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Semi-automated diagnosis of bearing faults based on a hidden Markov model of the vibration signals

  • Published:2018-10 Issue: Volume:127 Page:141-166
  • ISSN:0263-2241
  • Container-title:Measurement
  • language:en
  • Short-container-title:Measurement

Author:

Xin GeORCID,Hamzaoui Nacer,Antoni JeromeORCID

Funder

Fundamental Research Funds for the Central Universities

National Key Research and Development Program of China

China Scholarship Council

Publisher

Elsevier BV

Subject

Applied Mathematics,Electrical and Electronic Engineering,Condensed Matter Physics,Instrumentation

Reference80 articles.

1. Rolling element bearing diagnostics-a tutorial;Randall;Mech. Syst. Signal Process.,2011

2. Rolling element bearing diagnostics using the Case Western Reserve University data: a benchmark study;Smith;Mech. Syst. Signal Process.,2015

3. Cyclostationary modelling of rotating machine vibration signals;Antoni;Mech. Syst. Signal Process.,2004

4. B. Boashash, Time-Frequency Signal Analysis and Processing, second ed., Oxford, 2016.

5. Recent advances in time–frequency analysis methods for machinery fault diagnosis: a review with application examples;Feng;Mech. Syst. Signal Process.,2013

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