Self-reorganization of symptom parameters in frequency domain for failure diagnosis by genetic algorithms

  • Authors:

  • Peng Chen;Masami Nasu;Toshio Toyota

  • Affiliations:

  • Faculty of Computer Science and System Engineering, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820, Japan. E-mail: chen@mse.kyutech.ac.jp, nasu@mse.kyutech.ac.jp, toyota@m ...;Faculty of Computer Science and System Engineering, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820, Japan. E-mail: chen@mse.kyutech.ac.jp, nasu@mse.kyutech.ac.jp, toyota@m ...;Faculty of Computer Science and System Engineering, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820, Japan. E-mail: chen@mse.kyutech.ac.jp, nasu@mse.kyutech.ac.jp, toyota@m ...

  • Venue:
  • Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
  • Year:
  • 1998

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Abstract

In the field of failure diagnosis of plant machinery, one of the most important and most difficult factors is the identification of Symptom Parameters (SP). Failures of machinery can be sensitively detected and the failure types can be distinguished by using the optimum SP. Currently, however, there is no acceptable method for extracting the optimum SP. In order to overcome this difficulty and ensure highly accurate failure diagnosis, in this paper a new method called "Self-reorganization of Symptom Parameters" has been proposed by using Genetic Algorithms (GA). The new method can also be applied to other pattern recognition problems. It has been proved that the optimum SP can be quickly discovered by applying the method to many practical machinery diagnoses.