Applied Fault Detection and Diagnosis for Industrial Gas Turbine Systems

Yu Zhang, Chris Bingham, Mike Garlick and Michael Gallimore. Applied Fault Detection and Diagnosis for Industrial Gas Turbine Systems. International Journal of Automation and Computing, vol. 14, no. 4, pp. 463-473, 2017 doi:  10.1007/s11633-016-0967-5
 Citation: Yu Zhang, Chris Bingham, Mike Garlick and Michael Gallimore. Applied Fault Detection and Diagnosis for Industrial Gas Turbine Systems. International Journal of Automation and Computing, vol. 14, no. 4, pp. 463-473, 2017

## Applied Fault Detection and Diagnosis for Industrial Gas Turbine Systems

###### Corresponding author:Chris Bingham received the B. Eng. degree in electronic systems and control engineering, from Shefield City Polytechnic, UK, in 1989, the M. Sc(Eng) degree in control systems engineering from the University of Shefield, Sheffield, UK, in 1990, and the Ph. D. from Cranfield University, UK, in 1994, where his research focused on control systems to accommodate nonlinear dynamic effects in aerospace flight-surface actuators. From 1994 to 2010, he held academic positions at the University of Sheffleld as a researcher, lecturer and senior lecturer. He is currently professor of Energy Conversion, and college of Science Director of Research at the University of Lincoln, UK. Prof. Bingham has made significant contributions to a diverse range of national and internationally funded research, with a bias towards industrial applications. He currently heads a research team investigating sensor fault detection and remedial strategies, and prognostic and diagnostic techniques for a global fleet of sub-15MW industrial gas turbines in order to maximize unit operational availability. He also actively pursues collaborative research into the modeling of the thermal environment of domestic buildings and their thermal control, and has a long-standing track record in EV/HEV research.     E-mail:cbingham@lincoln.ac.uk (Corresponding author)     ORCID iD:0000-000106684-7894
• Figure  1.  Principle system configuration for the study (a) Schematic of an industrial gas turbine system; (b) Sensor positioning: Group 1-EGT and BTT sensors, and Group 2-BV and BT sensors

Figure  2.  HC dendrogram: fingerprint for normal operation (Group 1: E = EGT; B = BTT)

Figure  3.  Temperature measurement taken during normal operation (Group 1): 13 EGT sensors; 6 BTT sensors)

Figure  4.  SOMNN neuron sample hits from 1440 samples of data from the 19 sensors

Figure  5.  Component planes of the map for normal operation (Group 1)

Figure  6.  Case 1. HC dendrogram indicating sensor BTT6 anomaly (E=EGT; B=BTT)

Figure  7.  Case 1: Temperature measurements showing an emerging fault relating to sensor BTT6

Figure  8.  Case 1: Component planes of the map showing BTT6 fault

Figure  9.  Photos of (a) an early stage of pre-chamber burnout; (b) a pre-chamber burnout failure

Figure  10.  Measurements during normal operation (Group 2): 8 BV sensors; 8 BT sensors

Figure  11.  HC dendrogram: fingerprint for normal operation (Group 2)

Figure  12.  Case 2: HC dendrogram showing sensor BV6 anomaly

Figure  13.  Case 2: Vibration and temperature information indicating a fault in BV6 sensor

Figure  14.  Component planes of the map for normal operation (Group 2)

Figure  15.  Case 2: Component planes of the map showing BV6 sensor fault

Figure  16.  Case 3: Vibration and temperature measurements indicating a bearing fault from BV1 and BV2

Figure  17.  Case 3: Dendrogram indicating gas turbine inlet vibration bearing (BV1 and BV2) fault

Figure  18.  Case 3: Component planes of the map showing gas turbine inlet bearing fault (BV1 and BV2)

Figure  19.  Photos of (a) a normal bearing (half assembly) and (b) the back and inside of normal tilt pads

Figure  20.  Photos of (a) a failed journal bearing and (b) wear on the back of the tilt pad due to fretting

