[1]陈湘源.煤矿充电硐室换电机器人滚筒主轴轴承故障诊断方法研究[J].机械与电子,2026,44(02):62-71.
　CHEN Xiangyuan. Research on Fault Diagnosis Method for Drum Spindle Bearing of Battery Swap Robots in Coal Mine Charging Chambers[J].Machinery & Electronics,2026,44(02):62-71.
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煤矿充电硐室换电机器人滚筒主轴轴承故障诊断方法研究()
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《机械与电子》[ISSN:1001-2257/CN:52-1052/TH]

卷:
44
期数:
2026年02期
页码:
62-71
栏目:
智能检测
出版日期:
2026-02-26

文章信息/Info

Title:
 Research on Fault Diagnosis Method for Drum Spindle Bearing of Battery Swap Robots in Coal Mine Charging Chambers
文章编号:
1001-2257(2026)02-0062-10
作者:
 陈湘源
 (国能榆林能源有限责任公司,陕西 榆林719000)
Author(s):
 CHEN Xiangyuan
 (Guoneng Yulin Energy Co.,Ltd.,Yulin 719000,China)
关键词:
 ; 轴承故障诊断; 主成分分析; t-分布邻域嵌入; 粒子群算法; 支持向量机
Keywords:
bearing fault diagnosis; principal component analysis; t distributed neighborhood embedding; particle swarm optimization algorithm; support vector machine
分类号:
TH113.1
文献标志码:
A
摘要:
换电机器人用于井下锂电池胶轮车的换电工作,但由于井下环境复杂、噪声大,导致机器人滚筒主轴轴承故障诊断困难,为解决该问题,提出了一种基于PCA tSNE PSO SVM 的故障诊断方法。首先,提取振动信号的时域特征构建高维特征集;接着,联合主成分分析与t 分布邻域嵌入算法分析各个特征的关系及贡献度,以消除冗余信息并保留敏感特征,提高诊断效率和降低内存需求;然后,利用粒子群算法自适应优化支持向量机的惩罚参数与核函数参数,构建最优参数的故障诊断模型;最后,分别通过公开数据集及自主实验台数据对算法性能进行验证。实验结果表明,该方法具有较高的诊断精度、抗噪能力以及较好的诊断效率和较低的运行成本需求。
Abstract:
 Battery swapping robots are employed for replacing lithium batteries in underground rubber tired vehicles.However,due to the complex and noisy underground environment,it is challenging to diagnose the faults of the drum spindle bearings of the robot.To address this issue,a fault diagnosis method based on PCA tSNE PSO SVM is proposed.Firstly,the time domain features of vibration signal are extracted to construct a high dimensional feature set.Subsequently,the Principal Component Analysis (PCA) and t distribution neighborhood mbedding (t SNE) algorithm are combined to analyze the relationships and contributions of each feature,thereby eliminating redundant information while retaining sensitive features.This improves the diagnostic efficiency and reduces memory requirements.Then,the Particle Swarm Optimization (PSO) algorithm is adopted to adaptively optimize the penalty parameters and kernel function parameters of the Support Vector Machine (SVM),constructing a fault diagnosis model with the optimal parameters.Finally,the algorithm performance is validated using both a public datasets and data collected from a self built experimental platform.The experiment results demonstrate that the proposed method achieves high diagnostic accuracy,strong noise resistance,favorable diagnostic efficiency and low operating cost requirements.

参考文献/References:

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备注/Memo

备注/Memo:
 收稿日期:2025-10-28
基金项目:辽宁省教育厅基础项目(JYTJT20220293);国能榆林能源青龙寺煤矿分公司项目(YLNY-QLSCB-JF-2023-22)
作者简介:陈湘源 (1972-),男,内蒙古鄂尔多斯人,硕士,高级工程师,研究方向为信息化与智能化、机电系统安全。
更新日期/Last Update: 2026-04-28