摘要
针对工业过程中存在的动态特性和多模态特性问题,提出一种动态加权差分主成分分析法(dynamic weighted differential principal component analysis,DWDPCA)。首先通过设置合理的时间窗描述系统的时序特性;其次对时间窗内的样本寻找第一近邻和第一近邻的近邻集,使用加权差分法对数据进行处理,解决数据中心漂移问题;最后利用处理好的数据建立主成分分析(principal component analysis,PCA)模型进行故障检测。该方法可解决数据动态、中心漂移问题。使用该方法对数值例子和TE(tennessee eastman)过程进行故障检测验证所提出方法的有效性。
In response to the dynamic and multimodal characteristics problems in industrial processes,a dynamic weighted difference principal component analysis approach(DWDPCA)was proposed.Firstly,the timing characteristics of the system were described by setting a reasonable time window.Then,the first nearest neighbor and the nearest neighbor set of the first nearest neighbor were searched for the samples within the time window.The weighted difference method was used to process the data to solve the problem of data center drift problem.Finally,a principal component analysis(PCA)model was built for fault detection using the processed data.The data dynamic and center drift issue can be resolved with this method.By employing the method for numerical examples and the Tennessee-Eastman(TE)process for fault detection,the effectiveness of the proposed method was confirmed.
作者
张谦谦
王文标
郝友维
ZHANG Qian-qian;WANG Wen-biao;HAO You-wei(College of Marine Electrical Engineering,Dalian Maritime University,Dalian 116026,China)
出处
《科学技术与工程》
北大核心
2023年第36期15522-15529,共8页
Science Technology and Engineering
基金
国家自然科学基金(52071047,62073054)。
关键词
动态
多模态
主成分分析(PCA)
故障检测
dynamic
multimodal
principal component analysis(PCA)
fault detection
