Mechanical Fault Diagnosis based on LCD Information Entropy Feature and SVM

Zhang Qiantu; Fang Liqing; Zhao Yulong; Lv Yan

doi:10.16578/j.issn.1004.2539.2015.12.031

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Mechanical Fault Diagnosis based on LCD Information Entropy Feature and SVM

更新时间：2022-10-20

- Mechanical Fault Diagnosis based on LCD Information Entropy Feature and SVM
- Vol. 39, Issue 12, Pages: 144-148(2015)
- 作者机构：
  
  1. 军械工程学院火炮工程系
- 作者简介：
- 基金信息：
- DOI：10.16578/j.issn.1004.2539.2015.12.031
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[1]张前图,房立清,赵玉龙,吕岩.基于LCD信息熵特征和SVM的机械故障诊断[J].机械传动,2015,39(12):144-148.

Zhang Qiantu, Fang Liqing, Zhao Yulong, et al. Mechanical Fault Diagnosis based on LCD Information Entropy Feature and SVM. [J]. 39(12):144-148(2015)
[1]张前图,房立清,赵玉龙,吕岩.基于LCD信息熵特征和SVM的机械故障诊断[J].机械传动,2015,39(12):144-148. DOI： 10.16578/j.issn.1004.2539.2015.12.031.

Zhang Qiantu, Fang Liqing, Zhao Yulong, et al. Mechanical Fault Diagnosis based on LCD Information Entropy Feature and SVM. [J]. 39(12):144-148(2015) DOI： 10.16578/j.issn.1004.2539.2015.12.031.

摘要

提出了一种基于局部特征尺度分解（LCD）信息熵特征和支持向量机（SVM）相结合的机械故障诊断方法。首先采用LCD对振动信号进行分解,得到若干个具有物理意义的内禀尺度分量（ISC）;结合信息熵理论,从时域、频域和时频域3个角度分别定义了时域奇异谱熵、频域功率谱熵以及时频域的特征空间熵、边际谱熵和瞬时能量熵,并将这些熵值组成特征向量;最后通过SVM对特征向量进行分类识别。轴承故障诊断的实例表明,基于LCD信息熵特征和SVM相结合的方法能够准确地对轴承故障信号进行识别,并且效果要好于EMD信息熵特征和SVM结合的方法。

Abstract

A new approach for mechanical fault diagnosis based on local characteristic- scale decomposition（ LCD） information entropy feature and support vector machine（ SVM） is proposed. Firstly,the fault mechanical vibration signal is decomposed by using the LCD to obtain a certain number of intrinsic scale component（ ISC）. Secondly,combined with information entropy theory,the singular spectrum entropy in time domain,power spectrum entropy in frequency domain,feature space entropy,marginal spectrum entropy and momentary energy entropy in time- frequency domain are defined and used as the feature vector. At last,the feature vectors are put into SVM classifier to recognize different fault type. The results of experiment of bearing fault diagnosis demonstrate that the method based on LCD information entropy feature and SVM is able to identify the bearing faults accurately and effectively,and the diagnosis effect is better than the method based on empirical mode decomposition（ EMD） information entropy and SVM.

关键词

局部特征尺度分解信息熵支持向量机特征提取故障诊断

Keywords

Local characteristic-scale decomposition（LCD）Information entropySupport vector machine（SVM）Feature extractionFault diagnosis

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Related Institution

School of Information Management, Wuxi Institute of Communications Technology

IOT Engineering College, Wuxi City College of Vocational Technology

Key Laboratory of Metallurgical Equipment and Control Technology，Ministry of Education，Wuhan University of Science and Technology

Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering，Wuhan University of Science and Technology

Department of Mechatronic Engineering,Liaoning Provincial College of Communications

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