Machine Condition Classification by Using Wavelet Packet Decomposition and Multi-Scale Entropy

Hong Kun Li; Shu Ai Zhou; Yu Zhen Chen

doi:10.4028/www.scientific.net/AEF.2-3.743

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The Model of Equipment Integrated Maintenance Management Based on MES
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HomeAdvanced Engineering ForumAdvanced Engineering Forum Vols. 2-3Machine Condition Classification by Using Wavelet...

Machine Condition Classification by Using Wavelet Packet Decomposition and Multi-Scale Entropy

Abstract:

A new condition classification method is put forward based on the analysis of vibration signals. Machine working condition can be recognized by the combination of wavelet packet decomposition (WPD) and multi-scale entropy (MSE). Firstly, vibration signal of machine is decomposed by wavelet packet with the appropriate decomposition layer. Then, each sub-signal in different frequency band is analyzed with the multi-scale entropy. Through analyzing the multi-scale entropy distribution curves of sub-signals for different operating conditions in each frequency band, entropy of certain frequency bands and scales will be chosen as the feature vector, which is used to distinguish different machine conditions. This method presents a novel perspective for rolling bearing default diagnosis and is tested to be very effective to classify different bearing operating conditions through series of experiments.

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Periodical:

Advanced Engineering Forum (Volumes 2-3)

Pages:

743-748

DOI:

https://doi.org/10.4028/www.scientific.net/AEF.2-3.743

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Online since:

December 2011

Authors:

Hong Kun Li, Shu Ai Zhou, Yu Zhen Chen

Keywords:

Condition Classification, Multi-Scale Entropy, Rolling Bearing, Wavelet Packet Decomposition

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Creative Commons CC BY 4.0

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