Advanced Gas Turbine Rotor Shaft Fault Diagnosis Using Artificial Neural Network


  • Ezenwa A. Ogbonnaya
  • Emmanuel M. Adigio
  • Hyginus U. Ugwu
  • Magnus. C. Anumiri


artificial intelligence, condition monitoring, excitation force, amplitude, resonance


The effect of vibration in plant leads to catastrophic failure of a system. This is why vibration monitoring of a system constitutes a very key practice of ensuring power plant availability. Force, Amplitude and Resonance a program written in Visual Basic Programming language was utilized in this study to monitor the vibration level of the Gas Turbine (GT17) in Afam thermal station and to calculate the force causing vibration on the bearing. The program was also run using the data obtained from the plant. Results show that vibration velocity amplitude of bearing 2 on weeks 5 and 8 were 6.7mm/s and 6.6mm/s and the forces causing vibration were 2.545x104N and 2.272x104N respectively. The comparison of results obtained with maximum vibration velocity amplitude of the machine (7mm/s) showed that the vibration of the machine was tending towards the maximum value. Therefore, proper attention should be given to bearing 2 to avoid failure of the Gas Turbine.


Y. Zhao, “A profit-based approach for gas turbine power plant outage planning,” Journal of Engineering for Gas Turbines and Power, vol. 128, pp. 806-814, 2006.

A. Razak, Industrial Gas Turbines Performance and Operability, UK: Woodhead Publishing, pp. 50- 73. 2007.

I. Loboda, S. Yepifanov and Y. Feldshteyn, “An Integrated Approach to Gas Turbine Monitoring and Diagnostics,” Proceedings of IGTI/ASME Turbo Expo, 2009.

Y. Zhao, “A Sequential Approach for Gas Turbine Power Plant Preventative Maintenance Scheduling,” Journal of Engineering for Gas Turbines and Power, vol. 128, pp. 796-805, 2006.

H. Asgari, C. XiaoQi, and S. Raazesh, “Applications of Artificial Neural Networks (ANNs) to Rotating Equipment”. 3 rd Conference on Rotating Equipment in Oil and Power Industries Nov. 22-23, 2011, Razi Intl. Conference Center, Tehran, Iran.

R. Vicen, Ship Finder, Madrid, Spain Ed. Thilmany J in Computing, Mechanical Engineering, The Magazine of ASME, vol. 133, May 2011.

M. Fast, S. De and M. Assadi, “Condition Based Maintenance of Gas Turbines Using Simulation Data and Artificial Neural Network: A Demonstration of Feasibility”, ASME Turbo Expo, Berlin, 2008.

M.H. Beale, T. Hagan and H. Demuth, Neural Network Toolbox™, User’s Guide, Addison-Wesley Limited, Finland. pp. 69- 74, 2011.

I. Loboda and S. Yepifanov, “A Mixed Data-Driven and Model Based Fault Classification for Gas Turbine Diagnosis”, Proceedings of ASME Turbo Expo 2010: International Technical Congress, 8p., Scotland, UK, June 14-18, Glasgow, ASME Paper No. GT2010-23075, 2010.

E. A. Ogbonnaya and K.T. Johnson, “Optimizing Gas Turbine Rotor Shaft Fault Detection, Identification and Analysis for Effective Condition Monitoring”, Journal of Emerging Trends in Engineering and Applied Sciences (JETEAS), vol. 2, pp. 11-17, 2011.

I.Y. Li, “Performance-Analysis-Based Gas-turbine Diagnostics a Review”, Journal of Power and Energy, vol. 216 Part A IMechE, pp. 363-377, 2002.

R.V. Dukkipati and J. Srinivas, Textbook of Mechanical Vibrations, 1st ed., New Delhi: Prentice-Hall, pp. 92-101, 2006.




How to Cite

E. A. Ogbonnaya, E. M. Adigio, H. U. Ugwu, and M. C. Anumiri, “Advanced Gas Turbine Rotor Shaft Fault Diagnosis Using Artificial Neural Network”, Int. j. eng. technol. innov., vol. 3, no. 1, pp. 58–69, Jan. 2013.