ROTATING MACHINERY MONITORING AND FAULT DIAGNOSIS WITH NEURAL NETWORK ENHANCED FUZZY LOGIC EXPERT SYSTEM Conference Paper uri icon

abstract

  • This paper presents an intelligent monitoring and fault diagnosis approach for rotating machinery by utilizing artificial neural networks and fuzzy logic expert systems (FLES). A recurrent neural network (RNN) is introduced to filter the input signal before they are forwarded to the expert system. The RNN is trained based on existing operational data so that it can adapt to a specific machines configurations and conditions. The RNN is able to generate proper baseline signal even if the machine is not under the exact same condition. A fuzzy logical expert system is then used for diagnosis based on the residual signal generated by the RNN. The system is incorporated into an existing comprehensive roto-dynamics software package named LVTRC.

name of conference

  • Volume 6: Ceramics; Controls, Diagnostics and Instrumentation; Education; Manufacturing Materials and Metallurgy

published proceedings

  • PROCEEDINGS OF THE ASME TURBO EXPO: TURBINE TECHNICAL CONFERENCE AND EXPOSITION, 2016, VOL 6

author list (cited authors)

  • Li, X., Palazzolo, A., & Wang, Z.

citation count

  • 6

complete list of authors

  • Li, Xiaojun||Palazzolo, Alan||Wang, Zhiyang

publication date

  • June 2016