Transmission component monitoring and comparison of two artificial neural network schemes

Min-Chun Pan; Yean-Hong Liu

doi:10.1117/12.537717

29 July 2004 Transmission component monitoring and comparison of two artificial neural network schemes

Min-Chun Pan, Yean-Hong Liu

Proceedings Volume 5391, Smart Structures and Materials 2004: Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems; (2004) https://doi.org/10.1117/12.537717
Event: Smart Structures and Materials, 2004, San Diego, CA, United States

Abstract

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.

Citation Download Citation

Min-Chun Pan and Yean-Hong Liu "Transmission component monitoring and comparison of two artificial neural network schemes", Proc. SPIE 5391, Smart Structures and Materials 2004: Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems, (29 July 2004); https://doi.org/10.1117/12.537717

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