Fault Detection and Diagnosis for Gas Turbines Based on a Kernelized Information Entropy Model
Joint Authors
Wang, Weiying
Xu, Zhiqiang
Tang, Rui
Wu, Wei
Li, Shu-ying
Source
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-08-28
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Medicine
Information Technology and Computer Science
Abstract EN
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.
American Psychological Association (APA)
Wang, Weiying& Xu, Zhiqiang& Tang, Rui& Li, Shu-ying& Wu, Wei. 2014. Fault Detection and Diagnosis for Gas Turbines Based on a Kernelized Information Entropy Model. The Scientific World Journal،Vol. 2014, no. 2014, pp.1-13.
https://search.emarefa.net/detail/BIM-1050352
Modern Language Association (MLA)
Wang, Weiying…[et al.]. Fault Detection and Diagnosis for Gas Turbines Based on a Kernelized Information Entropy Model. The Scientific World Journal No. 2014 (2014), pp.1-13.
https://search.emarefa.net/detail/BIM-1050352
American Medical Association (AMA)
Wang, Weiying& Xu, Zhiqiang& Tang, Rui& Li, Shu-ying& Wu, Wei. Fault Detection and Diagnosis for Gas Turbines Based on a Kernelized Information Entropy Model. The Scientific World Journal. 2014. Vol. 2014, no. 2014, pp.1-13.
https://search.emarefa.net/detail/BIM-1050352
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1050352