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

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

Abstract It is increasingly important to monitor sliding interfaces within machines, since this is where both energy is lost, and failures occur. Acoustic emission (AE) techniques offer a way to monitor contacts remotely without requiring transparent or electrically conductive materials. However, ac...

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Bibliographic Details
Main Authors: Robert Gutierrez, Tianshi Fang, Robert Mainwaring, Tom Reddyhoff
Format: Article
Language:English
Published: SpringerOpen 2024-02-01
Series:Friction
Subjects:
acoustic emission
condition monitoring
friction
machine learning
Gaussian process regression
support vector machine
Online Access:https://doi.org/10.1007/s40544-023-0834-7

Internet

https://doi.org/10.1007/s40544-023-0834-7

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