Transmission-component monitoring and comparison of two artificial neural network schemes

Min Chun Pan, Yean Hong Liu

Research output: Contribution to journalConference articlepeer-review

1 Scopus citations


This study conducts an investigation on flaw cogged V-belts, galling roller-chains, and imbalancing rotors through a constructed transmission- component test bench. Nine channels of noise and vibration data are acquired and processed to extract features that exhibit the faulty condition of components in specific states. Two artificial neural network schemes, i.e., the backward propagation and self-organization mapping algorithms, are employed as pattern recognition tools. Additionally, the classification of condition patterns of machine components is further illustrated using a discrimination-space technique. Thus, the mechanism of pattern recognition of artificial neural networks can be clearly realized, but not only considered as an inaccessible processing black box.

Original languageEnglish
Pages (from-to)806-814
Number of pages9
JournalProceedings of SPIE - The International Society for Optical Engineering
StatePublished - 2004
EventSmart Structures and Materials 2004 - Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems - San Diego, CA, United States
Duration: 15 Mar 200418 Mar 2004


  • Artificial neural networks
  • Feature extraction
  • Machine monitoring
  • Pattern recognition


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