Condition Identification of Gears based on CEEMDAN Energy Entropy

Dou Chunhong; Zhao Guangsheng; Kou Xinglei

doi:10.16578/j.issn.1004.2539.2018.01.022

您当前的位置：

首页 >

文章列表页 >

Condition Identification of Gears based on CEEMDAN Energy Entropy

更新时间：2022-10-20

- Condition Identification of Gears based on CEEMDAN Energy Entropy
- Vol. 42, Issue 1, Pages: 102-105(2018)
- 作者机构：
  
  1. 潍坊学院信息与控制工程学院
  2. 山东交通职业技术学院
  3. 山东寿光巨能金玉米开发有限公司
- 作者简介：
- 基金信息：
- DOI：10.16578/j.issn.1004.2539.2018.01.022
  CLC：
扫描看全文
[1]窦春红,赵光胜,寇兴磊.基于CEEMDAN能量熵的齿轮状态识别[J].机械传动,2018,42(01):102-105.

Dou Chunhong, Zhao Guangsheng, Kou Xinglei. Condition Identification of Gears based on CEEMDAN Energy Entropy. [J]. 42(1):102-105(2018)
[1]窦春红,赵光胜,寇兴磊.基于CEEMDAN能量熵的齿轮状态识别[J].机械传动,2018,42(01):102-105. DOI： 10.16578/j.issn.1004.2539.2018.01.022.

Dou Chunhong, Zhao Guangsheng, Kou Xinglei. Condition Identification of Gears based on CEEMDAN Energy Entropy. [J]. 42(1):102-105(2018) DOI： 10.16578/j.issn.1004.2539.2018.01.022.

摘要

集合经验模式分解（Ensemble Empirical Mode Decomposition,EEMD）存在着辅助白噪声难以消除和容易产生虚假模式的缺陷。针对EEMD方法在齿轮箱故障信号处理中的不足,将自适应噪声完备集合经验模式分解（Complete Ensemble Empirical Mode Decomposition with Adaptive Noise,CEEMDAN）应用于齿轮故障信号分析,提出了基于CEEMDAN能量熵的齿轮状态识别方法。该方法首先利用CEEMDAN分解齿轮振动信号,然后计算振动信号分解结果的能量熵,将能量熵作为特征参数来区分不同的齿轮运行状态。将该方法用于区分正常、轻度刮伤和中度刮伤齿轮运行状态,并与基于EMDEEMD能量熵的方法进行了对比。结果表明,该方法可以有效地区分相近的齿轮运行状态,与其他几种方法相比具有明显的优势。

Abstract

The Ensemble Empirical Mode Decomposition（EEMD） often encounters two difficulies in removing the added white noise residing in extracted components and easy production of spurious modes. Aiming at the deficiencies in the EEMD,the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise（CEEMDAN） is introduced to examine gearbox fault data and a method for condition identification of gearboxes based on CEEMDAN energy entropy is proposed. In the proposed method,the gearbox vibration signal is decomposed by using the CEEMDAN,then the energy entropy of the decomposition results is calculated and the energy entropy is taken as a characteristic parameter to identify different gear operating condition. Afterwards,the proposed method is used to discriminate between normal,slight-scratch and medium-scratch gear operating conditions,and compared with the method based on EMD \ EEMD energy entropy. The results show that the proposed method can effectively discriminate between these three similar gear operating conditions,and the proposed method has a clear advantage in condition identification of gearboxes.

关键词

自适应噪声完备集合经验模式分解信号处理齿轮状态识别

Keywords

CEEMDANSignal processingGearCondition identification

references

Views

235

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Application of Adaptive Local Iterative Filtering and Fuzzy Entropy in Gear Fault Identification

Study on Tooth Surface Roughness Modeling of Form Grinding Considering the Change of the Local Grinding Condition

Analysis of Gear Bulk Temperature Field and Transmission Errors Based on Workbench

Discussion on Allowable Values of Deviations with ISO 1328-1:2013 and ISO 1328-2:2020

Effect of Ion Nitriding on Gear Accuracy

Related Author

No data

Related Institution

Key Laboratory of Mechanical Performance Analysis and Optimization of Plateau in Yunnan Province， College of Engineering，Honghe University， Mengzi

AECC ZhongChuan Transmission Machinery Co., Ltd.

State Key Laboratory of High-Performance Complex Manufacturing, Central South University

Faculty of Materials and Manufacturing, Beijing University of Technology

Changcheng Institute of Metrology & Measurement, AVIC

⁰