Signal feature extraction based on cascaded multi-stable stochastic resonance denoising and EMD method

Signal feature extraction based on cascaded multi-stable stochastic resonance denoising and EMD... On the basis of cascaded multi-stable stochastic resonance system (CMSRS) theoretical studies, for the empirical mode decomposition (EMD) in heavy noisy mixtures, a method of EMD based on CMSRS denoising is presented. First, CMSRS is employed as the pretreatment to remove noise by virtue of its good effect in denoising performance, and the energy gradually is shifted from high to low frequency, then the denoised signal is decomposed by EMD. In simulated experiment, EMD is used to decompose the original and CMSRS output signals respectively. The result from the comparison shows that this method, not only removes high-frequency noise efficiently, but also reduces the decomposition layers and lets them have more reality meanings. At last, a diagnosis on the fault of inner race of rolling bearing confirms that this method removes high-frequency noise step by step, improves low-frequency signal’s energy, and can effectively identify characteristic signals. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Measurement Elsevier

Signal feature extraction based on cascaded multi-stable stochastic resonance denoising and EMD method

Loading next page...
 
/lp/elsevier/signal-feature-extraction-based-on-cascaded-multi-stable-stochastic-0V2ia60hAJ
Publisher
Elsevier
Copyright
Copyright © 2016 Elsevier Ltd
ISSN
0263-2241
eISSN
1873-412X
D.O.I.
10.1016/j.measurement.2016.04.073
Publisher site
See Article on Publisher Site

Abstract

On the basis of cascaded multi-stable stochastic resonance system (CMSRS) theoretical studies, for the empirical mode decomposition (EMD) in heavy noisy mixtures, a method of EMD based on CMSRS denoising is presented. First, CMSRS is employed as the pretreatment to remove noise by virtue of its good effect in denoising performance, and the energy gradually is shifted from high to low frequency, then the denoised signal is decomposed by EMD. In simulated experiment, EMD is used to decompose the original and CMSRS output signals respectively. The result from the comparison shows that this method, not only removes high-frequency noise efficiently, but also reduces the decomposition layers and lets them have more reality meanings. At last, a diagnosis on the fault of inner race of rolling bearing confirms that this method removes high-frequency noise step by step, improves low-frequency signal’s energy, and can effectively identify characteristic signals.

Journal

MeasurementElsevier

Published: Aug 1, 2016

References

You’re reading a free preview. Subscribe to read the entire article.


DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 12 million articles from more than
10,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Unlimited reading

Read as many articles as you need. Full articles with original layout, charts and figures. Read online, from anywhere.

Stay up to date

Keep up with your field with Personalized Recommendations and Follow Journals to get automatic updates.

Organize your research

It’s easy to organize your research with our built-in tools.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

Monthly Plan

  • Read unlimited articles
  • Personalized recommendations
  • No expiration
  • Print 20 pages per month
  • 20% off on PDF purchases
  • Organize your research
  • Get updates on your journals and topic searches

$49/month

Start Free Trial

14-day Free Trial

Best Deal — 39% off

Annual Plan

  • All the features of the Professional Plan, but for 39% off!
  • Billed annually
  • No expiration
  • For the normal price of 10 articles elsewhere, you get one full year of unlimited access to articles.

$588

$360/year

billed annually
Start Free Trial

14-day Free Trial