聊城大学学报（自然科学版）2025，Vol.38Issue(4)：516-526,11.DOI:10.19728/j.issn1672-6634.2024110004
基于小波散射和机器学习的轴承故障检测
Fault detection of bearings based on wavelet scattering and machine learning
摘要
Abstract
Bearing condition monitoring and fault diagnosis are essential for the stability and reliability of medical devices.In response to issues such as reliance on manual experience,low detection efficiency,and accuracy in bearing fault detection,a bearing fault detection model based on wavelet scattering and support vector machine is proposed.Firstly,the original vibration signal is preprocessed,and a wavelet scattering network is constructed.Cross validation and grid search algorithm are used to train the SVM model,which can determine the fault type and size at different speed.To verify the performance of the model,ex-periments are conducted using a bearing dataset collected by Case Western Reserve University.The results show that the model can achieve an accuracy rate of 100%in detecting fault positions and fault sizes.The model demonstrates excellent accuracy and stability,and provides a new approach for automatic bearing fault detection.关键词
故障检测/滚动轴承/小波散射/机器学习/支持向量机Key words
fault detection/rolling bearing/wavelet scattering/machine learning/SVM分类
机械制造引用本文复制引用
梅娜,颜秉政..基于小波散射和机器学习的轴承故障检测[J].聊城大学学报（自然科学版）,2025,38(4):516-526,11.基金项目
国家自然科学基金项目(62305100) （62305100）
河北省高等学校科学研究项目(BJK2022008)资助 （BJK2022008）
