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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/1702

    Title: 總體經驗模態分解法(EEMD)結合自回歸(AR)模型在旋轉機械之元件鬆脫故障診斷之應用;Applications of Ensemble Empirical Mode Decomposition (EEMD) and Auto-Regressive (AR) Model for Diagnosing Looseness Faults of Rotating Machinery
    Authors: 洪暉程;Huei-Cheng Hong
    Contributors: 光機電工程研究所
    Keywords: 後處理總體經驗模態分解法;重要性測試;自回歸模型;自相關函數;故障診斷;希爾伯特黃轉換;總體經驗模態分解法;經驗模態分解法;Fault Diagnosing;EMD;HHT;Auto-Regressive;ACF;AR model;Significance test;post-processing of EEMD;EEMD
    Date: 2009-07-09
    Issue Date: 2009-09-21 09:58:00 (UTC+8)
    Publisher: 國立中央大學圖書館
    Abstract: 後處理總體經驗模態分解法可將旋轉機械振動訊號分解成數個無模態混雜的內稟模態函數,運算後的基底波形對稱,符合原始內稟模態函數的要求。對訊號建立自回歸模型則可以對訊號波形的未來發展進行預測,其係數凝聚了系統特質。 本論文結合後處理總體經驗模態分解法與自回歸模型為旋轉機械作出故障診斷。以自相關係數為輔助,針對後處理總體經驗模態分解法得到的內稟模態函數作出分析,挑選有意義的內稟模態函數時域波形建立自回歸模型,取其係數作為鬆動故障診斷之依據,並得到良好的診斷效果。 Post processing of Ensemble Empirical Mode Decomposition (EEMD) can be utilized to decompose the vibration signals of rotating machinery into finite number of Intrinsic Mode Functions (IMFs) without mode mixing problem. The basis of the post processing of EEMD will satisfy the well-defined conditions of IMF. The Autoregressive (AR) model of information-contained IMFs can be used to predict the unmeasured vibration signal, and the coefficients of AR model represent the feature of systematic dynamic behavior. In this paper, the post-processing of EEMD combining the AR model is proposed for diagnosing the looseness faults at different conponents of rotating machinery. The information-contained IMFs are selected to build the AR model. The looseness types are identified by analyzing the coefficients of AR model. The effectiveness of the proposed method is validated through the analysis of the experimental data.
    Appears in Collections:[光機電工程研究所 ] 博碩士論文

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