Work

New approaches to pattern discovery in signals via empirical mode decomposition

Year: 2017

Type: article

Abstract: 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.

Source: Vibroengineering PROCEDIA

DOI: 10.21595/vp.2017.18676

Authors: Dmitry Klionskiy, Mikhail Kupriyanov, Dmitry Kaplun, Alexander Voznesenskiy

Institution: Saint Petersburg State Electrotechnical University

Language: English

Topic: Machine Fault Diagnosis Techniques

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: diamond

Funder: Ministry of Education and Science of the Russian Federation

Award: G3191953174