Lecture Title: Complete Stribeck Curve Prediction by Applying Machine Learning to Acoustic Emission Data from a Lubricated Sliding Contact

Date & Time: Thursday, July 3, 2025, 09:00–11:00 AM

Venue: Room 2057, Building 2, Innovation Harbor

Speaker: Dr. Min Yu, Imperial College London

Speaker Biography: Dr. Min Yu is a Research Fellow and PhD supervisor at Imperial College London and a recipient of the Imperial College Junior Research Fellowship. His research focuses on intelligent mechanical surface interfaces by integrating sensing, control, physical modeling, and data-driven methods, with key applications in lubricated transmission systems and robotic tactile sensing. He has published over 60 papers in leading journals such as Nano Energy, Chemical Engineering Journal, Engineering, IEEE Transactions on Industrial Electronics, and Mechanical Systems and Signal Processing. He holds 6 invention patents and leads 5 research projects. Dr. Yu also actively collaborates with major international companies including Toyota, Shell, ExxonMobil, and Safran Group through academia–industry partnerships.

Lecture Abstract: The Stribeck curve for a sliding contact characterizes its lubrication behaviour and enables friction to be predicted across various regimes. Current methods of obtaining Stribeck curves are confined to laboratory settings and often involve destructive testing, which precludes real-time monitoring. The work shows a successful prediction of Stribeck curve for a sliding contact by applying machine learning (ML) models to recorded acoustic emission (AE) data, which is suitable for in-situ and non-destructive condition monitoring and failure prediction.