Skip to main navigation Skip to search Skip to main content

Mechanical fault diagnosis based on local mean decomposition method

  • China Three Gorges University
  • Xi'an Jiaotong University

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

7 Scopus citations

Abstract

With the effect of periodical impulses, most machinery fault vibration signals are muticomponent modulation signals. The effective decomposition approach is the key to demodulate signals and extract fault characteristics. Local mean decomposition (LMD) is a new kind of time-frequency analysis approach, which can decompose the signals adaptively into a set of product function (PF) components. The envelope of PF is the instantaneous amplitude and instantaneous frequency can be calculated by the derivative of the unwrapped phase of modulation signal with uniform amplitude. The method bypasses the Hilbert transform totally. Therefore, it involves no question about negative frequency of no physical meaning. Through the successful mechanical fault diagnosis examples, it shows that LMD has high theoretical value and engineering practicability.

Original languageEnglish
Title of host publication2009 International Conference on Measuring Technology and Mechatronics Automation, ICMTMA 2009
Pages681-684
Number of pages4
DOIs
StatePublished - 2009
Event2009 International Conference on Measuring Technology and Mechatronics Automation, ICMTMA 2009 - Zhangjiajie, Hunan, China
Duration: 11 Apr 200912 Apr 2009

Publication series

Name2009 International Conference on Measuring Technology and Mechatronics Automation, ICMTMA 2009
Volume1

Conference

Conference2009 International Conference on Measuring Technology and Mechatronics Automation, ICMTMA 2009
Country/TerritoryChina
CityZhangjiajie, Hunan
Period11/04/0912/04/09

Keywords

  • Fault diagnosis
  • Local mean decomposition
  • Modulation signal

Fingerprint

Dive into the research topics of 'Mechanical fault diagnosis based on local mean decomposition method'. Together they form a unique fingerprint.

Cite this