化工学报2017，Vol.68Issue(8)：3177-3182,6.DOI:10.11949/j.issn.0438-1157.20170281
变量加权型主元分析算法及其在故障检测中的应用
Variable weighted principal component analysis algorithm and its application in fault detection
摘要
Abstract
Traditional principal component analysis (PCA) algorithm, targeting to explore correlations among measured variables in training dataset, has been intensively investigated and applied to data-driven fault detection. However, all variables are considered equally important in modeling process of traditional PCA-based methods, the difference between variable correlations cannot be comprehensively described. A variable weighted PCA (VWPCA) algorithm was proposed and applied to fault detection. Weight calculations were performed on the training dataset so correlation differences among measured variables were fully reflected in the processed data and a distributed PCA-based fault detection model was constructed. When implemented in online fault detection, the Bayesian inference was used to combine multiple monitoring results into an ensemble of probability indices. VWPCA approach assigned different weights to different variables according to the correlation difference, thus PCA modeling took correlation difference into account and the models could completely describe characteristics of the training dataset. Finally, superiority of the proposed VWPCA method was validated by well-known TE process.关键词
主元分析/过程系统/过程控制/故障检测Key words
principal component analysis/process systems/process control/fault detection分类
信息技术与安全科学引用本文复制引用
蓝艇,童楚东,史旭华..变量加权型主元分析算法及其在故障检测中的应用[J].化工学报,2017,68(8):3177-3182,6.基金项目
国家自然科学基金项目(61503204) （61503204）
浙江省自然科学基金项目(Y16F030001) （Y16F030001）
宁波市自然科学基金项目(2016A610092). supported by the National Natural Science Foundation of China (61503204), the Natural Science Foundation of Zhejiang Province(Y16F030001) and the Natural Science Foundation of Ningbo (2016A610092). （2016A610092）
