基于改进蜣螂优化算法的轴承数字孪生模型的动态故障注入研究

doi:10.3901/JME.260097

机械工程学报 ›› 2026, Vol. 62 ›› Issue (3): 446-457.doi: 10.3901/JME.260097

• 机械动力学 • 上一篇

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基于改进蜣螂优化算法的轴承数字孪生模型的动态故障注入研究

刘素艳^1,2, 董一林², 乔一鸣², 马增强^1,2,3

1. 石家庄铁道大学河北省交通电力网智能融合技术与装备协同创新中心石家庄 050043;
2. 石家庄铁道大学电气与电子工程学院石家庄 050043;
3. 省部共建交通工程结构力学行为与系统安全国家重点实验室石家庄 050043

修回日期:2025-03-28 接受日期:2025-07-25 发布日期:2026-03-25
作者简介:刘素艳,女,1982年出生,博士,讲师,硕士研究生导师。主要研究方向为基于数据驱动的滚动轴承故障诊断。E-mail:liusuyan@stdu.edu.cn
马增强(通信作者),男,1975年出生,博士,教授,博士研究生导师。主要研究方向为轨道车辆安全运行状态监测与故障诊断。E-mail:mzqlunwen@126.com

Research on Dynamic Fault Injection of Bearing Digital Twin Model Based on Improved Dung Beetle Optimization Algorithm

LIU Suyan^1,2, DONG Yilin², QIAO Yiming², MA Zengqiang^1,2,3

1. Hebei Province Transportation Power Grid Intelligent Fusion Technology and Equipment Collaborative Innovation Center, Shijiazhuang Tiedao University Shijiazhuang 050043;
2. School of Electrical and Electronic Engineering, Shijiazhuang Tiedao University, Shijiazhuang 050043;
3. State Key Laboratory of Mechanical Behavior and System Safety of Traffic Engineering Structures Jointly Built by the Provincial and Ministerial Departments, Shijiazhuang 050043

Revised:2025-03-28 Accepted:2025-07-25 Published:2026-03-25
Supported by:
国家自然科学基金资助项目(52205571,12072207)。

摘要/Abstract

摘要： 在实际应用中，故障样本的缺乏和故障样本类别的不平衡往往限制了诊断的有效性。为了解决轴承故障样本不平衡的问题，文中设计了一种基于动态故障注入的数字孪生系统，该系统不仅能够反映正常运行状态，还能生成不同故障的样本。系统分为代理模型部分和参数识别部分：代理模型部分采用故障点冲击响应的二自由轴承动力学模型，利用四阶变步长龙格－库塔法进行仿真计算，生成模拟振动数据；参数识别部分将原算法与Chebyshev混沌映射、黄金正弦策略与自适应权重因子方法相结合并改进其适应度函数，提出改进的蜣螂优化算法，该算法可以通过实测振动数据识别动力学参数，构建数字孪生系统，实现动态故障注入。通过实验证明，该系统生成的故障数据相比于使用逆物理信息神经网络、CycleGAN和GAN生成的数据，具有更高的准确率。另外，通过该系统扩充外圈故障数据后，诊断模型的整体诊断准确率提高了8.9%，外圈故障的诊断准确率提高了26.7%，分别达到99.8%和99.5%，为滚动轴承故障诊断中故障样本不均衡问题提供一定的参考。

关键词: 数字孪生, 轴承动力学模型, 改进蜣螂优化算法, 动态故障注入, 样本不平衡

Abstract: In practical applications, the lack of fault samples and the imbalance of fault sample categories often limit the effectiveness of diagnosis. In order to solve the problem of unbalanced bearing fault samples, a digital twin system based on dynamic fault injection is designed, which can not only reflect the normal operation state, but also generate samples of different faults. The system is divided into an agent model part and a parameter identification part: the agent model part adopts a two-freedom bearing dynamics model of the impact response at the fault point, and uses the fourth-order variable-step-length Lunger-Kutta method to perform simulation calculations and generate the simulated vibration data; the parameter identification part combines the original algorithm with the Chebyshev chaotic mapping, the golden sinusoidal strategy, and the adaptive weighting factor method and improves its fitness function to propose an improved dung beetle optimisation algorithm, which can identify the dynamic parameters through the measured vibration data, construct a digital twin system, and realise dynamic fault injection. It is proved through experiments that the fault data generated by this system has a higher accuracy rate compared with the data generated using inverse physical information neural network, CycleGAN and GAN. In addition, after expanding the outer ring fault data by this system, the overall diagnostic accuracy of the diagnostic model is increased by 8.9%, and the diagnostic accuracy of the outer ring fault is increased by 26.7% to 99.8% and 99.5%, respectively, which provides a certain reference for the problem of unbalanced fault samples in the fault diagnosis of rolling bearings.

Key words: digital twin, bearing dynamics model, improved dung beetle optimisation algorithm, dynamic fault injection, sample imbalance

中图分类号:

TH133.33

刘素艳, 董一林, 乔一鸣, 马增强. 基于改进蜣螂优化算法的轴承数字孪生模型的动态故障注入研究[J]. 机械工程学报, 2026, 62(3): 446-457.

LIU Suyan, DONG Yilin, QIAO Yiming, MA Zengqiang. Research on Dynamic Fault Injection of Bearing Digital Twin Model Based on Improved Dung Beetle Optimization Algorithm[J]. Journal of Mechanical Engineering, 2026, 62(3): 446-457.

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基于改进蜣螂优化算法的轴承数字孪生模型的动态故障注入研究

Research on Dynamic Fault Injection of Bearing Digital Twin Model Based on Improved Dung Beetle Optimization Algorithm

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