Fault Detection for Gas Turbine Hot Components Based on a Convolutional Neural Network

Jiao Liu, Jinfu Liu, Daren Yu, Myeongsu Kang, Weizhong Yan, Zhongqi Wang and Michael G. Pecht
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Jiao Liu: School of Energy Science and Engineering, Harbin Institute of Technology, Harbin 150001, China
Jinfu Liu: School of Energy Science and Engineering, Harbin Institute of Technology, Harbin 150001, China
Daren Yu: School of Energy Science and Engineering, Harbin Institute of Technology, Harbin 150001, China
Myeongsu Kang: Center for Advanced Life Cycle Engineering (CALCE), University of Maryland, College Park, MD 20742, USA
Weizhong Yan: Machine Learning Lab, GE Global Research Center, Niskayuna, NY 12309, USA
Zhongqi Wang: School of Energy Science and Engineering, Harbin Institute of Technology, Harbin 150001, China
Michael G. Pecht: Center for Advanced Life Cycle Engineering (CALCE), University of Maryland, College Park, MD 20742, USA

Energies, 2018, vol. 11, issue 8, 1-18

Abstract: Gas turbine hot component failures often cause catastrophic consequences. Fault detection can improve the availability and economy of hot components. The exhaust gas temperature (EGT) profile is usually used to monitor the performance of the hot components. The EGT profile is uniform when the hot component is healthy, whereas hot component faults lead to large temperature differences between different EGT values. The EGT profile swirl under different operating and ambient conditions also cause temperature differences. Therefore, the influence of EGT profile swirl on EGT values must be eliminated. To improve the detection sensitivity, this paper develops a fault detection method for hot components based on a convolutional neural network (CNN). This paper demonstrates that a CNN can extract the information between adjacent EGT values and consider the impact of the EGT profile swirl. This paper reveals, in principle, that a CNN is a viable solution for dealing with fault detection for hot components. Based on the distribution characteristics of EGT thermocouples, the circular padding method is developed in the CNN. The sensitivity of the developed method is verified by real-world data. Moreover, the developed method is visualized in detail. The visualization results reveal that the CNN effectively considers the influence of the EGT profile swirl.

Keywords: gas turbine; hot component; fault detection; exhaust gas temperature (EGT); convolutional neural network (CNN) (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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