Study on an Intelligent Ferrography Diagnosis Expert System

  • Jiadao, Wang (Department of Precision Instruments and Mechanology, State Key Laboratory of Tribology, Tsinghua University) ;
  • Darong, Chen (Department of Precision Instruments and Mechanology, State Key Laboratory of Tribology, Tsinghua University) ;
  • Xianmei, Kong (Department of Precision Instruments and Mechanology, State Key Laboratory of Tribology, Tsinghua University)
  • 발행 : 2002.10.21

초록

Wear is one of the main factors causing breakdown and fault of machine, so ferrography technique analyzing wear particles can be an effective way for condition monitoring and fault diagnosis. On the base of the forward multilayer neural network, a nodes self-deleting neural network model is provided in this paper. This network can itself deletes the nodes to optimize its construction. On the basis of the nodes self-deleting neural network, an intelligent ferrography diagnosis expert system (IFDES) for wear particles recognition and wear diagnosis is described. This intelligent expert system can automatically slim lip knowledge by learning from samples and realize basically the entirely automatic processing from wear particles recognition to wear diagnosis.

키워드