臺灣博碩士論文加值系統

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.

中文提要 ……………………………………………………………… i
英文提要 ……………………………………………………………… ii
誌謝 ……………………………………………………………… iii
目錄 ……………………………………………………………… iv
圖目錄 ……………………………………………………………… v
表目錄 ……………………………………………………………… x
第一章 緒論………………………………………………………… 1
1.1 前言………………………………………………………… 1
1.2 研究動機…………………………………………………… 3
1.3 文獻回顧…………………………………………………… 4
1.4 研究內容與大綱…………………………………………… 8
第二章 HHT理論基礎……………………………………………… 10
2.1 希爾伯特黃轉換(Hilbert-Huang Transform, HHT)………… 10
2.2 經驗模態分解法(Empirical Mode Decomposition, EMD)… 11
2.3 總體經驗模態分解法(Ensemble EMD,EEMD)與後處理總
體經驗模態分解法(post-processing of EEMD) …………… 19
2.4 Hilbert轉換………………………………………………… 25
2.5 Significance test…………………………………………… 27
2.6 HHT之特性………………………………………………… 29
第三章 時間序列分析概說………………………………………… 31
3.1 自回歸模型(Autoregressive model ,AR model)…………… 31
3.2 自相關函數(Autocorrelation Function, ACF)……………… 38
第四章 實驗架構及實驗方法……………………………………… 41
4.1 實驗說明…………………………………………………… 41
4.2 指標………………………………………………………… 45
第五章 資料性質分析及故障診斷實例…………………………… 47
5.1 正弦模擬訊號……………………………………………… 47
5.2 振動訊號1…………………………………………………… 51
5.3 振動訊號2…………………………………………………… 57
5.4 振幅調製現象之觀察……………………………………… 68
5.5 IMF性質之分析…………………………………………… 73
5.6 故障診斷實例……………………………………………… 85
第六章 結論與未來研究…………………………………………… 91
參考文獻 93

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