Fault Feature Extraction of Gear Pitting based on AR-MCKD

Lü Hongqiang; Wu Zhifei; Wang Tie; Gu Fengshou

doi:10.16578/j.issn.1004.2539.2017.03.022

您当前的位置：

首页 >

文章列表页 >

Fault Feature Extraction of Gear Pitting based on AR-MCKD

更新时间：2022-10-20

- Fault Feature Extraction of Gear Pitting based on AR-MCKD
- Vol. 41, Issue 3, Pages: 109-113(2017)
- 作者机构：
  
  1. 太原理工大学齿轮研究所
  2. 哈德斯菲尔德大学计算与工程学院
- 作者简介：
- 基金信息：
- DOI：10.16578/j.issn.1004.2539.2017.03.022
  CLC：
扫描看全文
[1]吕宏强,武志斐,王铁,谷丰收.基于AR-MCKD的齿轮点蚀故障特征提取[J].机械传动,2017,41(03):109-113.

Lü Hongqiang, Wu Zhifei, Wang Tie, et al. Fault Feature Extraction of Gear Pitting based on AR-MCKD. [J]. 41(3):109-113(2017)
[1]吕宏强,武志斐,王铁,谷丰收.基于AR-MCKD的齿轮点蚀故障特征提取[J].机械传动,2017,41(03):109-113. DOI： 10.16578/j.issn.1004.2539.2017.03.022.

Lü Hongqiang, Wu Zhifei, Wang Tie, et al. Fault Feature Extraction of Gear Pitting based on AR-MCKD. [J]. 41(3):109-113(2017) DOI： 10.16578/j.issn.1004.2539.2017.03.022.

摘要

以实测齿轮箱振动信号为分析对象,对齿轮点蚀进行故障特征提取。利用最小信息准则（AIC）确定自回归（AR）模型最优阶数,通过此AR模型将采集到的振动信号进行预处理,降低可线性预测的平稳成分;利用最大相关峭度解卷积（MCKD）进一步增强振动信号中的冲击成分,然后进行Hilbert变换得到振动信号的包络谱来分析故障特征。将上述方法应用到试验振动信号包络谱的变化趋势分析。结果表明,AR-MCKD能够有效提取齿轮点蚀故障特征,能够体现齿轮点蚀过程的包络谱变化。

Abstract

Taking the measured gearbox vibration signals as the analysis object,the fault feature of gear pitting is extracted. The Akaike Information Ceriterion（ AIC） is applied to evaluate the optimum Auto- regressive（ AR） model order,and then this AR model is used to pre- process the vibration signals to eliminate the linearly predictable stationary part. The Maximum Correlated Kurtosis Deconvolution（ MCKD） is applied to enhance the impact component. The fault characteristic could be identified by analyzing the envelope spectrum.And the AR- MCKD method is applied to analyze the changing trend of the vibration signals. The results proved the efficiency of the AR- MCKD method in the gear fault feature extraction and the changing procedure of the envelope spectrum of gear pitting fault.

关键词

齿轮故障诊断AR模型最大相关峭度解卷积包络谱

Keywords

Gear fault diagnosisAR modelMaximum correlated kurtosis deconvolutionEnvelope spectrum

references

Views

224

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Gear Fault Diagnosis Method Based on Deep Transfer Learning

Gear Fault Diagnosis based on Distribution Adaptation Layer and Soft Label Learning

Gear Fault Diagnosis Method based on Multi-domain Feature and Improved D-S Evidence Theory

Degradation Assessment of the Rolling Bearing Performance based on AR-FCM

Related Author

No data

Related Institution

School of Aircraft Engineering, Nanchang Hangkong University

School of Navigation, Nanchang Hangkong University

School of Aircraft Engineering， Nanchang Hangkong University

School of Navigation， Nanchang Hangkong University

Aviation Key Laboratory of Science and Technology on Fault Diagnosis and Health Management

⁰