Variable reconstruction and sensor fault identification using canonical variate analysis |
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Authors: | Changkyu Lee Sang Wook Choi In-Beum Lee |
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Affiliation: | aDepartment of Chemical Engineering, Pohang University of Science and Technology, San 31 Hyoja Dong, Nam-gu, Pohang, Kyungbuk 790-784, Republic of Korea;bCentre for Process Analytics and Control Technology, School of Chemical Engineering and Advanced Materials, University of Newcastle, Newcastle upon Tyne NE1 7RU, UK |
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Abstract: | The detection and identification of faults in dynamic continuous processes has received considerable recent attention from researchers in academia and industry. In this paper, a canonical variate analysis (CVA)-based sensor fault detection and identification method via variable reconstruction is described. Several previous studies have shown that CVA-based monitoring techniques can effectively detect faults in dynamic processes. Here we define two monitoring indices in the state and noise spaces for fault detection and, for sensor fault identification, we propose three variable reconstruction algorithms based on the proposed monitoring indices. The variable reconstruction algorithms are based on the concepts of conditional mean replacement and object function minimization. The proposed approach is applied to a simulated continuous stirred tank reactor and the results are compared to those obtained using the traditional dynamic monitoring technique, dynamic principal component analysis (PCA). The results indicate that the proposed methodology is quite effective for monitoring dynamic processes in terms of sensor fault detection and identification. |
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Keywords: | Fault detection Sensor fault identification Variable reconstruction Canonical variate analysis |
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