中原大學電機工程學系
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99學年度
風力發電機組軸承及漏油故障自動辨識技術
指導老師:李俊耀   組長:林巧仟   組員:許宸華、廖冠霖、沈譯輔
本專題針對小型風力發電機齒輪箱漏油及軸承破壞故障進行研究,並參考實際故障情況,製作特殊之損壞故障試品,並藉由風力發電機故障輸出訊號之量測與分析,辨識風力發電機之故障型式。最後,利用三種訊號分析法,擷取其訊號的特徵值,再以類神經網路進行故障訊號特徵值分類,實驗結果顯示,本專題所使用三種訊號分析法之辨識率均可達98%以上,對於風力發電機齒輪箱漏油及軸承破壞故障之辨識能力具有優越性。
This study focuses on the detection of the bearing damage and the failure of gear box oil leak of a small wind generator. The tailor made damage generators were used to simulate the real damage of the wind generators. In this study, the signals measured from the damage wind generators were analyzed to realize the status of the damage. Finally, the three analysis method is adopted to obtain the features of the signals and, then the network is applied to classify the types of damages. The experimental results show that the classification accuracy is about 98% and the superiority of the proposed method to the detect the damage of the wind generators can be verified.
我們決定將壓電陶瓷晶片和鞋子做結合。
   
 
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