Nonlinear Spectrum Based Fault Diagnosis Method for Industrial Robot Drive Systems

doi:10.7652/xjtuxb201904014

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Nonlinear Spectrum Based Fault Diagnosis Method for Industrial Robot Drive Systems

更新时间：2025-07-09

- Nonlinear Spectrum Based Fault Diagnosis Method for Industrial Robot Drive Systems
- Vol. 53, Issue 4, Pages: 93-99(2019)
- 作者机构：
  
  西安交通大学机械制造系统工程国家重点实验室,西安,710049
- 作者简介：
- 基金信息：
- DOI：10.7652/xjtuxb201904014
  CLC： TP391.9
- Online First：10 April 2019，
  
  Published：2019
- 稿件说明：
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Nonlinear Spectrum Based Fault Diagnosis Method for Industrial Robot Drive Systems[J]. 2019, 53(4): 93-99.
DOI：

Nonlinear Spectrum Based Fault Diagnosis Method for Industrial Robot Drive Systems[J]. 2019, 53(4): 93-99. DOI： 10.7652/xjtuxb201904014.

摘要

针对广义频率响应函数(GFRF)在故障诊断中存在计算量大、无法满足系统对诊断实时性要求的问题

提出基于非线性输出频率响应函数(NOFRF)的工业机器人驱动系统故障诊断方法。该方法构建系统一维频谱函数的辨识模型

将系统的输出频谱与估计频谱进行比较求出残差

根据残差大小改变辨识步长迭代出前4阶频谱; 对获取到的4阶频谱进行逐阶采样

每阶频谱采集10个数值

共40个频谱构成40维特征矢量

将其作为系统的故障特征输入核主成分分析方法(KPCA)进行压缩

通过计算主元累计贡献率将高维数据压缩至3维

降低变量之间的非线性度; 构造SVM分类器

将KPCA方法生成的低维数据中60%的数据作为训练集对分类器进行训练

将40%的数据作为测试集进行故障识别。实验结果表明

在相同的数据提取任务下

与基于GFRF的方法相比

所提方法节约时间854%

可以准确、快速地提取系统故障特征

进一步验证了该方法在工业机器人驱动系统故障诊断应用上的可靠性。

Abstract

A fault diagnosis method for industrial robot drive systems based on nonlinear output frequency response function(NOFRF)is proposed to solve the problem that the generalized frequency response function(GFRF)cannot satisfy the real-time requirement of systems due to its large amount of calculation in fault diagnosis. The method constructs an identification model of one-dimensional spectrum function for a system

and calculates the residual between the output spectrum and the estimated spectrum of the system in order to change iteration step length of identification and to get spectrum values of the first four orders. These four orders' NOFRF spectrum values are then sampled order by order with 10 values for each order

and a total of 40 spectrum values form a 40-dimensional feature vector. The resulting vectors are sent to the kernel principal component analysis(KPCA)for compression by calculating the cumulative contribution rate of the principal component. The high-dimensional data are compressed into 3-dimensional data to reduce the nonlinearity between variables. An SVM classifier is constructed

and 60% of the low-dimensional data generated by KPCA are used as a training set to train the classifier

while the rest 40% of the data are used as a test set to identify faults. Experimental results and a comparison with GFRF show that the proposed method saves 85.4% of the processing time under the same data extraction task

and accurately and quickly extracts fault features of the system

which proves the reliability of the method in the application to fault diagnosis of industrial robot drive systems.

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references

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