参数优化GBDT算法的密封继电器材质识别

Recognition of sealed relay material based on parameter optimization GBDT algorithm

传统多余物微粒材质识别算法在判断金属和非金属微粒时,误判现象比较严重。本文将参数优化的梯度提升决策树算法(Gradient boosting decision tree,GBDT)应用于多余物材质识别。特征选择方面,在原有的脉冲面积、脉冲左右对称度、脉冲上下对称度、脉冲持续时间、脉冲上升占比、能量密度、脉冲占比、波峰系数、面积占比、频谱质心、均方频率、方差、过零点率和均方根差14个特征基础上,利用小波变换提取出了能量系数作为新的特征值,并建立新的样本特征集。然后结合机器学习方法训练得到基于GBDT多余物材质识别模型。为了使分类器性能达到最优,对其进行超参数调优。采用贝叶斯优化库中的分布式异步超参数优化模块(Hyperopt),并结合树形窗密度评估器(Parzen)可以获得模型的最佳优化参数。经过与K近邻(K-nearest neighbor classification,KNN)和支持向量机分类模型(Support vector machine,SVM)进行对比,结果表明,采用参数优化的GBDT算法对金属和非金属微粒具有很好的识别效果,能够有效提高其分类性能。

Traditional algorithms for recognizing the material of loose particles have serious misjudgments when judging metals and non-metals.The parameter-optimized gradient boosting decision tree algorithm(GBDT)was applied to the recognition of redundant materials.In terms of feature selection,based on original 14 features of the original pulse area,pulse left-right symmetry,pulse up-and-down symmetry,pulse duration,pulse rise percentage,energy density,pulse percentage,crest factor,area percentage,spectral centroid,mean square frequency,variance,zero-crossing rate,and root mean square error,wavelet transform was used to extract the energy coefficient as a new feature value,and a new sample feature set was established,and then combined with machine learning methods to train a GBDT-based redundant material recognition model.In order to optimize the performance of the classifier,hyperparameter tuning was performed on it.The Hyperopt tool optimized by Bayesian was used,combined with the tree-shaped Parzen estimator to obtain the most suitable parameters.The results indicate that the parameter-optimized GBDT algorithm has a good recognition effect on metal and non-metal particles,and can improve the classification performance effectively by comparing with KNN and support vector machine classification models.