風力發電機關鍵零組件故障診斷之研究

The study on fault diagnosis of key components in wind turbine

紀騰揚(Teng-Yang Chi)

指導教授 ： 蔡進發

國立臺灣大學/工學院/工程科學及海洋工程學研究所/碩士(2010年)

https://doi.org/10.6342/NTU.2010.02326

摘要

摘要
本研究偵測風力發電機的軸承以及齒輪箱振動訊號，以進行故障診斷，並藉由實驗數據建立頻譜與故障情況之間的關係。
實驗操作上將建立齒輪軸承轉子實驗平台，用以模擬出三種齒輪故障訊號，包括齒輪不平衡、齒輪斷齒以及軸不平行等，並將時域訊號作快速傅立葉轉換取得頻譜訊號，再從中擷取頻譜特徵作為診斷依據。
擷取特徵數據後便透過K平均法以及貝氏網路別分進行分析。分析結果顯示，採用貝氏網路分析時，有著明顯優於K平均法的準確率，且貝氏網路的平均準確率高達90%以上。

關鍵字

風力發電 ； 故障診斷 ； 快速傅立葉轉換 ； K平均法 ； 貝氏網路

並列摘要

Abstract
This paper studies on monitoring vibrational signal of bearing and gearbox in wind turbine to diagnose its condition, and build up the relationship between fault and spectrum by using experimental data.
To simulate three kinds of failure conditions of gears, including imbalance gear, tooth breakage and unparallel shaft, the gear-rotor system is built up for the gear fault experiment. Fast Fourier Transform will transform time domain signal into spectrum which is frequency domain signal, and extract features of spectrum as the basis of diagnosis.
After features extraction, K-means algorithm and Bayesian Network are used to analyze features of spectrum. It is shown that Bayesian Network has higher precision as compared with K-means algorithm, and the average precision of Bayesian Network is up to 90 percent and above.

並列關鍵字

wind energy ； fault diagnosis ； Fast Fourier Transform ； K-means algorithm ； Bayesian Network

參考文獻

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