Smart Structures and Systems

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Image recognition technology in rotating machinery fault diagnosis based on artificial immune

  • Zhu, Dachang (College of Mechanical and Electrical Engineering, Jiangxi University of Science and Technology) ;
  • Feng, Yanping (College of Mechanical and Electrical Engineering, Jiangxi University of Science and Technology) ;
  • Chen, Qiang (College of Mechanical and Electrical Engineering, Jiangxi University of Science and Technology) ;
  • Cai, Jinbao (Faculty of Foreign Studies, Jiangxi University of Science and Technology)
  • Received : 2007.11.15
  • Accepted : 2009.09.11
  • Published : 2010.05.25
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Abstract

By using image recognition technology, this paper presents a new fault diagnosis method for rotating machinery with artificial immune algorithm. This method focuses on the vibration state parameter image. The main contribution of this paper is as follows: firstly, 3-D spectrum is created with raw vibrating signals. Secondly, feature information in the state parameter image of rotating machinery is extracted by using Wavelet Packet transformation. Finally, artificial immune algorithm is adopted to diagnose rotating machinery fault. On the modeling of 600MW turbine experimental bench, rotor's normal rate, fault of unbalance, misalignment and bearing pedestal looseness are being examined. It's demonstrated from the diagnosis example of rotating machinery that the proposed method can improve the accuracy rate and diagnosis system robust quality effectively.

Keywords

Acknowledgement

Supported by : National Natural Science Foundation of China

References

  1. Brandt, Y., Jervis, B.W. and Maidon, Y. (1997), "Circuit multi-fault diagnosis and prediction error estimation using a committee of Bayesian neural networks", Proceedings of the'97 IEE Colloquium on Testing Mixed Signal Circuits and Systems, London, UK, October.
  2. Caslellini, P., Scalise, A. and Scalise, L. (2000), "A 3-D measurement system for the extraction of diagnostic parameters in suspected skin nevoid lesions", IEEE T. Instrum. Meas., 49(5), 924-928. https://doi.org/10.1109/19.872909
  3. Dasgupta, D., Ji, Z. and Gonzalez, F. (2003), "Artificial immune system (AIS) research in the last five years", Proceedings of the '03 Congress on Evolutionary Computation, Canberra, Australia, December.
  4. Elhadet, M., Das, S. and Nayak, A. (2006), "A novel artificial-immune-based approach for system-level fault diagnosis", Proceedings of the 1st International Conference on Availability, Reliability and Security, Vienna, Austria, April.
  5. Guohua, Gao, Yu, Zhu, Guanghuang, Duan and Yongzhong, Zhang (2006), "Intelligent fault identification based on wavelet packet energy analysis and SVM", Proceedings of the '06 9th International Conference on Control, Automation, Robotics and Vision, Singapore, December.
  6. Hayashi, S., Asakura, T. and Sheng, Zhang (2002), "Study of machine fault diagnosis system using neural networks", Proceedings of the '02 International Joint Conference on Neural Networks Congress, Hawaii, USA, May.
  7. Kao, I., Li, X.L. and Tsai, C.H.D. (2009), "Model-based and wavelet-based fault detection and diagnosis for biomedical and manufacturing applications: leading towards better quality of life", Smart Struct. Syst., 5(2), 153-171. https://doi.org/10.12989/sss.2009.5.2.153
  8. Khan, M.A.S.K., Radwan, T.S. and Rahman, M.A. (2007), "Real-time implementation of Wavelet packet transform-based diagnosis and protection of three-phase induction motors", IEEE T. Energy Conver., 22(3), 647-655. https://doi.org/10.1109/TEC.2006.882417
  9. McCoy, D.F. and Devarajan, V. (1997), "Artificial immune systems and aerial image segmentation", Proceedings of the '97 IEEE International Conference on Systems, Man and Cybernetics, Hyatt Orlando, Orlando, Florida, USA, October.
  10. Ortiz, E. and Syrmos, V. (2006), "Support vector machines and wavelet packet analysis for fault detection and identification", Proceedings of the '06 International Joint Conference on Neural Networks Congress, Vancouver, BC, Canada, July.
  11. Shen, M., Sun, L. and Chan, F.H.Y. (2001), "Method for extracting time-varying rhythms of electroencephalography via wavelet packet analysis", IEE P.-Sci. Meas. Tech., 148(1), 23-27. https://doi.org/10.1049/ip-smt:20010107
  12. Sathyanath, S. and Sahin, F. (2001), "An AIS approach to a color image classification problem in a real time industrial application", Proceedings of the '01 IEEE International Conference on Systems, Man and Cybernetics, Tucson, Arizona, October.
  13. Tao, W., Nian, L., Chi, X. and Kejin, S. (2006), "Study of fault diagnosis in brushless machines based on artificial immune algorithm", Proceedings of the '06 IEEE International Symposium on Industrial Electronics, Montreal, Quebec, July.
  14. Tarakanov, A. and Dasgupta, D. (2000), "A formal model of an artificial immune system", Biosystems, 55(1-3), 151-158. https://doi.org/10.1016/S0303-2647(99)00093-3
  15. Upadhyaya, B.R. and Skorska, M. (1982), "A modular approach for the diagnostic analysis of dynamic systems using stochastic time-series models", IEEE T. Syst. Man Cy., 12(6), 794-804. https://doi.org/10.1109/TSMC.1982.4308913
  16. Wei, Dou, Zhan-sheng, Liu and Xiaowei, Wang (2007), "Application of image recognition based on artificial immune in rotating machinery fault diagnosis", Proceedings of the 1st International Conference on Bioinformatics and Biomedical Engineering, Wuhan, China, July.
  17. Yun, G.J., Ogorzalek, K.A., Dyke, S.J. and Song, W. (2009), "A two-stage damage detection approach based on subset selection and genetic algorithms", Smart Struct. Syst., 5(1), 1-21. https://doi.org/10.12989/sss.2009.5.1.001
  18. Zhinong, Li, Junjie, Sun and Jie, Han (2006), "Parametric bispectrum analysis of cracked rotor based on blind idendification of time series models", Proceedings of '06 6th World Congress on Intelligent Control and Automation Congress, Dalian, China, June.

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