摘要
基于机器学习故障诊断方法,针对船用滚动轴承复合故障特征提取多样化的特点,提出一种以振动信号时域指标为特征的随机森林故障诊断方法。将振动时域信号进行清洗转换,构造5个量纲一化指标的衍生特征,并选取以决策树为基本分类器的随机森林算法建立训练模型;通过特征筛选、评估测试和模型优化得到较为理想的故障诊断分类模型;采用滚动轴承竞赛数据集进行模型仿真,并结合实际模拟8种船用滚动轴承故障状态。通过三向振动实验和算法建模,证明特征提取的科学性和故障诊断模型的有效性。结果表明:采用该方法,数据仿真诊断准确率为98.61%,实验诊断准确率为98.85%,且该方法在振动采集方向为轴向时诊断效果最优。
Based on the machine learning fault diagnosis method,a random forest fault diagnosis method characterized by the time domain index of vibration signal was proposed in view of the diversified characteristics of the composite fault state feature of marine rolling bearings.The vibration time domain signals were cleaned and converted to construct the derived features of five dimensionless indicators,and the random forest algorithm with the decision tree as the basic classifier was selected to establish a training model;the ideal fault diagnosis classification model was obtained through the feature selection,evaluation testing and model optimization;the model simulation was carried out by using the rolling bearing competition data set,and 8 kinds of marine rolling bearing fault conditions in actual cases were simulated.The scientificity of the feature extraction and the validity of the fault diagnosis model were proved through the three-way vibration experiment and algorithm modeling.The results show that the data simulation diagnosis accuracy and experimental diagnosis accuracy are 98.61%and 98.85%respectively,and the fault diagnosis effect of this method is the best when the vibration acquisition is in the axial direction.
作者
陈阳
李一
姬正一
张胜光
雷博
CHEN Yang;LI Yi;JI Zhengyi;ZHANG Shengguang;LEI Bo(Army of 92493,Huludao Liaoning 125000,China;Army of 92941,Huludao Liaoning 125000,China)
出处
《机床与液压》
北大核心
2021年第14期193-200,共8页
Machine Tool & Hydraulics
关键词
滚动轴承
故障诊断
随机森林
机器学习
特征提取
Rolling bearing
Fault diagnosis
Random forest
Machine learning
Feature extraction
