these problems. Firstly, the supervision relationship between process variables and quality variables is established via mutual information and Bayesian Inference. Then a set of process variables that contain the largest amount of quality variable information is selected and the PCA is modeled on them. After that, the principal components containing more information on the quality variables are selected and used to establish the statistics and monitor the process. Finally, the feasibility and effectiveness of the WMIPCA are verified by experiments.

Key words: process systems, principal component analysis, fault detection, quality-related, weighted mutual information

摘要：

质量相关的故障检测已成为近几年研究热点，它的目标是在过程监测中，对质量相关的故障检测率更高，对质量无关的故障少报警或不报警。传统主元分析算法的故障检测会对所有故障均报警，不能达到上述要求。另外，在实际工业生产中，质量变量通常难以实时获得，需要后续分析或延时得到。为此，提出一种融合贝叶斯推断与互信息的加权互信息主元分析算法。首先利用贝叶斯推断的加权方法将度量过程变量和质量变量之间相关关系的互信息进行融合，选出包含质量变量信息量最大的一组过程变量。然后对过程变量利用主元分析（principal component analysis，PCA）进行统计建模，再次根据加权互信息选出包含质量变量信息量最大的主元，建立统计量进行故障检测。最后，通过实验验证该方法的可行性和有效性。

关键词: 过程系统, 主元分析, 故障检测, 质量相关, 加权互信息

CLC Number:

TP277

ZHAO Shuai, SONG Bing, SHI Hongbo. Quality-related fault detection based on weighted mutual information principal component analysis[J]. CIESC Journal, 2018, 69(3): 962-973.

赵帅, 宋冰, 侍洪波. 基于加权互信息主元分析算法的质量相关故障检测[J]. 化工学报, 2018, 69(3): 962-973.

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