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Research Papers: Gas Turbines: Turbomachinery

A New Gas Path Fault Diagnostic Method of Gas Turbine Based on Support Vector Machine

[+] Author and Article Information
Dengji Zhou

Gas Turbine Research Institute,
Shanghai Jiao Tong University,
800 Dongchuan Road,
Minhang District,
Shanghai 200240, China
e-mail: zhoudj@sjtu.edu.cn

Huisheng Zhang

Gas Turbine Research Institute,
Shanghai Jiao Tong University,
800 Dongchuan Road,
Minhang District,
Shanghai 200240, China
e-mail: zhslm@sjtu.edu.cn

Shilie Weng

Gas Turbine Research Institute,
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 137(10), 102605 (Oct 01, 2015) (6 pages) Paper No: GTP-14-1687; doi: 10.1115/1.4030277 History: Received December 30, 2014; Revised March 04, 2015; Online May 06, 2015

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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References

Figures

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Fig. 1

Gas turbine gas path fault diagnostic theory

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Fig. 2

Application process of SVM for gas path diagnosis

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Fig. 3

The traditional SVM model for gas path diagnosis

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Fig. 4

The novel SVM model for gas path diagnosis

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Fig. 5

Effect of sample size and kernel function on SVM diagnostic accuracy

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Fig. 6

Effect of monitoring parameters number on SVM diagnostic accuracy (sample size = 400)

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Fig. 7

Effect of monitoring parameters number on SVM diagnostic accuracy (sample size = 144)

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Fig. 8

Diagnostic accuracy comparison of SVM and neural networks

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