0
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.

Copyright © 2017 by ASME
Your Session has timed out. Please sign back in to continue.

References

Volponi, A. J. , 2014, “ Gas Turbine Engine Health Management: Past, Present, and Future Trends,” ASME J. Eng. Gas Turbines Power, 136(5), p. 051201. [CrossRef]
Marinai, L. , Probert, D. , and Singh, R. , 2004, “ Prospects for Aero Gas-Turbine Diagnostics: A Review,” Appl. Energy, 79(1), pp. 109–126. [CrossRef]
Urban, L. A. , 1973, “ Gas Path Analysis Applied to Turbine Engine Condition Monitoring,” J. Aircr., 10(7), pp. 400–406. [CrossRef]
Pu, X. , Liu, S. , Jiang, H. , and Yu, D. , 2013, “ Sparse Bayesian Learning for Gas Path Diagnostics,” ASME J. Eng. Gas Turbines Power, 135(7), p. 071601. [CrossRef]
Doel, D. L. , 1994, “ TEMPER—A Gas-Path Analysis Tool for Commercial Jet Engines,” ASME J. Eng. Gas Turbines Power, 116(1), pp. 82–89. [CrossRef]
Gulati, A. , Zedda, M. , and Singh, R. , 2000, “ Gas Turbine Engine and Sensor Multiple Operating Point Analysis Using Optimization Techniques,” AIAA Paper No. 2000-3716.
Kamboukos, P. , and Mathioudakis, K. , 2005, “ Comparison of Linear and Nonlinear Gas Turbine Performance Diagnostics,” ASME J. Eng. Gas Turbines Power, 127(1), pp. 49–56. [CrossRef]
Stamatis, A. , Mathioudakis, K. , and Papailiou, K. D. , 1990, “ Adaptive Simulation of Gas Turbine Performance,” ASME J. Eng. Gas Turbines Power, 112(2), pp. 168–175. [CrossRef]
Naderi, E. , Meskin, N. , and Khorasani, K. , 2012, “ Nonlinear Fault Diagnosis of Jet Engines by Using a Multiple Model-Based Approach,” ASME J. Eng. Gas Turbines Power, 134(1), p. 11602. [CrossRef]
Kobayashi, T. , and Simon, D. L. , 2005, “ Evaluation of an Enhanced Bank of Kalman Filters for In-Flight Aircraft Engine Sensor Fault Diagnostics,” ASME J. Eng. Gas Turbines Power, 127(3), pp. 497–504. [CrossRef]
Loboda, I. , Feldshteyn, Y. , and Ponomaryov, V. , 2012, “ Neural Networks for Gas Turbine Fault Identification: Multilayer Perceptron or Radial Basis Network?,” Int. J. Turbo Jet-Engines, 29(1), pp. 37–48. http://www.degruyter.com/view/j/tjj.2012.29.issue-1/tjj-2012-0005/tjj-2012-0005.xml
Bettocchi, R. , Pinelli, M. , Spina, P. R. , and Venturini, M. , 2007, “ Artificial Intelligence for the Diagnostics of Gas Turbines—Part I: Neural Network Approach,” ASME J. Eng. Gas Turbines Power, 129(3), pp. 711–719. [CrossRef]
Romessis, C. , and Mathioudakis, K. , 2006, “ Bayesian Network Approach for Gas Path Fault Diagnosis,” ASME J. Eng. Gas Turbines Power, 128(1), pp. 64–72. [CrossRef]
Lee, Y. K. , Mavris, D. N. , Volovoi, V. V. , Yuan, M. , and Fisher, T. , 2010, “ A Fault Diagnosis Method for Industrial Gas Turbines Using Bayesian Data Analysis,” ASME J. Eng. Gas Turbines Power, 132(4), p. 41602. [CrossRef]
Li, Y. G. , Ghafir, M. F. A. , Wang, L. , Singh, R. , Huang, K. , and Feng, X. , 2011, “ Nonlinear Multiple Points Gas Turbine Off-Design Performance Adaptation Using a Genetic Algorithm,” ASME J. Eng. Gas Turbines Power, 133(7), p. 71701. [CrossRef]
Ganguli, R. , 2003, “ Application of Fuzzy Logic for Fault Isolation of Jet Engines,” ASME J. Eng. Gas Turbines Power, 125(3), pp. 617–623. [CrossRef]
Shabanian, M. , and Montazeri, M. , 2011, “ A Neuro-Fuzzy Online Fault Detection and Diagnosis Algorithm for Nonlinear and Dynamic Systems,” Int. J. Control Autom. Syst., 9(4), pp. 665–670. [CrossRef]
Martis, D. , 2007, “ Fuzzy Logic Estimation Applied to Newton Methods for Gas Turbines,” ASME J. Eng. Gas Turbines Power, 129(1), pp. 88–96. [CrossRef]
Doel, D. L. , 1990, “ The Role for Expert Systems in Commercial Gas Turbine Engine Monitoring,” ASME Paper No. 90-GT-374.
Johnson, D. , Gilbert, K. E. , and Buckley, L. P. , 1983, “ The SPEEDTRONIC Mark IV Control, a Distributed Fault Tolerant Gas Turbine Control System,” ASME Paper No. 83-GT-106.
Liu, J. , 2014, “ The Research for Exhaust Temperature Anomaly Detection in Gas Turbine,” M.S. thesis, Harbin Institute of Technology, Harbin, China.
Siemens Power Generation, 1999, “ TELEPERM XP The Process Control System for Economical Power Plant Control System Overview,” Siemens Power Generation, Berlin.
Gulen, S. C. , Griffin, P. R. , and Paolucci, S. , 2002, “ Real-Time On-Line Performance Diagnostics of Heavy-Duty Industrial Gas Turbines,” ASME J. Eng. Gas Turbines Power, 124(4), pp. 910–921. [CrossRef]
Song, Y. X. , Zhang, K. X. , and Shi, Y. S. , 2009, “ Research on Aeroengine Performance Parameters Forecast Based on Multiple Linear Regression Forecasting Method,” J. Aerosp. Power, 24(2), pp. 427–431. http://en.cnki.com.cn/Article_en/CJFDTOTAL-HKDI200902029.htm
Yılmaz, İ. , 2009, “ Evaluation of the Relationship Between Exhaust Gas Temperature and Operational Parameters in CFM56-7B Engines,” Proc. Inst. Mech. Eng., Part G, 223(4), pp. 433–440. [CrossRef]
Korczewski, Z. , 2011, “ Exhaust Gas Temperature Measurements in Diagnostic Examination of Naval Gas Turbine Engines,” Pol. Marit. Res., 18(2), pp. 37–43. http://www.degruyter.com/view/j/pomr.2011.18.issue-2/v10012-011-0010-2/v10012-011-0010-2.xml
Tarassenko, L. , Nairac, A. , Townsend, N. , Buxton, I. , and Cowley, Z. , 2000, “ Novelty Detection for the Identification of Abnormalities,” Int. J. Syst. Sci., 31(11), pp. 1427–1439. [CrossRef]
Medina, P. , Saez, D. , and Roman, R. , 2008, “ On Line Fault Detection and Isolation in Gas Turbine Combustion Chambers,” ASME Paper No. GT2008-51316.
Basseville, M. , Benveniste, A. , Mathis, G. , and Zhang, Q. , 1994, “ Monitoring the Combustion Set of a Gas Turbine,” IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes—SAFEPROCESS, Vol. 94, pp. 397–402. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.9.2107&rep=rep1&type=pdf
Webb, A. R. , and Copsey, K. D. , 1990, Introduction to Statistical Pattern Recognition, Wiley, New York.
Duda, R. O. , Hart, P. E. , Stork, D. G. , Duda, C. R. O. , Hart, P. E. , and Stork, D. G. , 2001, Pattern Classification, 2nd ed., Wiley, New York.

