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Title of Paper:

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

Indexed by:

会议论文

Date of Publication:

2012-01-01

Included Journals:

CPCI-S

Document Type:

A

Volume:

2-3

Page Number:

743-748

Key Words:

Wavelet Packet Decomposition; Multi-scale Entropy; Rolling Bearing; Condition Classification

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.