中原大學電機工程學系
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100學年度
風力發電機組故障警示之人機介面製作
指導老師: 李俊耀   組長:趙韋翔   組員:李陳宗、鄭仲佑
本專題主要是對小型風力發電機組的齒輪箱漏油及軸承破壞故障進行研究,並且對於上述兩種常見的故障情形製做出實際的故障樣品,藉由量測風力發電機所產生的故障電流訊號加以分析,辨識出風力發電機組的故障情形並立即加以修復。之後,透過快速傅立葉轉換法來分析之前所量測的故障訊號,再利用K-最鄰近分類法交叉驗證故障訊號加以分類。實驗結論顯示,本專題研究所使用的分析法,辨識率可達到96.7%,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 gearbox oil leak of a small wind generator. The sample of damage generators were used to model the real damage of the wind generators. Then, by analyzing the current signals from the damage generators, the damage would be recognized and repaired immediately. Then, the features of the current signals are obtained by using fast Fourier transform (FFT). And the classifications of the current signals are obtained by using the K-nearest neighbor (KNN). The conclusion of the experiment indicates that the recognition rate of classification can reach 96.7% and the superiority of the proposed methods to 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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