基于数字孪生的不同工况下谐波减速器故障诊断方法

doi:10.3901/JME.2025.18.012

机械工程学报 ›› 2025, Vol. 61 ›› Issue (18): 12-26.doi: 10.3901/JME.2025.18.012

• 仪器科学与技术 • 上一篇

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基于数字孪生的不同工况下谐波减速器故障诊断方法

王玉静¹, 李祎然¹, 康守强¹, 刘连胜², 李玉庆³, 孙宇林¹

1. 哈尔滨理工大学模式识别与信息感知黑龙江省重点实验室哈尔滨 150080;
2. 哈尔滨工业大学电子与信息工程学院哈尔滨 150001;
3. 哈尔滨工业大学航天学院哈尔滨 150001

收稿日期:2024-08-05 修回日期:2024-12-28 发布日期:2025-11-08
作者简介:王玉静，女，1983年出生，博士，教授，博士研究生导师。主要研究方向为非平稳信号处理、故障诊断、状态评估与预测技术。E-mail：mirrorwyj@163.com;李祎然，女，2000年出生，硕士研究生。主要研究方向为非平稳信号处理、故障诊断、状态评估与预测技术。E-mail：1178864214@qq.com;李玉庆(通信作者)，男，1980年出生，博士，教授，博士研究生导师。主要研究方向为复杂装备智能状态感知与健康管控、航天器智能任务规划与自主运行。E-mail：bradley@hit.edu.cn
基金资助:
国家重点研发计划(2021YFA1003501)、国家自然科学基金(52375533、52075117)、山东省自然科学基金(ZR2023ME057)、黑龙江省重点研发计划项目(2022ZX01A20)、黑龙江省自然科学基金(PL2024F018)和哈尔滨市制造业科技创新人才(2023CXRCCG017)资助项目

Fault Diagnosis Method for Harmonic Reducers under Different Working Conditions Based on Digital Twin

WANG Yujing¹, LI Yiran¹, KANG Shouqiang¹, LIU Liansheng², LI Yuqing³, SUN Yulin¹

1. Heilongjiang Province Key Laboratory of Pattern Recognition and Information Perception, Harbin University of Science and Technology, Harbin 150080;
2. School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150001;
3. School of Astronautics, Harbin Institute of Technology, Harbin 150001

Received:2024-08-05 Revised:2024-12-28 Published:2025-11-08

摘要/Abstract

摘要： 谐波减速器作为工业机器人的重要组件，运行环境复杂多变，一旦发生故障造成巨大损失。考虑到实际谐波减速器振动数据采集难度大，故障样本量少且数据标签缺失，不同工况下数据分布存在差异问题，提出一种基于数字孪生的不同工况下谐波减速器故障诊断方法。首先，基于动力学建模方法构建谐波减速器故障数字孪生模型，获取孪生数据；其次，提出基于循环生成对抗网络的数字孪生虚实映射方法，实现孪生数据与实测数据的虚实映射；然后，引入改进的半软阈值函数构建深度残差收缩网络，抑制噪声干扰并提取特征，同时在无监督场景下对所提特征进行域适应处理，利用MMD减小领域间分布差异，实现不同工况下的故障诊断。最后，搭建谐波减速器故障模拟试验台并进行试验验证，所提方法在所有迁移任务中平均准确率可达99.2%，有效解决无监督场景中不同工况下谐波减速器的故障诊断问题。

关键词: 谐波减速器, 不同工况, 数字孪生, 动力学模型, 故障诊断

Abstract: The harmonic reducer, a crucial component of industrial robots, works in complex and variable environments, leading to significant losses when failures occur. Due to the challenges in acquiring actual vibration data of harmonic reducers, the limited number of fault sample, missing data labels, and differences in data distribution under varying working conditions, a fault diagnosis method for harmonic reducer under different working conditions based on digital twin is proposed. Firstly, a digital twin model of the faulty harmonic reducer is constructed using dynamic modeling to generate twin data. Secondly, a virtual-real mapping method based on a cyclic generative adversarial network is proposed to achieve the mapping between twin data and real measured data. To enhance feature extraction and suppress noise interference, an improved semi-soft threshold function is integrated into a deep residual shrinkage network. Meanwhile, the extracted features are subjected to domain adaptation in unsupervised scenarios, using the maximum mean discrepancy to reduce distribution differences between domains, thereby achieving fault diagnosis under different working conditions. Finally, a fault simulation test bench for the harmonic reducer is established, and experimental verification shows that the proposed method achieves an average accuracy of 99.2% in all transfer tasks. It effectively addresses the fault diagnosis challenges of harmonic reducers in unsupervised scenarios under different working conditions.

Key words: harmonic reducer, different working conditions, digital twin, dynamic modeling, fault diagnosis

中图分类号:

TN911
TH165

王玉静, 李祎然, 康守强, 刘连胜, 李玉庆, 孙宇林. 基于数字孪生的不同工况下谐波减速器故障诊断方法[J]. 机械工程学报, 2025, 61(18): 12-26.

WANG Yujing, LI Yiran, KANG Shouqiang, LIU Liansheng, LI Yuqing, SUN Yulin. Fault Diagnosis Method for Harmonic Reducers under Different Working Conditions Based on Digital Twin[J]. Journal of Mechanical Engineering, 2025, 61(18): 12-26.

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基于数字孪生的不同工况下谐波减速器故障诊断方法

Fault Diagnosis Method for Harmonic Reducers under Different Working Conditions Based on Digital Twin

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