摘要
为解决在训练样本不足条件下,轴承故障特征提取困难的问题,提出一种基于改进神经混沌学习(neurochaos learning+AdaBoost,NL-AdaBoost)的轴承故障诊断新方法。首先,对时域振动信号进行快速傅里叶变换(fast fourier transform,FFT)提取频域特征,拼接时频域信号获得一维特征样本;其次,输入信号产生对混沌GLS神经元的激励,形成ChaoFEX特征,馈送至集成学习分类器(AdaBoost);随后,选取轴承故障特征样本,对样本集做k折交叉验证,获得模型最优超参数值,将其应用于测试集进行模型分类能力验证;最后,在小样本对比实验中,与4种常见深度学习算法比较模型的macro F1-score。实验结果证明,在低训练样本条件下,NL-AdaBoost模型具有良好的准确性和泛化能力。
A new method of bearing fault diagnosis based on improved neurochaos learning(neurochaos learning+AdaBoost,NL-AdaBoost) is proposed.First,the fast fourier transform(FFT) of the time-domain vibration signal is performed to extract the frequency-domain features,and the one-dimensional feature samples are obtained by splicing the time-frequency-domain signals;then,the input signal generates excitation to the chaotic GLS neurons to form ChaoFEX features,which are fed to the integrated learning classifier(AdaBoost);subsequently,the bearing fault feature samples,and do k-fold cross-validation on the sample set to obtain the optimal hyperparameter values of the model,which are applied to the test set for model classification capability validation;finally,in a small-sample comparison experiment,the macro F1-score of the model is compared with four common deep learning algorithms.The experimental results demonstrate that under the low training sample condition,NL-AdaBoost,the model has good accuracy and generalization ability.
作者
李天昊
李志星
王衍学
LI Tianhao;LI Zhixing;WANG Yanxue(School of Mechanical-Electrical and Vehicle Engineering,Beijing University of Civil Engineering and Architecture,Beijing 100044,China;Key Laboratory of Service Performance Assurance of Urban Rail Vehicles,Beijing University of Civil Engineering and Architecture,Beijing 100044,China)
出处
《组合机床与自动化加工技术》
北大核心
2024年第2期182-185,192,共5页
Modular Machine Tool & Automatic Manufacturing Technique
基金
国家自然科学基金项目(51875032)
国家自然科学基金青年基金项目(51805275)
北京建筑大学青年教师科研能力提升计划课题项目(X21053)。
关键词
小样本训练
神经混沌学习
滚动轴承
故障诊断
small sample training
neural chaos learning
rolling bearing
fault diagnosis
