• DocumentCode
    1609199
  • Title

    Intelligent Fault Diagnosis of Rotating Machinery Based on Grey Similar Relation Degree

  • Author

    Xiong, Wei ; Su, Yanping ; Zhou, Yanjie ; Wang, Hongjun ; Zhang, Wenbin

  • Author_Institution
    Eng. Coll., Honghe Univ., Mengzi, China
  • fYear
    2012
  • Firstpage
    335
  • Lastpage
    337
  • Abstract
    After deeply studying the relationship between reason and symptom of the fault, a novel intelligent fault diagnosis method was proposed based on grey similar relation degree. Firstly, the definition of grey relation degree was introduced. Secondly, on the base of analyzing the defects existed in the grey relation degree, the definition of grey similar relation degree was introduced. Thirdly, the symptom set and standard fault set had been established based on the known knowledge, experience and fault examples. Finally, the grey similar relation degree was used to describe the similarity between the faults and symptoms. Even the fault information was imperfect and the fault mechanism was not clear, the results of diagnosis would be more correct than before. The practical results show that this approach is quite efficient and intelligent. It´s suitable for on-line monitoring and diagnosis of rotating machinery.
  • Keywords
    fault diagnosis; grey systems; matrix algebra; set theory; turbomachinery; fault mechanism; fault reason; fault symptom; grey similar relation degree; intelligent fault diagnosis; rotating machinery; standard fault set; symptom set; Educational institutions; Fault diagnosis; Industrial control; Machinery; Mathematical model; Standards; Vectors; grey similar relation degree; intelligent fault diagnosis; rotating machinery; standard fault set;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Industrial Control and Electronics Engineering (ICICEE), 2012 International Conference on
  • Conference_Location
    Xi´an
  • Print_ISBN
    978-1-4673-1450-3
  • Type

    conf

  • DOI
    10.1109/ICICEE.2012.95
  • Filename
    6322384