Journal
INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH
Volume 52, Issue 4, Pages 1635-1644Publisher
AMER CHEMICAL SOC
DOI: 10.1021/ie3017016
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Funding
- National Natural Science Foundation of China [21176073]
- Ministry of Education of China [20090074110005]
- Program for New Century Excellent Talents in University [NCET-09-0346]
- Shu Guang project [09SG29]
- 973 project [2012CB721006]
- Fundamental Research Funds for the Central Universities
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Sensitive principal component analysis (SPCA) is proposed to improve the principal component analysis (PCA) based chemical process monitoring performance, by solving the information loss problem and reducing nondetection rates of the T-2 statistic. Generally, principal components (PCs) selection in the PCA-based process monitoring is subjective, which can lead to information loss and poor monitoring performance. The SPCA method is to subsequently build a conventional PCA model based on normal samples, index PCs which reflect the dominant variation of abnormal observations, and use these sensitive PCs (SPCs) to monitor the process. Moreover, a novel fault diagnosis approach based on SPCA is also proposed due to SPCs' ability to represent the main characteristic of the fault. The case studies on the Tennessee Eastman process demonstrate the effect of SPCA on online monitoring, showing its performance is significantly better than that of the classical PCA methods.
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