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

Zhang, Yu and Bingham, Chris and Yang, Zhijing and Gallimore, Michael and Ling, Wing-Kuen (2012) Sensor fault detection for industrial gas turbine system by using principal component analysis based y-distance indexes. In: 8th IEEE, IET International Symposium on Communication Systems, Networks & Digital Signal Processing (CSNDSP), 18–20 July 2012, Poznan University of Technology, Poznan, Poland.

Full content URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumbe...

Documents
CSNDSP(Y).pdf

Request a copy
[img] PDF
CSNDSP(Y).pdf
Restricted to Repository staff only

951kB
Item Type:Conference or Workshop contribution (Presentation)
Item Status:Live Archive

Abstract

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.

Keywords:Industrial gas turbine, Principal component analysis, Sensor fault detection
Subjects:G Mathematical and Computer Sciences > G790 Artificial Intelligence not elsewhere classified
Divisions:College of Science > School of Engineering
ID Code:12545
Deposited On:20 Nov 2013 10:46

Repository Staff Only: item control page