Rotor fault diagnosis system based on individual neural networks and fuzzy synthesized engine

Journal of the Chinese Institute of Engineers, Transactions of the Chinese Institute of Engineers,Series A/Chung-kuo Kung Ch'eng Hsuch K'an

Volumn 33, Issue 7, 2010, Pages 975-986

  Chen, Chin Sheng ^a   Lai, You Hung ^a  

^a NATIONAL TAIPEI UNIVERSITY OF TECHNOLOGY   (Taiwan)

Author keywords

Fault diagnosis; Fuzzy reasoning engine; Individual neural networks; Taguchi's method

Indexed keywords

EID: 78049476154     PISSN: 02533839     EISSN: 21587299     Source Type: Journal    
DOI: 10.1080/02533839.2010.9671686     Document Type: Article

References (18)

1. Bai, M. R., Jeng, J., and Chen, C., 2002. “Adaptive Order Tracking Technique Using Recursive Least-Square Algorithm”. Journal of Vibration and Acoustics, 124 (4):502–511.
2. Donald, E., and Bently. 2002. “Fundamentals of Rotating Machinery Diagnostics,”. NV, USA:Bently Pressurized Bearing Press.
3. Diao, Y., and Passino, K. M., 2004. “Fault Diagnosis for a Turbine Engine”. Control Engineering Practice, 12 (9):1151–1165.
4. Fu, L. D., Chen, K. S., Yu, J. S., and Zeng, L. C., “The Fault Diagnosis for Electro-Hydraulic Servo Valve Based on the Improved Genetic Neural Network Algorithm”. Proceedings of the 2006 International Conference on Machine Learning and Cybernetics. Dalian, China. pp. 2995–2999.
5. Fyfe, K. R., and Munck, E. D. S., 1997. “Analysis of Computed Order Tracking”. Mechanical Systems and Signal Processing, 11 (2):187–205.
6. Goldman, P., and Muszynska, A., 1999. “Application of Full Spectrum to Rotating Machinery Diagnostics”. Orbit, 20 (1):17–21.
7. Hayashi, S., Asakura, T., and Zhang, S., “Study of Machine Fault Diagnosis System Using Neural Networks”. Proceedings of the International Joint Conference on Neural Networks. Honolulu, HI, USA. Vol. 1, pp. 956–961.
8. Jang, J. S. R., and Sun, C. T., “Neuro-Fuzzy Modeling and Control”. Proceedings of the 16th IEEE International Conference on Electronics, Communications and Computers. Vol. 83, pp. 378–382. 3
9. Laws, B., 1998. “When You Use Spectrum, Don't Use It Halfway”. Orbit, 18 (2):23–26.
10. Macleod, C., Dror, G., and Maxwell, G., 1999. “Training Artificial Neural Networks Using Taguchi Methods”. Artificial Intelligence Review, 13 (3):177–184.
11. Negi, G., and Mark, L., 2004. “Residual Life Predictions From Vibration-Based Degradation Signal:A Neural Network Approach”. IEEE Transactions on Industrial Electronics, 51 (3):694–700.
12. Southwick, D., 1993. “Using Full Spectrum Plots”. Orbit, 14 (4):19–21.
13. Southwick, D., 1994. “Using Full Spectrum Plots Part2”. Orbit, 15 (2):11–15.
14. Sukthomya, W., and Tannock, J., 2005. “The Optimization of Neural Network Parameters Using Taguchi's Design of Experiments Approach:An Application in Manufacturing Process Modeling”. Neural Computing and Applications, 14 (4):337–344.
15. Toutountzakis, T., Tan, C. K., and Mba, D., 2005. “Application of Acoustic Emission to Seeded Gear Fault Detection”. NDT & E International, 38 (1):27–36.
16. Viswanathan, A., MacLeod, C., Maxwell, G., and Kalidindi, S., 2005. “Training Neural Networks Using Taguchi Methods:Overcoming Interaction Problems”. Lecture Notes in Computer Science, 3697:103–108.
17. Wu, J. D., and Liu, C. H., 2009. “An Expert System for Fault Diagnosis in Internal Combustion Engines Using Wavelet Packet Transform and Neural Network”. Expert system with Applications, 36 (3):4278–4286.
18. Zhang, S., Asakura, T., Xu, X., and Xu, B., 2003. “Fault Diagnosis System for Rotary Machines Based on Fuzzy Neural Networks”. JSME International Journal, Series C:Mechanical Systems, Machine Elements and Manufacturing, 46 (3):1035–1041.