Classification of Autoregressive Spectral Estimated Signal Patterns Using an Adaptive Resonance Theory Neural Network
學年 82
學期 1
出版(發表)日期 1993-08-01
作品名稱 Classification of Autoregressive Spectral Estimated Signal Patterns Using an Adaptive Resonance Theory Neural Network
作品名稱(其他語言)
著者 Lin, Chang-ching; Wang, Hsu-pin
單位 淡江大學經營決策學系
出版者
著錄名稱、卷期、頁數 Computers in Industry22(2), pp.143-157
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.
關鍵字 Neural networks;Autoregressive;Pattern classification;Machine condition monitoring;Vibration
語言 en
ISSN
期刊性質 國內
收錄於 SCI
產學合作
通訊作者
審稿制度
國別 TWN
公開徵稿
出版型式 ,電子版
相關連結

機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/64906 )

機構典藏連結