基于GA-SVR的小样本数据缺失下的设备故障诊断

doi:10.16182/j.issn1004731x.joss.20-0046

系统仿真学报 ›› 2021, Vol. 33 ›› Issue (6): 1342-1349.doi: 10.16182/j.issn1004731x.joss.20-0046

基于GA-SVR的小样本数据缺失下的设备故障诊断

位晶晶, 刘勤明, 叶春明, 李冠林

上海理工大学管理学院,上海 200093

收稿日期:2020-01-15 修回日期:2020-03-17 出版日期:2021-06-18 发布日期:2021-06-23
作者简介:位晶晶(1994-),女,硕士生,研究方向为设备维护。E-mail：2388208985@qq.com
基金资助:
国家自然科学基金(71840003,71471116); 上海市自然科学基金(19ZR1435600)

Fault Diagnosis of Mechanical Equipment Based on GA-SVR with Missing Data in Small Samples

Wei Jingjing, Liu Qinming, Ye Chunming, Li Guanlin

Business School, University of Shanghai for Science and Technology, Shanghai 200093, China

Received:2020-01-15 Revised:2020-03-17 Online:2021-06-18 Published:2021-06-23

摘要/Abstract

摘要： 针对小样本数据缺失下的设备故障诊断问题,提出基于遗传算法优化支持向量回归的缺失数据填补方法,以改善设备故障诊断效果。利用缺失数据所属变量的数据,训练遗传算法优化的支持向量回归,得到单变量预测结果;通过相关性分析重构训练集,获得多变量预测结果。建立动态权重将单变量预测与多变量预测的结果相组合,对缺失数据进行填补。将完整的数据作为输入,利用支持向量机对设备进行故障诊断。实例分析表明,所提出的方法具有较佳的故障诊断效果。

关键词: 故障诊断, 小样本, 数据缺失, 支持向量机, 动态权重, 组合预测

Abstract: In view of the equipment fault diagnosis with small and missing sample data, a method of missing data filling based on support vector regression optimized by genetic algorithm is proposed to improve the accuracy of equipment fault diagnosis. The support vector regression optimized by genetic algorithm was trained by other data values of missing data, and univariate prediction results were obtained. The training set was reconstructed through correlation analysis, so as to obtain the multivariate prediction results. Dynamic weights were established to combine univariate prediction results and multivariate prediction results to fill in the missing data. The complete data is taken as the input, and the equipment fault is diagnosed by support vector machine. Example analysis shows that the method proposed in this paper has a high fault diagnosis accuracy.

Key words: fault diagnosis, small sample, missing data, support vector machine, dynamic weight, combination prediction

中图分类号:

TP29
TP391

位晶晶, 刘勤明, 叶春明, 李冠林. 基于GA-SVR的小样本数据缺失下的设备故障诊断[J]. 系统仿真学报, 2021, 33(6): 1342-1349.

Wei Jingjing, Liu Qinming, Ye Chunming, Li Guanlin. Fault Diagnosis of Mechanical Equipment Based on GA-SVR with Missing Data in Small Samples[J]. Journal of System Simulation, 2021, 33(6): 1342-1349.

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基于GA-SVR的小样本数据缺失下的设备故障诊断

Fault Diagnosis of Mechanical Equipment Based on GA-SVR with Missing Data in Small Samples

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