• DocumentCode
    3600095
  • Title

    Study of Rotating Machinery Fault Diagnosis Model with Integrating Neural Network and Expert System

  • Author

    Wang, Yanqiu ; Yang, Kejian

  • Author_Institution
    Coll. of Electr. Eng., Liaoning Univ. of Technol., Jinzhou, China
  • Volume
    2
  • fYear
    2009
  • Firstpage
    9
  • Lastpage
    13
  • Abstract
    Aimed at these shorts of the unity expert system in rotate machine fault diagnosis, a new method of neural network interfused with traditional expert system is presented in rotate machine fault diagnosis. The frame and function expression of rotate machine fault diagnosis system based on neural network and traditional expert system are given. The select method of diagnosis parameter and the setting up process of knowledge database and module of neural network are analyzed. The experiment results demonstrate that diagnosis faults method is effective.
  • Keywords
    electric machines; expert systems; fault diagnosis; mechanical engineering computing; neural nets; expert system; knowledge database; neural network; rotating machinery fault diagnosis; Artificial neural networks; Diagnostic expert systems; Educational institutions; Expert systems; Explosions; Fault diagnosis; Knowledge acquisition; Logic; Machinery; Neural networks; Expert System; Fault Diagnosis; Neural Network; Rotate Machine;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Hybrid Intelligent Systems, 2009. HIS '09. Ninth International Conference on
  • Print_ISBN
    978-0-7695-3745-0
  • Type

    conf

  • DOI
    10.1109/HIS.2009.114
  • Filename
    5254410