29 July 2004 Transmission component monitoring and comparison of two artificial neural network schemes
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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.
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Min-Chun Pan, 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); doi: 10.1117/12.537717; https://doi.org/10.1117/12.537717
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KEYWORDS
Feature extraction

Neurons

Pattern recognition

Artificial neural networks

Signal processing

Control systems

Diagnostics

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