[1]苑浩德,付 庄,金惠良.FFDEZOA 优化的 SCARA 机器人故障数据聚类分析[J].机械与电子,2024,42(10):69-75.
　YUAN Haode,FU Zhuang,JIN Huiliang.FFDEZOA Optimized Clustering Analysis of SCARA Robot Fault Data[J].Machinery & Electronics,2024,42(10):69-75.
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FFDEZOA 优化的 SCARA 机器人故障数据聚类分析()
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《机械与电子》[ISSN:1001-2257/CN:52-1052/TH]

卷:
42
期数:
2024年10期
页码:
69-75
栏目:
机电一体化
出版日期:
2024-10-30

文章信息/Info

Title:
FFDEZOA Optimized Clustering Analysis of SCARA Robot Fault Data
文章编号:
1001-2257 ( 2024 ) 10-0069-07
作者:
苑浩德; 付 庄; 金惠良
上海交通大学机械系统与振动国家重点实验室,上海 200240
Author(s):
YUAN Haode ;  FU Zhuang ;  JIN Huiliang
( State Key Laboratory of Mechanical Systems and Vibration , Shanghai Jiao Tong University , Shanghai 200240 , China )
关键词:
k-means 聚类算法; 斑马算法;  SCARA 机器人; 差分进化
Keywords:
k-means clustering algorithm ;  zebra algorithm ;  SCARA robot ;  differential evolution
分类号:
TP301.6 ; TP242
文献标志码:
A
摘要:
针对现有聚类方法对机器人故障数据聚类时对初始点选取依赖性大、收敛速度慢且精度低等问题,提出了一种 FFDEZOA 算法来对 KMC 聚类算法进行优化。 ZOA 算法具有寻优能力较强,收敛速度快,且在聚类时对初始点选取依赖性小,但其有几率会陷入到局部最优解。首先针对 ZOA 算法的缺点,提出了自由觅食策略、非线性收敛因子及斑马进化策略等来对其进行改进,能够有效提高算法搜索范围,从而避免局部最优;进而结合 FFDEZOA 和 KMC 算法的互补迭代,既加快了算法的搜索速度,也提升了精度。在多个公开数据集上的实验表明, FFDEZOA-KMC 在精确度和归一化互信息的指标上均优于 ZOA-KMC 、 AO-KMC 、 KMC 和 MFO-KMC ,具有更好的收敛性能和聚类效果。最后依据各故障特征的主成分不同,利用 FFDEZOA-KMC 对故障数据进行了聚类,可在多种工况下对机器人进行针对性的保养和维护。
Abstract:
A FFDEZOA algorithm is proposed to optimize the KMC clustering algorithm in response to the problems of high dependence on initial point selection , slow convergence speed , and low accuracy in existing clustering methods for robot fault data clustering.The ZOA algorithm has strong optimization ability , fast convergence speed , and little dependence on initial point selection during clustering , but it has a chance of falling into local optimal solutions.Firstly , in response to the shortcomings of the ZOA algorithm , free foraging strategy , nonlinear convergence factor , and zebra evolution strategy were proposed to improve it , which can effectively increase the search range of the algorithm and avoid local optima ; furthermore , combining the complementary iteration of FFDEZOA and KMC algorithms not only accelerates the search speed of the algorithm , but also improves accuracy.Experiments on multiple public datasets have shown that FFDEZOA-KMC outperforms ZOA-KMC , AO-KMC , KMC , and MFO-KMC in terms of accuracy and normalized mutual information , with better convergence performance and clustering performance.Finally , based on the different principal components of each fault feature , FFDEZOA-KMC was used to cluster the fault data , which can provide targeted maintenance and upkeep for robots under various working conditions.

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备注/Memo

备注/Memo:
收稿日期: 2024-04-19
基金项目:深圳市科创委技术攻关重点项目( JSGG20200701095003006 );基础加强计划项目( 2020 JCJQ );国家自然科学基金面上项目( 61973210 )
作者简介:苑浩德 ( 1999- ),男,河北石家庄人,硕士研究生,研究方向为云平台监控、机器人故障诊断;付 庄 ( 1972- ),男,上海人,教授,博士研究生导师,研究方向为医疗机器人、特种机器人、服务机器人、工业机器人、仿生机器人及其智能控制系统、多自由度经济型机器人、智能控制系统设计、机电产品及机电生产线设计、智能测量设备,通信作者。
更新日期/Last Update: 2024-10-31