DYNAMIC MODELING AND SIMULATION OF ROLLING BEARING WITH PITTING FAILURE

ZHANG HuiLing; LV FuLing

doi:10.16579/j.issn.1001.9669.2019.04.009

您当前的位置：

首页 >

文章列表页 >

DYNAMIC MODELING AND SIMULATION OF ROLLING BEARING WITH PITTING FAILURE

更新时间：2022-09-22

- DYNAMIC MODELING AND SIMULATION OF ROLLING BEARING WITH PITTING FAILURE
- Journal of Mechanical Strength Vol. 41, Issue 4, Pages: 821-827(2019)
- 作者机构：
  
  1. 新乡职业技术学院
- 作者简介：
- 基金信息：
- DOI：10.16579/j.issn.1001.9669.2019.04.009
  CLC：
扫描看全文
ZHANG HuiLing, LV FuLing. DYNAMIC MODELING AND SIMULATION OF ROLLING BEARING WITH PITTING FAILURE. [J]. 41(4):821-827(2019)
DOI：

ZHANG HuiLing, LV FuLing. DYNAMIC MODELING AND SIMULATION OF ROLLING BEARING WITH PITTING FAILURE. [J]. 41(4):821-827(2019) DOI： 10.16579/j.issn.1001.9669.2019.04.009.

摘要

轴承是机械传动系统中最关键也是最易发生故障的部位之一,准确的识别其故障是实现设备稳定运行的关键。综合考虑油膜和滚动体滑动等非线性因素,建立了包含局部故障的滚动轴承系统五自由度动力学模型,提出了一种改进的点蚀故障模型,并对不同故障参数对应的模型进行了分析。仿真了内圈和外圈含点蚀故障时的系统振动信号并分析了相应的故障特征。通过仿真信号和实验信号的对比分析,验证了所建模型的正确性,为实现轴承故障的早期诊断提供了帮助。

Abstract

Bearing is one of the most critical and the most vulnerable parts of mechanical transmission system,Accurate identification of its faults is the key to achieve stable operation of equipment. The five degree of freedom dynamic model of rolling bearing system with local fault is established by considering the nonlinear factors such as oil film and the sliding of rolling element. An improved model of pitting failure is proposed, and the corresponding models of different fault parameters are analyzed. The system vibration signal of the inner ring and the outer ring with pitting fault is simulated, and the corresponding fault characteristics are analyzed. Through the comparative analysis of the simulation signal and experimental signal, the correctness of the model is verified, which can help to realize the early diagnosis of bearing fault.

关键词

滚动轴承点蚀动力学故障诊断

Keywords

Rolling bearingPittingDynamicsFault diagnosis

references

Views

800

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

ROLLING BEARING FAULT DIAGNOSIS BASED ON FUSION CNN AND PSO-SVM

ROTATING MACHINERY FAULT DIAGNOSIS BASED ON TWO-DIMENSIONAL CONVOLUTION NEURAL NETWORK

APPLICATION OF COMPRESSIVE SENSING AND IMPROVED DEEP WAVELET NEURAL NETWORK IN BEARING FAULT DIAGNOSIS

ROLLING BEARING FAULT FEATURE EXTRACTION RESEARCH BASED ON IMPROVED CEEMDAN AND RECONSTRUCTION

METHOD OF FAULT FEATURE EXTRATION BASED ON CEEMD AND FASTICA

Related Author

No data

Related Institution

College of Engineering Science and Technology,Shanghai Ocean University

Mechanical and Automotive Engineering,Qingdao University of Technological

School of Mechanical-electronic and Automobile Engineering,Beijing University of Civil Engineering and Architecture

Beijing Engineering Research Center of Monitoring for Construction Safety

Faculty of Mechanical ＆ Electrical Engineering,Kunming University of Science ＆ Technology

⁰