一种无监督的汽车装配车间桁架机器人健康状态监测方法

doi:10.3901/JME.2025.04.044

机械工程学报 ›› 2025, Vol. 61 ›› Issue (4): 44-54.doi: 10.3901/JME.2025.04.044

• 仪器科学与技术 • 上一篇

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一种无监督的汽车装配车间桁架机器人健康状态监测方法

刘阔^1,2, 崔益铭¹, 杨顼³, 李明禹¹, 李凯¹, 王永青^1,2

1. 大连理工大学高性能精密制造全国重点实验室大连 116024;
2. 智能制造龙城实验室常州 213164;
3. 华晨宝马汽车有限公司沈阳 110044

收稿日期:2024-02-19 修回日期:2024-09-05 发布日期:2025-04-14
作者简介:刘阔，男，1983年出生，博士，教授，博士研究生导师。主要研究方向为数控机床热误差实时补偿技术、数控机床及产线智能监控技术和数控机床性能测试与优化技术。E-mail：liukuo@dlut.edu.cn
崔益铭(通信作者)，男，1999年出生，硕士研究生。主要研究方向为机械故障监测技术。E-mail：cuiyiming715@mail.dlut.edu.cn
基金资助:
国家自然科学基金联合基金资助项目(U22B2085)。

Unsupervised Health Monitoring Methods for Truss Robots in Automobile Assembly Workshops

LIU Kuo^1,2, CUI Yiming¹, YANG Xu³, LI Mingyu¹, LI Kai¹, WANG Yongqing^1,2

1. State Key Laboratory of High-performance Precision Manufacturing, Dalian University of Technology, Dalian 116024;
2. Intelligent Manufacturing Longcheng Laboratory, Changzhou 213164;
3. BMW Brilliance Automotive Ltd., Shenyang 110044

Received:2024-02-19 Revised:2024-09-05 Published:2025-04-14

摘要/Abstract

摘要： 桁架机器人是自动化汽车装配生产线的重要组成部分，实现桁架机器人运行状态的实时监测与故障诊断是提升生产效率、保障产线可靠性的重要手段。针对实际生产过程中故障实例稀缺等问题，提出一种无监督的健康状态监测方法。首先根据功率曲线划分运行数据，基于多源传感器融合方法生成训练样本；然后建立基于堆叠稀疏自编码器的数据提取模型，得到反映关键运行信息的低维数据；最后训练基于支持向量描述的单分类模型用于异常状态监测。为验证所提方法的有效性，以宝马车身车间Zollern桁架机器人作为研究对象进行试验验证。试验结果表明，提出的监测方法针对桁架机器人X、Z轴减速电机的监测准确率分别达到91.33%和99.26%，相较于其他无监督监测方法具有明显性能优势。

关键词: 健康状态监测, 桁架机器人, 无监督学习, 多源信息融合

Abstract: Truss robots are an important part of automated automotive assembly lines. Real-time monitoring and fault diagnosis of the running condition of truss robots is an important method of improving production efficiency and ensuring the reliability of production lines. In order to solve the problem of scarcity of fault instances in the actual production process, an unsupervised health monitoring method is proposed. First, running data is divided based on the power curves, then the samples for training dataset is generated based on multi-source information fusion method. Then a feature extraction model based on stacked sparse autoencoder is established for obtaining low-dimensional data that reflects critical information during running process. Finally, a one-class classification model based on support vector data description is trained for anomaly detection. In order to verify the effectiveness of the proposed method, validation experiments are conducted on Zollern truss robots in BMW body shop. The results show that the accuracy of the proposed monitoring method reach 91.33% and 99.26% respectively on monitoring tasks of truss robot X-axis and Z-axis geared motors and the proposed method have obvious performance advantages over other unsupervised monitoring methods.

Key words: health monitoring, truss robot, unsupervised learning, multi-source information fusion

中图分类号:

TP242

刘阔, 崔益铭, 杨顼, 李明禹, 李凯, 王永青. 一种无监督的汽车装配车间桁架机器人健康状态监测方法[J]. 机械工程学报, 2025, 61(4): 44-54.

LIU Kuo, CUI Yiming, YANG Xu, LI Mingyu, LI Kai, WANG Yongqing. Unsupervised Health Monitoring Methods for Truss Robots in Automobile Assembly Workshops[J]. Journal of Mechanical Engineering, 2025, 61(4): 44-54.

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一种无监督的汽车装配车间桁架机器人健康状态监测方法

Unsupervised Health Monitoring Methods for Truss Robots in Automobile Assembly Workshops

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