As a crucial section of gas turbine maintenance decision-making process, to date, gas path fault diagnostic has gained a lot of attention. However, model-based diagnostic methods, like nonlinear gas path analysis (GPA) and genetic algorithms, need an accurate gas turbine model, and diagnostic methods without gas turbine model, like expert system, need a knowledge database. Both are difficult to gain. Thus, data-driven approach for gas path diagnosis, like artificial neural network, is increasingly attractive. Support vector machine (SVM), a novel computational learning method, seems to be a good choice for data-driven gas path fault diagnosis of gas turbine. In this paper, SVM is employed to diagnose a deteriorated gas turbine. The effect of sample number, kernel function, and monitoring parameters on diagnostic accuracy are studied, respectively. Additionally, the diagnostic result of SVM is compared to the result of artificial neural networks. The comparing result confirms that SVM has an obvious advantage over artificial neural networks method based on a small sample of data and can be employed to gas path fault diagnosis of gas turbine. In addition, SVM with radial basis kernel function is the best choice for gas turbine gas path fault diagnosis based on small sample.
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October 2015
Research-Article
A New Gas Path Fault Diagnostic Method of Gas Turbine Based on Support Vector Machine
Dengji Zhou,
Dengji Zhou
Gas Turbine Research Institute,
e-mail: zhoudj@sjtu.edu.cn
Shanghai Jiao Tong University
,800 Dongchuan Road
,Minhang District
,Shanghai 200240
, China
e-mail: zhoudj@sjtu.edu.cn
Search for other works by this author on:
Huisheng Zhang,
Huisheng Zhang
Gas Turbine Research Institute,
e-mail: zhslm@sjtu.edu.cn
Shanghai Jiao Tong University
,800 Dongchuan Road
,Minhang District
,Shanghai 200240
, China
e-mail: zhslm@sjtu.edu.cn
Search for other works by this author on:
Shilie Weng
Shilie Weng
Gas Turbine Research Institute,
e-mail: slweng@sjtu.edu.cn
Shanghai Jiao Tong University
,800 Dongchuan Road
,Minhang District
,Shanghai 200240
, China
e-mail: slweng@sjtu.edu.cn
Search for other works by this author on:
Dengji Zhou
Gas Turbine Research Institute,
e-mail: zhoudj@sjtu.edu.cn
Shanghai Jiao Tong University
,800 Dongchuan Road
,Minhang District
,Shanghai 200240
, China
e-mail: zhoudj@sjtu.edu.cn
Huisheng Zhang
Gas Turbine Research Institute,
e-mail: zhslm@sjtu.edu.cn
Shanghai Jiao Tong University
,800 Dongchuan Road
,Minhang District
,Shanghai 200240
, China
e-mail: zhslm@sjtu.edu.cn
Shilie Weng
Gas Turbine Research Institute,
e-mail: slweng@sjtu.edu.cn
Shanghai Jiao Tong University
,800 Dongchuan Road
,Minhang District
,Shanghai 200240
, China
e-mail: slweng@sjtu.edu.cn
Contributed by the Turbomachinery Committee of ASME for publication in the JOURNAL OF ENGINEERING FOR GAS TURBINES AND POWER. Manuscript received December 30, 2014; final manuscript received March 4, 2015; published online May 6, 2015. Editor: David Wisler.
J. Eng. Gas Turbines Power. Oct 2015, 137(10): 102605 (6 pages)
Published Online: October 1, 2015
Article history
Received:
December 30, 2014
Revision Received:
March 4, 2015
Online:
May 6, 2015
Citation
Zhou, D., Zhang, H., and Weng, S. (October 1, 2015). "A New Gas Path Fault Diagnostic Method of Gas Turbine Based on Support Vector Machine." ASME. J. Eng. Gas Turbines Power. October 2015; 137(10): 102605. https://doi.org/10.1115/1.4030277
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