A Tour of the Tour of the Hilbert-Huang Transform: An Empirical Tool for Signal Analysis

The HHT provides an alternative tool for signal analysis. Case studies in applying the HHT technique for bearing degradation monitoring and machine tool breakage detection have demonstrated its effectiveness for revealing the non-stationary and non-linear features hidden in dynamic signals. In addition to the application illustrated in this article, the HHT technique has shown to be effective in other applications, such as biomedical engineering [8], [9], system identification [10], [11], environmental monitoring [12], or financial analysis [13]. Because of its empirical nature, rigorous mathematical proof of this technique has remained an active research topic. More interesting reports are to be expected on the application of this technique for solving various types of real-world problems.

[1]  N. Huang,et al.  System identification of linear structures based on Hilbert–Huang spectral analysis. Part 1: normal modes , 2003 .

[2]  N. Huang,et al.  The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis , 1998, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[3]  Norden E. Huang,et al.  Anatomy of plasma structures in an equatorial spread F event , 2001 .

[4]  S. S. Shen,et al.  Applications of Hilbert–Huang transform to non‐stationary financial time series analysis , 2003 .

[5]  D. Menicucci,et al.  Deriving the respiratory sinus arrhythmia from the heartbeat time series using empirical mode decomposition , 2003, q-bio/0310002.

[6]  J. Bendat,et al.  Random Data: Analysis and Measurement Procedures , 1987 .

[7]  Robert X. Gao,et al.  Hilbert–Huang Transform-Based Vibration Signal Analysis for Machine Health Monitoring , 2006, IEEE Transactions on Instrumentation and Measurement.

[8]  N. Huang,et al.  A new view of nonlinear water waves: the Hilbert spectrum , 1999 .

[9]  Hualou Liang,et al.  Application of the empirical mode decomposition to the analysis of esophageal manometric data in gastroesophageal reflux disease , 2005, IEEE Transactions on Biomedical Engineering.

[10]  S. S. Shen,et al.  A confidence limit for the empirical mode decomposition and Hilbert spectral analysis , 2003, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[11]  Yonghong Peng,et al.  Empirical Model Decomposition Based Time-Frequency Analysis for the Effective Detection of Tool Breakage , 2006 .

[12]  S. Hahn Hilbert Transforms in Signal Processing , 1996 .