NDLI: Progressive PCA modeling for enhanced fault diagnosis in a batch process

Content Provider	IEEE Xplore Digital Library
Author	Jeong Jin Hong Jie Zhang
Copyright Year	2010
Description	Author affiliation: Centre for Process Analytics and Control Technology (CPACT), School of Chemical Engineering and Advanced Materials, Newcastle University, Newcastle upon Tyne, NE1 7RU, UK (Jeong Jin Hong; Jie Zhang)
Abstract	The conventional process monitoring procedure using principal component analysis (PCA) can show which variable is highly related with the fault by looking at the contribution plots for the monitoring statistics, SPE (squared prediction errors) and $T^{2}.$ However, this procedure is not able to determine if the variable is just affected by the fault or the variable is the cause of the fault. In addition, it is not able to show fault propagation through the process variables during the process time. The proposed progressive PCA modeling procedure can identify all variables related to the fault through progressively removing the identified variables and PCA modeling with the remaining variables. It can also provide timing information of when abnormal behaviors are observed for the identified variables by using time series SPE plots with control limits estimated by weighted chi-squared distribution. Based on the timing information, it is able to build a flow chart showing the fault propagation paths. The proposed method is demonstrated on a benchmark fed-batch penicillin process simulator.
Starting Page	713
Ending Page	718
File Size	1504651
Page Count	6
File Format	PDF
ISBN	9781424474530
e-ISBN	9788993215021
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-10-27
Publisher Place	Korea (South)
Access Restriction	Subscribed
Rights Holder	IEEE International Conference on Control & Automation
Subject Keyword	Multivariate Statistical Process Control Batch Processes Progressive PCA Time series analysis Fault Diagnosis Data models Timing Feeds Monitoring Substrates Principal component analysis
Content Type	Text
Resource Type	Article

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