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Research Papers: Gas Turbines: Aircraft Engine

Early Fault Detection of Hot Components in Gas Turbines

[+] Author and Article Information
Liu Jinfu

School of Energy Science and Engineering,
Harbin Institute of Technology,
No. 92, West Dazhi Street,
Harbin 150001, China
e-mail: jinfuliu@hit.edu.cn

Liu Jiao

School of Energy Science and Engineering,
Harbin Institute of Technology,
No. 92, West Dazhi Street,
Harbin 150001, China
e-mail: liujiaohit@outlook.com

Wan Jie

School of Energy Science and Engineering,
Harbin Institute of Technology,
No. 92, West Dazhi Street,
Harbin 150001, China
e-mail: whhitwanjie08@126.com

Wang Zhongqi

School of Energy Science and Engineering,
Harbin Institute of Technology,
No. 92, West Dazhi Street,
Harbin 150001, China
e-mail: wangzhognqi@hit.edu.cn

Yu Daren

School of Energy Science and Engineering,
Harbin Institute of Technology,
No. 92, West Dazhi Street,
Harbin 150001, China
e-mail: yudaren@hit.edu.cn

1Corresponding author.

Contributed by the Aircraft Engine Committee of ASME for publication in the JOURNAL OF ENGINEERING FOR GAS TURBINES AND POWER. Manuscript received May 31, 2016; final manuscript received June 17, 2016; published online September 13, 2016. Editor: David Wisler.

J. Eng. Gas Turbines Power 139(2), 021201 (Sep 13, 2016) (12 pages) Paper No: GTP-16-1198; doi: 10.1115/1.4034153 History: Received May 31, 2016; Revised June 17, 2016

The working environment of hot components is the most adverse of all gas turbine components. Malfunction of hot components is often followed by catastrophic consequences. Early fault detection plays a significant role in detecting performance deterioration immediately and reducing unscheduled maintenance. In this paper, an early fault detection method is introduced to detect early fault symptoms of hot components in gas turbines. The exhaust gas temperature (EGT) is usually used to monitor the performance of the hot components. The EGT is measured by several thermocouples distributed equally at the outlet of the gas turbine. EGT profile is symmetrical when the unit is in normal operation. And the faults of hot components lead to large temperature differences between different thermocouple readings. However, interferences can potentially affect temperature differences, and sometimes, especially in the early stages of the fault, its influence can be even higher than that of the faults. To improve the detection sensitivity, the influence of interferences must be eliminated. The two main interferences investigated in this study are associated with the operating and ambient conditions, and the structure deviation of different combustion chambers caused by processing and installation errors. Based on the basic principles of gas turbines and Fisher discriminant analysis (FDA), a new detection indicator is presented that characterizes the intrinsic structure information of the hot components. Using this new indicator, the interferences involving the certainty and the uncertainty are suppressed and the sensitivity of early fault detection in gas turbine hot components is improved. The robustness and the sensitivity of the proposed method are verified by actual data from a Taurus 70 gas turbine produced by Solar Turbines.

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References

Figures

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Fig. 1

Bayesian decision diagram

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Fig. 2

Typical gas turbine configuration

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Fig. 3

Combustion chambers and thermocouples distribution

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Fig. 4

Comparison between normal and abnormal operation

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Fig. 6

EGT profiles under different operating conditions

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Fig. 7

Ambient temperature

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Fig. 8

EGT profiles under different ambient conditions

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Fig. 9

Exhaust thermocouple readings

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Fig. 10

EGT profiles in the different structure deviation of different combustion chambers

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Fig. 11

Schematic diagram of FDA

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Fig. 12

Linear relationship between T4,avg and T4,1

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Fig. 13

Normal operation detection schematic diagram

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Fig. 14

Abnormal operation detection schematic diagram

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Fig. 16

Ambient temperature in case 1

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Fig. 18

Vector α in case 1

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Fig. 21

Vector α in case 2

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Fig. 24

Vector α in case 3

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Fig. 27

Vector α in case 4

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Fig. 28

Coefficient α8 in case 4

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Fig. 31

Gas and fuel position feedback in case 5

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Fig. 32

Vector α before and after the first fuel changeover in case 5

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Fig. 33

Vector α before the first fuel changeover and after the second fuel changeover in case 5

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Fig. 34

Constructed fault data

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Fig. 36

Temperature differences indicator

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Fig. 37

α7 of the proposed method

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