Bivariate empirical mode decomposition and its applications in machine condition monitoring

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

Abstract

Attributed to providing a more realistic representation of the signal without the artifacts imposed by non-adaptive limitations suffered by both Fourierand Wavelet-transform based methods, Empirical Mode Decomposition (EMD) has been widely accepted as a favored tool for interpreting nonlinear, non-stationary signals, which are often associated with the occurrence of faults or variable operations of rotating machinery. In this chapter, the fundamental theory of the EMD will be explained. But more context will be spent on discussing its two dimensional form, namely Bivariate Empirical Mode Decomposition, and the powerful capacity of this innovative technique in the application of machine condition monitoring.

Original languageEnglish
Title of host publicationStructural Health Monitoring
Subtitle of host publicationAn Advanced Signal Processing Perspective
EditorsRuqiang Yan, Xuefeng Chen, Subhas Chandra Mukhopadhyay
PublisherSpringer International Publishing AG
Pages293-319
Number of pages27
Edition1st
ISBN (Electronic)9783319561264
ISBN (Print)9783319561257, 9783319858326
DOIs
Publication statusPublished - 5 May 2017
Externally publishedYes

Publication series

NameSmart Sensors, Measurement and Instrumentation
PublisherSpringer
Volume26
ISSN (Print)2194-8402
ISSN (Electronic)2194-8410

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