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The Scientific World Journal
Volume 2014 (2014), Article ID 617162, 13 pages
Research Article

Fault Detection and Diagnosis for Gas Turbines Based on a Kernelized Information Entropy Model

1College of Power and Energy Engineering, Harbin Engineering University, Harbin 150001, China
2Harbin Marine Boiler & Turbine Research Institute, Harbin 150036, China
3Harbin Institute of Technology, Harbin 150001, China

Received 28 April 2014; Accepted 19 June 2014; Published 28 August 2014

Academic Editor: Xibei Yang

Copyright © 2014 Weiying Wang 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.


Gas turbines are considered as one kind of the most important devices in power engineering and have been widely used in power generation, airplanes, and naval ships and also in oil drilling platforms. However, they are monitored without man on duty in the most cases. It is highly desirable to develop techniques and systems to remotely monitor their conditions and analyze their faults. In this work, we introduce a remote system for online condition monitoring and fault diagnosis of gas turbine on offshore oil well drilling platforms based on a kernelized information entropy model. Shannon information entropy is generalized for measuring the uniformity of exhaust temperatures, which reflect the overall states of the gas paths of gas turbine. In addition, we also extend the entropy to compute the information quantity of features in kernel spaces, which help to select the informative features for a certain recognition task. Finally, we introduce the information entropy based decision tree algorithm to extract rules from fault samples. The experiments on some real-world data show the effectiveness of the proposed algorithms.