基于改进型ESMD和动力学模型的齿轮箱冲击特征提取方法研究

谷晟; 别锋锋; 缪新婷; 赵威; 郭越

doi:10.16578/j.issn.1004.2539.2023.01.022

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基于改进型ESMD和动力学模型的齿轮箱冲击特征提取方法研究

开发应用 | 更新时间：2023-06-07

- 基于改进型ESMD和动力学模型的齿轮箱冲击特征提取方法研究
- Research on Gearbox Impact Feature Extraction Method Based on the Improved ESMD and Dynamics Model
- 机械传动 2023年47卷第1期页码：155-162
- 作者机构：
  
  常州大学机械工程学院，江苏常州 213164
- 作者简介：
  
  谷晟（1992— ），男，江苏淮安人，硕士研究生；研究方向为机械设备状态检测与故障诊断，振动信号处理与分析；gushengxp@sina.cn。
- 基金信息：
  
  国家自然科学基金(52105141);江苏省高等学校自然科学研究重大项目(19KJA430004)
- DOI：10.16578/j.issn.1004.2539.2023.01.022
  中图分类号：
- 纸质出版日期：2023-01-15，
  
  收稿日期：2021-12-22，
  
  修回日期：2022-02-09，
扫描看全文
谷晟,别锋锋,缪新婷等.基于改进型ESMD和动力学模型的齿轮箱冲击特征提取方法研究[J].机械传动,2023,47(01):155-162.

Gu Sheng,Bie Fengfeng,Miao Xinting,et al.Research on Gearbox Impact Feature Extraction Method Based on the Improved ESMD and Dynamics Model[J].Journal of Mechanical Transmission,2023,47(01):155-162.
谷晟,别锋锋,缪新婷等.基于改进型ESMD和动力学模型的齿轮箱冲击特征提取方法研究[J].机械传动,2023,47(01):155-162. DOI： 10.16578/j.issn.1004.2539.2023.01.022.

Gu Sheng,Bie Fengfeng,Miao Xinting,et al.Research on Gearbox Impact Feature Extraction Method Based on the Improved ESMD and Dynamics Model[J].Journal of Mechanical Transmission,2023,47(01):155-162. DOI： 10.16578/j.issn.1004.2539.2023.01.022.

摘要

齿轮箱振动信号具有非线性冲击特征，其有效特征信息易于被振动信号其他干扰成分所淹没。针对如何有效提取其冲击特征这一热点和难点问题，通过构建直齿锥齿轮动力学模型，研究其典型故障振动机理，提出了一种基于改进型极点对称模态分解（ESMD）和支持向量机（SVM）相结合的故障诊断方法。该方法通过改进型ESMD将振动信号自适应分解为多个IMF分量，然后利用最大峭度-包络谱指标选取一定量的分量并提取每个分量的奇异值，构建特征向量集合并输入SVM进行故障模式识别。动力学仿真模拟和齿轮箱实验研究表明，改进型ESMD-SVM法能够有效提取并识别齿轮箱故障信息。

Abstract

The gearbox vibration signal contains nonlinear impact characteristics

and the significant feature information tends to be overwhelmed with other interference components. Aiming at the key issue of how to effectively extract its impact characteristics

a fault diagnosis method based on an improved extreme symmetric mode decomposition (ESMD) and support vector machine (SVM) as well as a dynamics model of spur bevel gear for investigating typically gearbox fault mechanism is proposed. In this method

the vibration signal is adaptively decomposed into multiple IMF components by the improved ESMD

and then a certain number of components are selected with the maximum kurtosis-envelope spectrum index. Meanwhile

the singular values of each selected IMF are extracted to construct the feature vector set

which is input into the SVM for the fault pattern recognition finally. Dynamic simulation and gearbox experimental research show that the improved ESMD-SVM method can extract and identify gearbox fault information effectively.

关键词

齿轮箱改进型ESMD动力学分析模式识别

Keywords

GearboxImproved ESMDDynamics analysisPattern recognition

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