A new Ensemble Empirical Mode Decomposition (EEMD) is presented. This new approach consists of sifting an ensemble of white noise-added signal (data) and treats the mean as the final true result. Finite, not infinitesimal, amplitude white noise is necessary to force the ensemble to exhaust all possible solutions in the sifting process, thus making the different scale signals to collate in the proper intrinsic mode functions (IMF) dictated by the dyadic filter banks. As EEMD is a time–space analysis method, the added white noise is averaged out with sufficient number of trials; the only persistent part that survives the averaging process is the component of the signal (original data), which is then treated as the true and more physical meaningful answer. The effect of the added white noise is to provide a uniform reference frame in the time–frequency space; therefore, the added noise collates the portion of the signal of comparable scale in one IMF. With this ensemble mean, one can separate scales naturall...

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Ensemble empirical mode decomposition: a noise-assisted data analysis method

We survey the newly developed Hilbert spectral analysis method and its applications to Stokes waves, nonlinear wave evolution processes, the spectral form of the random wave field, and turbulence. Our emphasis is on the inadequacy of presently available methods in nonlinear and nonstationary data analysis. Hilbert spectral analysis is here proposed as an alternative. This new method provides not only a more precise definition of particular events in time-frequency space than wavelet analysis, but also more physically meaningful interpretations of the underlying dynamic processes.

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A new view of nonlinear water waves: the Hilbert spectrum

Rolling element bearing diagnostics—A tutorial

A review on empirical mode decomposition in fault diagnosis of rotating machinery

Support vector machine in machine condition monitoring and fault diagnosis

The synchronous (time domain) average revisited

The Extraction of Periodic Waveforms by Time Domain Averaging

Altered heart rate variability in panic disorder patients

Described is a vibration based diagnostic method aimed at detecting localized defects developing in roller/ball bearings. The signature is decomposed into generalized periodic functions and a search strategy for the various components developed and implemented. The method is shown to be applicable for cases where spectral analysis fails to give results.

Simon Braun

Papers

The synchronous (time domain) average revisited

The Extraction of Periodic Waveforms by Time Domain Averaging

Altered heart rate variability in panic disorder patients

Analysis of Roller/Ball Bearing Vibrations

Decomposition of non-stationary signals into varying time scales: Some aspects of the EMD and HVD methods