Gear Fault Diagnosis based on Variational Mode Decomposition and ANFIS

Zheng Xiaoxia; Jia Wenhui; Zhou Guowang; Li Jia

doi:10.16578/j.issn.1004.2539.2018.03.031

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Gear Fault Diagnosis based on Variational Mode Decomposition and ANFIS

更新时间：2022-10-20

- Gear Fault Diagnosis based on Variational Mode Decomposition and ANFIS
- Vol. 42, Issue 3, Pages: 149-154(2018)
- 作者机构：
  
  1. 上海电力学院自动化工程学院
- 作者简介：
- 基金信息：
- DOI：10.16578/j.issn.1004.2539.2018.03.031
  CLC：
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[1]郑小霞,贾文慧,周国旺,李佳.基于变分模态分解和ANFIS的齿轮故障诊断[J].机械传动,2018,42(03):149-154.

Zheng Xiaoxia, Jia Wenhui, Zhou Guowang, et al. Gear Fault Diagnosis based on Variational Mode Decomposition and ANFIS. [J]. 42(3):149-154(2018)
[1]郑小霞,贾文慧,周国旺,李佳.基于变分模态分解和ANFIS的齿轮故障诊断[J].机械传动,2018,42(03):149-154. DOI： 10.16578/j.issn.1004.2539.2018.03.031.

Zheng Xiaoxia, Jia Wenhui, Zhou Guowang, et al. Gear Fault Diagnosis based on Variational Mode Decomposition and ANFIS. [J]. 42(3):149-154(2018) DOI： 10.16578/j.issn.1004.2539.2018.03.031.

摘要

针对齿轮振动信号的传递路径复杂,噪声污染严重,故障特征信息微弱等问题,提出了基于变分模态分解和自适应神经模糊推理系统（Adaptive Neuro-Fuzzy Inference System,ANFIS）的故障诊断方法。将原始振动信号利用变分模态分解得到不同尺度的本征模态函数后,通过提取各模态函数的排列熵,构造出表征模态分量信息的特征向量,并将提取的特征向量输入自适应神经模糊推理系统进行训练,建立齿轮故障诊断模型。最后通过齿轮实验故障数据对模型进行验证,并与支持向量机（Support Vector Machine,SVM）识别方法进行对比,结果表明,提出方法具有很强的学习能力,能够有效地对齿轮故障进行诊断,提高故障识别的准确率,识别效果明显优于SVM。

Abstract

In order to solve the fault recognition problems caused by a complex signal transfer path,severe noise pollution and the weak fault features,a method for gear based on variational mode decomposition（ VMD）and adaptive neuro-fuzzy inference system（ ANFIS） is proposed. Firstly,VMD is used to decompose a fault signal into several intrinsic mode functions（ IMFs）,and introduced the permutation entropy to construct the feature vectors characterizing the modal component information. Then the extracted feature vectors are input into the adaptive neuro-fuzzy inference system to establish the fault diagnosis model. Finally,the model is validated by the vibration signal data of the gears and compared with the support vector machine（ SVM） method. The results show that the proposed method has a strong learning ability and it can effectively diagnose the gear fault,improve the accuracy of fault identification. The recognition effect is obviously superior to SVM.

关键词

变分模态分解自适应神经模糊推理系统齿轮排列熵故障诊断

Keywords

Variational mode decompositionAdaptive neuro-fuzzy inference systemGearPermutation entropyFault diagnosis

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