A Study on Fault Detection of a Turboshaft Engine Using Neural Network Method

  • Published : 2008.05.10


It is not easy to monitor and identify all engine faults and conditions using conventional fault detection approaches like the GPA (Gas Path Analysis) method due to the nature and complexity of the faults. This study therefore focuses on a model based diagnostic method using Neural Network algorithms proposed for fault detection on a turbo shaft engine (PW 206C) selected as the power plant for a tilt rotor type unmanned aerial vehicle (Smart UAV). The model based diagnosis should be performed by a precise performance model. However component maps for the performance model were not provided by the engine manufacturer. Therefore they were generated by a new component map generation method, namely hybrid method using system identification and genetic algorithms that identifies inversely component characteristics from limited performance deck data provided by the engine manufacturer. Performance simulations at different operating conditions were performed on the PW206C turbo shaft engine using SIMULINK. In order to train the proposed BPNN (Back Propagation Neural Network), performance data sets obtained from performance analysis results using various implanted component degradations were used. The trained NN system could reasonably detect the faulted components including the fault pattern and quantity of the study engine at various operating conditions.


Fault detection;turboshaft engine;Neural Network;model based diagnose


  1. Zedda, M., and Singh, R., 1998, 'Fault Diagnosis of a Turbofan Engine using Neural Networks: A Quantitative Approach' , American Institute of Aeronautics and Astronautics, AIAA 98-3602
  2. Lu, P. J.,Zhang, M. C., Hsu, T. C., and Zhang, J., 2000, 'An Evaluation of Engine Faults Diagnostics using Artificial Neural Networks' , Proceedings of ASME TURBO EXPO 2000, 2000-GT-0029
  3. Volponi, A. J., Depold, H., Ganguli, R., and Daguang, C., 2000, 'The Use of Kalman Filter and Neural Network Methodologies in Gas Turbine Performance Diagnostics: A Comparative Study' , Proceedinss of ASME TURBO EXPO 2000, 2000-GT-547
  4. Urban, L.A., 1972, 'Gas Path Analysis Applied to Turbine Engine Condition Monitoring', J. of Aircraft, Vol. 10, No. 7, pp. 400-406
  5. Lee, H. Y., Mun, G. I., 1999, 'Fuzzy-Neuro using MATLAB' , A-Jin
  6. Heykin, S., 1994, 'Neural Networks A Comprehensive Foundation' , Macmilian
  7. Diakunchak, LS., 1992, 'Performance Deterioration in Industrial Gas Turbines' Trans. ASME Journal of Engineering for Gas Turbine and Power, Vol. 114 : 161-168
  8. Depold, H. R., and Gass, F. D., 1999, 'The Application of Expert Systems and Neural Networks to Gas Turbine Prognostics and Diagnostics' , Journal of Engineering for Gas Turbines and Power, Vol. 121, pp.607-612
  9. Sun, B., Zhang, J., Zhang, S., 2000, 'An Investigation of Artificial Neural Network (ANN) In Quantitative Fault Diagnosis for Turbofan Engine' , Proceedings of ASME TURBO EXPO 2000, 2000-GT-0032
  10. Tang, G., Yates, C. L., and Chen, D., 1998, 'Comparative Study of Two Neural Networks Applied to Jet Engine Fault Diagnosis' , American Institute of Aeronautics and Astronautics, AIAA 98-3549
  11. Kong, C.D. and Ki, J.Y., 2003, 'A New Scaling Method for Component Maps of Gas Turbine Using System Identification', J. of Engineering for Gas Turbines and Power, Vol. 125, 979-985
  12. Kong. C.D., Kho, S.H., Ki, J.Y., 2007, 'A Novel Method for Component Map Identification of a Gas Turbine Using Intelligent Method and Engine Performance Deck', ASME Turbo Expo 2007, GT-2007-27569
  13. Pratt-Whitney Ltd., 2006, 'EEPP(Estimated Engine Performance Program) Manual', Pratt-Whitney Canada
  14. Kurzke, J.,2007, 'Manual GASTURB 9.0 for Windows - A Program to Calculate Design and Off-design Performance of Gas Turbines', Technical Report