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    jsp.display-item.identifier=請使用永久網址來引用或連結此文件: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/64906

    题名: Classification of Autoregressive Spectral Estimated Signal Patterns Using an Adaptive Resonance Theory Neural Network
    作者: Lin, Chang-ching;Wang, Hsu-pin
    贡献者: 淡江大學經營決策學系
    关键词: Neural networks;Autoregressive;Pattern classification;Machine condition monitoring;Vibration
    日期: 1993-08-01
    上传时间: 2011-10-20 16:09:13 (UTC+8)
    摘要: Machine condition monitoring and fault detection has been an important issue for manufacturing practitioners and researchers around the world, as it impacts production efficiency and effectiveness as well as the morale of the production crew profoundly. This paper examines the use of a relatively new technology, Adaptive Resonance Theory (ART), to assess the machine condition through vibration signals. The vibration signal is first compressed with an Autoregressive (AR) technique in order to reduce the amount of information which the ART neural network is to deal with. The theoretical foundation of the fault classification system is discussed, followed by a brief case study.
    關聯: Computers in Industry22(2), pp.143-157
    DOI: 10.1016/0166-3615(93)90061-5
    显示于类别:[管理科學學系暨研究所] 期刊論文


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