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Please use this identifier to cite or link to this item: http://ir.ncue.edu.tw/ir/handle/987654321/11067

Title: 類神經學習向量量化網路之渦輪發電機故障診斷
Neural Learning Vector Quantization Network for Turbine Generator Fault Diagnosis
Authors: 魏忠必;洗鴻瑋;陳家湧
Contributors: 電機工程系
Keywords: 類神經網路;學習向量量化網路;渦輪發電機;故障診斷
Neural network;Learning vector quantization;Trbine generator;Fault diagnosis
Date: 2006-07
Issue Date: 2012-06-05T03:54:00Z
Publisher: 國立高雄應用科技大學
Abstract: 本文提出以類神經網路之學習向量量化網路(Learning Vector Quantization, LVQ)應用於渦輪發電機組故障診斷(Turbine-Generator Fault Diagnosis)之研究。利用學習向量量化網路之特性,用已知的故障數據對類神經網路進行訓練後,即可對渦輪發電機組的故障型態進行診斷。為驗證本文所提之LVQ對渦輪發電機故障診斷之準確性,使用MATLAB撰寫LVQ程式,並經過訓練後診斷新的樣本資料,分辨出故障型態,證明所提方法之準確性可達到100%。
This paper presents a Neural Network Learning Vector Quantization (LVQ) for turbine generator fault diagnosis. Using the known data to train the LVQ Network. Then, input the new samples to diagnose the fault type. In order to prove the accuracy of the LVQ for turbine generator fault diagnosis, using MATLAB to develop LVQ program. The simulation can prove the proposed method is effective and accurate.
Relation: 2006電子通訊與應用研討會, 國立高雄應用科技大學, 2006年7月6日
Appears in Collections:[電機工程學系] 會議論文

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