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Fault Analysis Of Process System Using Multi Block Principal Component Analysis

*S. B. Sasongko  -  , Indonesia
K. A. Ibrahim  -  , Indonesia
A. Ahmad  -  , Indonesia
Open Access Copyright (c) 2017 REAKTOR under

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This research looks into the issues of the quality improvement based on process control instead of product control using multivariate statistical process contro. A deterministic model of a proton exchange membrane fuel cell (PEM-FC) power plant was used as a case study to represent a multi variable or mukti equipment system. A three-step approach is proposed which  can be classified into fault detection, fault isolation, and faulr diagnosis. The fault detection and the isolation utilize the multivariate analysis and yhe contro chart method , which uses the series multi-block principal component analysis  of extended of PCA method. The series block principal component abalysis is solved using the non linear iteration partial least squares (NIPALS) algorithm. The SB-PCA can advangeouly incorporate the control chart, namely, T2 Hotelling control chart. In the fault diagnosis chart, the normalized variable method was successfully applied in this study with promising results. As a conclution, the result of this study demonstrated the potentials of multivariate statistical process control in solving fault detection and diagnosis problem for multi variable and multi equipment system.

Keywords : statistical process control, principal component, fault analysis

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Keywords: statistical process control, principal component, fault analysis

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