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Sensor fault detection for industrial gas turbine system by using principal component analysis based y-distance indexes

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5 Author(s)
Zhang, Y. ; Sch. of Eng., Univ. of Lincoln, Lincoln, UK ; Bingham, C.M. ; Yang, Z. ; Gallimore, M.
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The paper presents a readily implementable and computationally efficient method for sensor fault detection based upon an extension to principal component analysis (PCA) and y-distance indexes. The proposed extension is applied to system data from a sub-15MW industrial gas turbine, with explanations of the eigenvalue/eigenvector problem and the definition of z-scores and principal component (PC) scores. The y-distance index is introduced to measure the differences between sensor reading datasets. It is shown through use of real-time operational data that in-operation sensor faults can be detected through use of the proposed y-distance indexes. The efficacy of the approach is demonstrated through experimental trials on Siemens industrial gas turbines.

Published in:

Communication Systems, Networks & Digital Signal Processing (CSNDSP), 2012 8th International Symposium on

Date of Conference:

18-20 July 2012