Figures

Grahic Jump Location
Fig. 1

Bayesian decision diagram

Grahic Jump Location
Fig. 2

Typical gas turbine configuration

Grahic Jump Location
Fig. 3

Combustion chambers and thermocouples distribution

Grahic Jump Location
Fig. 4

Comparison between normal and abnormal operation

Grahic Jump Location
Fig. 6

EGT profiles under different operating conditions

Grahic Jump Location
Fig. 7

Ambient temperature

Grahic Jump Location
Fig. 8

EGT profiles under different ambient conditions

Grahic Jump Location
Fig. 11

Schematic diagram of FDA

Grahic Jump Location
Fig. 10

EGT profiles in the different structure deviation of different combustion chambers

Grahic Jump Location
Fig. 9

Exhaust thermocouple readings

Grahic Jump Location
Fig. 12

Linear relationship between T4,avg and T4,1

Grahic Jump Location
Fig. 13

Normal operation detection schematic diagram

Grahic Jump Location
Fig. 14

Abnormal operation detection schematic diagram

Grahic Jump Location
Fig. 16

Ambient temperature in case 1

Grahic Jump Location
Fig. 18

Vector α in case 1

Grahic Jump Location
Fig. 21

Vector α in case 2

Grahic Jump Location
Fig. 24

Vector α in case 3

Grahic Jump Location
Fig. 27

Vector α in case 4

Grahic Jump Location
Fig. 28

Coefficient α8 in case 4

Grahic Jump Location
Fig. 34

Constructed fault data

Grahic Jump Location
Fig. 36

Temperature differences indicator

Grahic Jump Location
Fig. 37

α7 of the proposed method

Grahic Jump Location
Fig. 31

Gas and fuel position feedback in case 5

Grahic Jump Location
Fig. 32

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

Grahic Jump Location
Fig. 33

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

Tables

Errata

Discussions

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In