Fault Detection Behavior and Performance Analysis of Principal Component Analysis Based Process Monitoring Methods
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- 作者：Haiqing Wang ; Zhihuan Song ; and Ping Li
- 刊名：Industrial & Engineering Chemistry Research
- 出版年：2002
- 出版时间：May 15, 2002
- 年：2002
- 卷：41
- 期：10
- 页码：2455 - 2464
- 全文大小：114K
- 年卷期：v.41,no.10(May 15, 2002)
- ISSN：1520-5045
文摘
Principal component analysis (PCA) is an effective approach to process monitoring, andsubstantial works in this field have been reported. However, the fault detection behavior andperformance of PCA are still equivocal and frequently lead to incorrect understanding of thedetection results. This issue is addressed in this paper from two directions simultaneously. First,the expectation formulas of T 2 and squared prediction error statistics are presented and theirrelations to the statistical parameters of process data are discussed. Based on these relationships,the process disturbances and faults can be differentiated, which makes further fault diagnosismore reliable. Second, detectability conditions of different faults both in the principal subspaceand in residual subspace are given. A new conception of critical fault magnitude was introducedto provide a definite description about the fault detection performance of PCA. The acquiredresults were illustrated and verified by monitoring a simulated double-effective evaporatorprocess.