Bearing Fault Diagnosis Based on Multiscale Permutation Entropy and Support Vector Machine

Bearing Fault Diagnosis Based on Multiscale Permutation Entropy and Support Vector Machine

Bearing fault diagnosis has attracted significant attention over the past few decades. It consists of two major parts: vibration signal feature extraction and condition classification for the extracted features. In this paper, multiscale permutation entropy (MPE) was introduced for feature extractio...

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Bibliographic Details
Main Authors: Jian-Jiun Ding, Chun-Chieh Wang, Chiu-Wen Wu, Po-Hung Wu, Shuen-De Wu
Format: Article
Language:English
Published: MDPI AG 2012-07-01
Series:Entropy
Subjects:
<strong> </strong>fault diagnosis
machine vibration
multiscale
permutation entropy
multiscale permutation entropy
support vector machine
Online Access:http://www.mdpi.com/1099-4300/14/8/1343
Description
Summary:Bearing fault diagnosis has attracted significant attention over the past few decades. It consists of two major parts: vibration signal feature extraction and condition classification for the extracted features. In this paper, multiscale permutation entropy (MPE) was introduced for feature extraction from faulty bearing vibration signals. After extracting feature vectors by MPE, the support vector machine (SVM) was applied to automate the fault diagnosis procedure. Simulation results demonstrated that the proposed method is a very powerful algorithm for bearing fault diagnosis and has much better performance than the methods based on single scale permutation entropy (PE) and multiscale entropy (MSE).
ISSN:1099-4300

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