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International Journal of Rotating Machinery
Volume 2017 (2017), Article ID 5435794, 9 pages
Research Article

Detection of Emerging Faults on Industrial Gas Turbines Using Extended Gaussian Mixture Models

1School of Engineering, University of Lincoln, Lincoln LN6 7TS, UK
2Siemens Industrial Turbomachinery Ltd., Lincoln LN5 7FD, UK

Correspondence should be addressed to Yu Zhang

Received 9 December 2016; Revised 24 March 2017; Accepted 23 April 2017; Published 21 May 2017

Academic Editor: P. Stephan Heyns

Copyright © 2017 Yu Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


This paper extends traditional Gaussian mixture model (GMM) techniques to provide recognition of operational states and detection of emerging faults for industrial systems. A variational Bayesian method allows a GMM to cluster with its mixture components to facilitate the extraction of steady-state operational behaviour; this is recognised as being a primary factor in reducing the susceptibility of alternative prognostic/diagnostic techniques, which would initiate false-alarms resulting from control set-point and load changes. Furthermore, a GMM with an outlier component is discussed and applied for direct novelty/fault detection. An advantage of the variational Bayesian method over traditional predefined thresholds is the extraction of steady-state data during both full- and part-load cases, and a primary advantage of the GMM with an outlier component is its applicability for novelty detection when there is a lack of prior knowledge of fault patterns. Results obtained from the real-time measurements on the operational industrial gas turbines have shown that the proposed technique provides integrated preprocessing, benchmarking, and novelty/fault detection methodology.