QGA-VPMCD智能诊断模型研究

杨宇,李紫珠,何知义,程军圣

振动与冲击 ›› 2015, Vol. 34 ›› Issue (13) : 31-35.

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振动与冲击 ›› 2015, Vol. 34 ›› Issue (13) : 31-35.
论文

QGA-VPMCD智能诊断模型研究

  • 杨宇,李紫珠,何知义,程军圣
作者信息 +

The intelligent diagnosis model research of QGA-VPMCD

  • YNAG Yu   LI Zi-zhu   He Zhi-yi  CHENG Jun-sheng
Author information +
文章历史 +

摘要

针对多变量预测模型模式识别(Variable predictive model-based class discriminate,简称VPMCD)分类方法中只选择了某单一模型的缺陷,提出了一种基于量子遗传算法优化的多变量智能诊断模型(Quantum genetic algorithm - Variable predictive model-based class discriminate,简称QGA-VPMCD)。该模型采用最优权值矩阵来综合考虑各诊断模型对分类结果的影响。即首先通过样本训练来建立多个SVPM(Subordinate variable predictive model,简称SVPM);然后采用量子遗传优化算法求出各SVPM的权值,从而得到最优权值矩阵;最后用最优权值矩阵加权融合测试样本的SVPM特征变量预测值,得到最佳特征变量预测值,并以预测误差平方和最小为判别函数来识别故障的类型。滚动轴承振动信号的分析结果表明了该模型的有效性。

Abstract

Aiming at the defects that only one single model was selected during the variable predictive model-based class discriminate (VPMCD) classification method, an intelligent diagnosis model quantum genetic algorithm - variable predictive model-based class discriminate (QGA-VPMCD) is presented. The optimal weight matrix is used to comprehensively consider the effect of each diagnosis model of classification results in this model. Firstly, multiple subordinate variable predictive models (SVPMs) can be established through samples training. Secondly, intelligent quantum genetic algorithm is used to acquire the weights of each SVPM and the optimal weight matrix is obtained. Finally, optimal weights matrix is exploited to get optimal feature variables predictions by weight fusion for the values, which are predicted by the SVPMs for the test samples, and fault types are identified according to the minimum error square sum which is regarded as discrimination function simultaneously. The analysis results from the vibration signals of rolling bearings show the effectiveness of the proposed model. 

关键词

多变量预测模型 / 量子遗传算法 / 最优权值矩阵 / 智能诊断模型

Key words

Variable predictive model / quantum genetic algorithm / optimal weight matrix / intelligent diagnosis model

引用本文

导出引用
杨宇,李紫珠,何知义,程军圣. QGA-VPMCD智能诊断模型研究[J]. 振动与冲击, 2015, 34(13): 31-35
YNAG Yu LI Zi-zhu He Zhi-yi CHENG Jun-sheng. The intelligent diagnosis model research of QGA-VPMCD[J]. Journal of Vibration and Shock, 2015, 34(13): 31-35

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