•  [1] M. Jiang, M. A. Munawar, T. Reidemeister, P. A. S. Ward. Efficient fault detection and diagnosis in complex software systems with information-theoretic monitoring. IEEE Transactions on Dependable and Secure Computing, vol. 8, no. 4, pp. 510-522, 2011. [2] Q. Y. Su, Y. C. Li, X. Z., Dai, J. Li. Fault detection for a class of impulsive switched systems. International Journal of Automation and Computing, vol. 11, no. 2, pp. 223-230, 2014. [3] F. Y. Chen, S. J. Zhang, B. Jiang, G. Tao. Multiple modelbased fault detection and diagnosis for helicopter with actuator faults via quantum information technique. Proceedings of the Institution of Mechanical Engineers, Part Ⅰ:Journal of Systems and Control Engineering, vol. 228, no. 3, pp. 182-190, 2014. [4] A. Soualhi, G. Clerc, H. Razik. Detection and diagnosis of faults in induction motor using an improved artificial ant clustering technique. IEEE Transactions on Industrial Electronics, vol. 60, no. 9, pp. 4053-4062, 2013. [5] F. Lu, J. Q. Huang, Y. D. Xing. Fault diagnostics for turboshaft engine sensors based on a simplified on-board model. Sensors, vol. 12, no. 8, pp. 11061-11076, 2012. https://www.researchgate.net/publication/232742389_Fault_Diagnostics_for_Turbo-Shaft_Engine_Sensors_Based_on_a_Simplified_On-Board_Model [6] P. M. Frank. Fault diagnosis in dynamic systems using analytical and knowledge-based redundancy:A survey and some new results. Automatica, vol. 26, no. 3, pp. 459-474, 1990. [7] Y. Zhang, C. M. Bingham, M. Gallimore. Fault detection and diagnosis based on extensions of PCA. Advances in Military Technology, vol. 8, no. 2, pp. 27-41, 2013. [8] W. Deng, X. H. Yang, J. J. Liu, H. M. Zhao, Z. G. Li, X. L. Yan. A novel fault analysis and diagnosis method based on combining computational intelligence methods. Proceedings of the Institution of Mechanical Engineers, Part E:Journal of Process Mechanical Engineering, vol. 227, no. 3, pp. 198-210, 2013. [9] M. F. Harkat, S. Djelel, N. Doghmane, M. Benouaret. Sensor fault detection, isolation and reconstruction using nonlinear principal component analysis. International Journal of Automation and Computing, vol. 4, no. 2, pp. 149-155, 2007. [10] B. Lee, X. S. Wang. Fault detection and reconstruction for micro-satellite power subsystem based on PCA. In Proceedings of the 3rd International Symposium on Systems and Control in Aeronautics and Astronautics (ISSCAA), IEEE, Harbin, China, pp. 1169-1173, 2010. https://www.researchgate.net/publication/227168197_Nonlinear_Principal_Component_Analysis [11] H. B. Liu, M. J. Kim, O. Y. Kang, B. Sankararao, J. T. Kim, C. K. Yoo. Sensor validation for monitoring indoor air quality in a subway station. In Proceedings of the 5th International Symposium on Sustainable Healthy Buildings, Seoul, Korea, pp. 477-489, 2011. [12] Y. H. Li, M. J. Pont, N. B. Jones, J. A. Twiddle. Applying MLP and RBF classifiers in embedded condition monitoring and fault diagnosis systems. Transactions of the Institute of Measurement and Control, vol. 23, no. 5, pp. 315-343, 2001. [13] J. B. Yu. A hybrid feature selection scheme and selforganizing map model for machine health assessment. Applied Soft Computing, vol. 11, no. 5, pp. 4041-4054, 2011. [14] X. Chen, T. Limchimchol. Monitoring grinding wheel redress-life using support vector machines. International Journal of Automation and Computing, vol. 3, no. 1, pp. 56-62, 2006. [15] N. Laouti, S. Othman, M. Alamir, N. Sheibat-Othman. Combination of model-based observer and support vector machines for fault detection of wind turbines. International Journal of Automation and Computing, vol. 11, no. 3, pp. 274-287, 2014. [16] L. B. Jack, A. K. Nandi. Fault detection using support vector machines and artificial neural networks, augmented by genetic algorithms. Mechanical Systems and Signal Processing, vol. 16, no. 2-3, pp. 373-390, 2002. [17] S. T. Wu, T. W. S. Chow. Induction machine fault detection using SOM-based RBF neural networks. IEEE Transactions on Industrial Electronics, vol. 51, no. 1, pp. 183-194, 2004. [18] L. F. Gonçalves, J. L. Bosa, T. R. Balen, M. S. Lubaszewski, E. L. Schneider, R. V. Henriques. Fault detection, diagnosis and prediction in electrical valves using self-organizing maps. Journal of Electronic Testing, vol. 27, no. 4, pp. 551-564, 2011. [19] A. A. Datta, C. A. Mavroidis, M. B. Hosek. A role of unsupervised clustering for intelligent fault diagnosis. In Proceedings of ASME International Mechanical Engineering Congress and Exposition, Seattle, Washington, USA, 2007. [20] Y. Kun, W. Bao, Q. Hu, D. Yu. Abnormal data detection based on hierarchical clustering. Power Engineering, vol. 25, no. 6, pp. 865-869, 2005. http://www.ecice06.com/EN/abstract/abstract26476.shtml [21] Y. G. Zhang, J. F. Zhang, J. Ma, Z. P. Wang. Fault detection based on hierarchical cluster analysis in wide area backup protection system. Energy and Power Engineering, vol. 1, no. 1, pp. 21-27, 2009. [22] C. Romesis, K. Mathioudakis. Setting up of a probabilistic neural network for sensor fault detection including operation with component faults. Journal of Engineering for Gas Turbines and Power, vol. 125, no. 3, pp. 634-641, 2003. [23] T. Kobayashi, D. L. Simon. Hybrid Kalman filter approach for aircraft engine in-flight diagnostics:Sensor fault detection case. Journal of Engineering for Gas Turbines and Power, vol. 129, no. 3, pp. 746-754, 2006. http://www.ecice06.com/EN/abstract/abstract26476.shtml [24] T. Hastie, R. Tibshirani, J. Friedman. 14.3.12 Hierarchical clustering. The Elements of Statistical Learning, New York, USA, Springer, pp. 520-528, 2009. [25] T. Kohonen. Self-organized formation of topologically correct feature maps. Biological Cybernetics, vol. 43, no. 1, pp. 59-69, 1982. [26] MATLAB Version 7. 10. 0. The Mathworks, Natick Mass, USA, 2010.
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##### 出版历程
• 收稿日期:  2013-08-09
• 录用日期:  2014-12-09
• 网络出版日期:  2016-12-05
• 刊出日期:  2017-08-01

## Applied Fault Detection and Diagnosis for Industrial Gas Turbine Systems

### English Abstract

Yu Zhang, Chris Bingham, Mike Garlick and Michael Gallimore. Applied Fault Detection and Diagnosis for Industrial Gas Turbine Systems. International Journal of Automation and Computing, vol. 14, no. 4, pp. 463-473, 2017 doi:  10.1007/s11633-016-0967-5
 Citation: Yu Zhang, Chris Bingham, Mike Garlick and Michael Gallimore. Applied Fault Detection and Diagnosis for Industrial Gas Turbine Systems. International Journal of Automation and Computing, vol. 14, no. 4, pp. 463-473, 2017

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