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。

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.

[1] Norden E. Huang, Zheng Shen, Steven R. Long, Manli C. Wu, Hsing H. Shih, Quanan Zheng, Nai-Chyuan Yen,Chi Chao Tung, Henry H. Liu, “The empirical mode decomposition and Hilbert spectrum for nonlinear and non-stationary time series analysis,” Proceedings of the Royal Society London A, Vol. 454, pp.903–995, 1998.
[2] Norden E. Huang, Class note of “Introduction to HHT”, Research Center for Adaptive Data Analysis, National Central University, 2008.
Web site: http://rcada.ncu.edu.tw/
[3] Yu Yang, Dejie Yu, Junsheng Cheng, “A roller bearing fault diagnosis method based on EMD energy entropy and ANN,” Journal of Sound and vibration, Vol.294, pp.269-277, 2006.
[4] Ruqiang Yan, Robert X. Gao, “Hilbert-Huang Transform-Based vibration signal analysis for machine health monitoring,” IEEE Transactions on instrumentation and measurement, Vol. 55, No. 6, 2006.
[5] Yu Yang, Yigang He, Junsheng Cheng, Dejie Yu, “A gear fault diagnosis using Hilbert spectrum based on MODWPT and a comparison with EMD approach,” Measurement, Vol. 42, pp.542-551, 2009.
[6] Dejie Yu, Junsheng Cheng, Yu Yang, “Application of EMD method and Hilbert Spectrum to the diagnosis of roller bearings,” Mechanical Systems and Signal Processing, Vol. 19, pp.259-270, 2003.
[7] Yu Yang, Dejie Yu, Junsheng Cheng, “A fault diagnosis approach for roller bearing based on IMF envelope spectrum and SVM,” Measurement, Vol. 40, pp.943-950, 2006.
[8] Junsheng Cheng, Dejie Yu, Yu Yang, “A fault diagnosis approach for roller bearings based on EMD method and AR model,” Mechanical Systems and Signal Processing, Vol. 20, pp.350-362, 2006.
[9] Dejie Yu, Yu Yang, Junsheng Cheng, “Application of time-frequency entropy method based on Hilbert-Huang transform to gear fault diagnosis,” Measurement, Vol. 40, pp.823-830, 2007.
[10] 于德介、程軍聖、楊宇編，機械故障診斷的Hilbert-Huang變換方法，
科學出版社。(2006)
[11] Wu, Z. and Huang, N. E., “Ensemble Empirical Mode Decomposition: a noise-assisted data analysis method,” Advances in Adaptive Data Analysis, Vol. 1, No. 1, pp. 1-41, 2009.
[12] Yaguo Lei, Zhengjia He, Yanyang Zi, “Application of the EEMD method to rotor fault diagnosis of rotating machinery,” Mechanical Systems and Signal Processing, Vol. 23, pp.1327-1338, 2009.
[13] Yaguo Lei, Ming J. Zuo, “Fault diagnosis of rotating machinery using an improved HHT based on EEMD and sensitive IMFs,” Measurement Science and Technology, Vol. 20, 125701(12pp), 2009.
[14] T Y Wu, Y L Chung, “Misalignment diagnosis of rotating machinery through vibration analysis via the hybrid EEMD and EMD approach,” Smart Materials and Structures, Vol. 18, 095004(13pp), 2009.
[15] M.P. Norton, Fundamental of Noise and Vibration Analysis for Engineers, Cambridge University Press, 1989.
[16] Patrick Flandrin, Gabriel Rilling, Paulo Gon?alv?s, “Empirical mode decomposition as a filter bank”, SIGNAL PROCESSING LETTERS, Vol. 11, No. 2, 2004