| 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 | 否 |
| 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 ) |
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