New approaches to pattern discovery in signals via empirical mode decomposition

Empirical mode decomposition (EMD) is an adaptive, data-driven technique for processing and analyzing various types of non-stationary vibrational signals. EMD is a powerful and effective tool for signal preprocessing (denoising, detrending, regularity estimation) and time-frequency analysis. This paper discusses pattern discovery in signals via EMD. New approaches to this problem are introduced. In addition, the methods expounded here may be considered as a way of denoising and coping with the redundancy problem of EMD. A general classification of intrinsic mode functions (IMFs) in accordance with their physical interpretation is offered and an attempt is made to perform classification on the basis of the regression theory, special classification statistics and a clustering algorithm. The main advantage of the suggested techniques is their capability of working automatically. Simulation studies have been undertaken on multiharmonic vibrational signals.