摘要
小波变换常应用于滚动轴承的故障诊断。然而,在实际应用中小波变换分解的层数需要人为指定,若层数过多,则相应的特征也会变多,增加特征筛选的难度。针对这一问题,本文以电机滚动轴承为研究对象,提出了一种基于相对小波能量与决策树算法的故障诊断方法。该方法具有较强的可解释性,主要体现在如下两个方面:1、以清晰的树形结构建立易于理解的分类规则;2、可利用特征重要性指标量化每个特征的重要性,并对重要特征进行筛选。结果表明:当小波分解的层数增加到6层以上时,准确率会提高至95%以上。此外,由于决策树中预剪枝策略,决策树实际使用到的特征较少,且构建决策树时选择了特征重要性较高的特征,避免了决策树结构过于庞大而降低其可解释性。
Wavelet transform is often used in fault diagnosis of rolling bearings.However,in practical applications,the number of layers decomposed by wavelet transform needs to be manually specified.If there are too many layers,the corresponding features will also increase,which will increase the difficulty of feature selection.To solve this problem,this paper takes the motor rolling bearing as the research object,and proposes a fault diagnosis method based on relative wavelet energy and decision tree algorithm.This method has strong interpretability,which is mainly reflected in the following two aspects:1.Establishing classification rules that is easy to understand with clear tree structures.2.The feature importance index can be used to quantify the importance of each feature and screen important features.The results show that:when the number of layers decomposed by wavelet increases to more than 6 layers,the accuracy rate will increase to more than 95%.In addition,due to the pre-pruning strategy in the decision tree,the decision tree actually uses fewer features,and the features with higher feature importance are selected when building the decision tree,so the decision tree will not become too large and reduce its interpretability.
作者
丁明彬
Ding Ming-bin(Xiamen King Long United Automotive Industry Co.,Ltd.,Fujian Xiamen 361023,China)
出处
《内燃机与配件》
2023年第23期54-57,共4页
Internal Combustion Engine & Parts
关键词
故障诊断
小波变换
相对小波能量
决策树
可解释性
Fault diagnosis
Wavelet transform
Relative wavelet energy
Decision tree
Interpretability
