检索结果-南通市图书馆

Predicting the coefficient of friction in a sliding contact by applying machine learning to acoustic emission data

Friction 2024年第6期12卷 1299-1321页

作者： robert GUTIERREZ Tianshi FANG robert mainwaring Tom REDDYHOFF Tribology Group Department of Mechanical EngineeringImperial College LondonExhibition RoadLondon SW72AZUK Shell Global Solutions(US)Inc.Shell Technology Center Houston 3333 Highway 6 SouthHoustonTX 77082USA Shell International Petroleum Company Limited Shell CentreYork RoadLondon SE17NAUK

It is increasingly important to monitor sliding interfaces within machines,since this is where both energy is lost,and failures *** emission(AE)techniques offer a way to monitor contacts remotely without requiring transparent or electrically conductive ***,acoustic data from sliding contacts is notoriously complex and difficult to ***,we simultaneously measure coefficient of friction(with a conventional force transducer)and acoustic emission(with a piezoelectric sensor and high acquisition rate digitizer)produced by a steel‒steel rubbing *** data is then used to train machine learning(ML)algorithms(e.g.,Gaussian process regression(GPR)and support vector machine(SVM))to correlated acoustic emission with *** training requires the dense AE data to first be reduced in size and a range of processing techniques are assessed for this(e.g.,down-sampling,averaging,fast Fourier transforms(FFTs),histograms).Next,fresh,unseen AE data is given to the trained model and the resulting friction predictions are compared with the directly measured *** is excellent agreement between the measured and predicted friction when the GPR model is used on AE histogram data,with root mean square(RMS)errors as low as 0.03 and Pearson correlation coefficients reaching ***,predictions remain accurate despite changes in test conditions such as normal load,reciprocating frequency,and stroke *** paves the way for remote,acoustic measurements of friction in inaccessible locations within machinery to increase mechanical efficiency and avoid costly failure/needless maintenance.

关键词： acoustic emission condition monitoring friction machine learning Gaussian process regression support vector machine

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

欢迎您,

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

请选择保存的检索档案：

请选择收藏分类：

通借通还

欢迎您,

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：