希爾伯特-黃變換(HHT)在旋轉機械之軸承故障診斷的應用

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姓名

傅翰敏(Han-min Fu) 查詢紙本館藏

畢業系所

機械工程學系

論文名稱

希爾伯特-黃變換(HHT)在旋轉機械之軸承故障診斷的應用
(Application of Hilbert-Huang Transform to the Bearing Fault Diagnosis of Rotating Machinery)

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摘要(中)

本篇的主要目的在於使用後處理總體經驗模態分解法來改善之前總體經驗模態分解法的缺點，即模態混雜與端點效應。首先將旋轉機械之振動訊號透過後處理總體經驗模態分解法分解成一組數個無模態混雜且符合內稟模態函數基本條件的內稟模態函數，並選擇其中含有故障特徵的內稟模態函數進行分析。
軸承故障特徵的提取則是將內稟模態函數的包絡線經過希爾伯特轉換後繪出時頻譜，最後計算出希爾伯特邊際譜，觀察是否含有軸承故障特徵頻率以達到故障檢測目的，本篇除了檢測軸承故障的種類外，並將進行軸承故障程度上的辨別。

摘要(英)

In order to improve the drawbacks of Ensemble Empirical Mode Decomposition (EEMD), such as mode mixing and end effect problem, we present an improved HHT approach based on post-processing of EEMD to solve the problem in this paper. Once the Intrinsic Mode Functions (IMFs) are obtained from the decomposition process, the crucial step is to extract the fault features from the information-contained IMFs.
The amplitude modulation (AM) phenomenon can be discovered in the IMFs with fault information. In this paper, we not only classify the types of bearing fault but also identify the level of the fault.

關鍵字(中)

★ 希爾伯特-黃變換
★ 故障診斷

關鍵字(英)

★ HHT
★ fault diagnosis

論文目次

摘要 i
ABSTRACT ii
ACKNOWLEDGEMENT iii
LIST OF TABLES v
LIST OF FIGURES vi
1. INTRODUCTION 1
1.1 Motivation 1
1.2 Literature Survey 2
2. BACKGROUND INFORMATION 4
2.1 Hilbert-Huang Transform (HHT) 4
2.2 Empirical Mode Decomposition (EMD) 4
2.3 Ensemble EMD, EEMD and post-processing of EEMD 5
2.4 Envelope Analysis and Marginal Hilbert Spectrum 7
3. EXPERIMENTAL VERIFICATION 9
4. CONCLUSIONS 12
5. FUTURE WORK 13
REFERENCES 14

參考文獻

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Web site: http://rcada.ncu.edu.tw/
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指導教授

黃以玫、吳天堯
(Yi-mei Huang、Tian-yau Wu)

審核日期

2010-7-28

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