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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/1701


    Title: 應用希爾伯特黃變換(HHT)之邊際譜分析於旋轉機械的元件鬆脫故障診斷;Hilbert-Huang Transform (HHT) Based technique for Identifying Looseness of Rotating Machinery through Marginal Hilbert Spectrum Analysis
    Authors: 黃國豪;Kuo-Hao Huang
    Contributors: 光機電工程研究所
    Keywords: 馬氏距離;經驗模態分解法;鬆動故障;內建模態函數;希爾伯特黃轉換;旋轉機械;Looseness faults;Mahalanobis distance;Empirical Mode Decomposition;Hilbert-Huang transform;Rotating machinery;Intrinsic Mode Function
    Date: 2009-07-09
    Issue Date: 2009-09-21 09:57:58 (UTC+8)
    Publisher: 國立中央大學圖書館
    Abstract: 本文目的主要是研究當旋轉機械發生零件鬆動時,使用HHT中的邊際譜方法來分析振動信號的可行性。通常機械的故障訊號都為非線性、非穩態的,所以利用傳統的傅立葉轉換對訊號做頻域分析,其頻譜無法真實地反映出非線性或非穩態振動的特質,所以必須採取其它方法來分析振動信號。因此我們採用黃鍔博士在1998年提出的希爾伯特黃轉換(Hilbert-Huang transform,HHT),透過整體經驗模態分解法(Ensemble Empirical Mode Decomposition,EEMD)分解而得的內建模態函數(Intrinsic Mode Function,IMF),並透過顯著性檢定(significance test)與除去低頻訊號,選取具有意義的內建模態函數(IMFs)來組成邊際譜,透過故障指標來分辨不同的振動信號。這個故障指標方法結合傳統馬氏距離(Mahalanobis distance)與餘弦指標(cosine),將由使用邊際譜所得到的特徵頻率,根據這個新的距離辨別方法分析振動信號,將不同類型的鬆動故障特徵分辨出來,有效地的提高了機械鬆動的辨別率,並且經由圖形來說明HHT的優點。透過實驗室的轉子平台系統(PBS-5000型)模擬轉子零件鬆動的情形,根據分析振動訊號的結果顯示,本文提出新的故障診斷方法能夠提昇距離分量在不同的邊際譜分布,所以可以辨別不同類型的機械鬆動故障。 The purpose of the research in this paper is to investigate the feasibility of utilizing the marginal Hilbert spectra of vibration signals to identify the looseness faults at different components of rotating machinery. The faulty signals of rotating machinery are generally nonlinear and non-stationary. The traditional Fourier-based spectral analysis can not precisely describe the nonlinear or non-stationary vibration behavior. Therefore, it motivates this research to use an alternative method to analyze the complicated vibration signals. Under such conditions, Huang et al. (1998) propose a new method, termed as Hilbert-Huang Transform (HHT), to analyze the nonlinear or non-stationary data. Through the Ensemble Empirical Mode Decomposition (EEMD) method, the original data can be decomposed into several Intrinsic Mode Function (IMF) components. The information-contained IMFs are extracted through the significance test and removing the low frequency components. The marginal Hilbert spectra consist of the information-contained IMFs and represent the characteristics of the machine operating condition. The fault indicator index that is combined by Mahalanobis distance and cosine index is defined to measure the spectral similarities among different looseness types in the operating machinery. The effectiveness of the proposed method is evaluated by analyzing the marginal Hilbert spectra of vibration signals in the different types of looseness faults. A test bed of rotor-bearing system (PBS-5000) is performed to illustrate the looseness faults at different mechanical components. The results show that the proposed diagnose method is capable of enhancing the distinction quantities of different marginal Hilbert spectra distributions, and thus identify the type of looseness fault at machinery.
    Appears in Collections:[光機電工程研究所 ] 博碩士論文

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