ENERCOMP Seminar Series
Reliable Condition Monitoring Across Engineering Systems: From Ultrasonic Guided Waves to Vibration-Based Tool Monitoring
Thursday, October 15th, 2026
ABSTRACT
Reliable condition monitoring is critical for detecting degradation and supporting timely engineering decisions, yet its practical implementation is often complicated by environmental and operational variability. Changes in temperature, loading, operating regimes and process dynamics can significantly alter measured signals, making it difficult to distinguish genuine condition changes from benign variations.
This talk will explore approaches for extracting robust and reliable condition information from sensing data across structural health monitoring and manufacturing applications. Examples will include ultrasonic guided-wave monitoring for damage detection in engineering structures, where environmental effects such as temperature variation can influence signal characteristics and detection reliability, as well as vibration-based monitoring of machining processes, where changes in operating conditions can affect the relationship between measured responses and tool condition. The talk will discuss how signal processing, physics-based modelling, probabilistic analysis and machine learning can be combined to improve the interpretation of monitoring data, quantify confidence in monitoring outcomes, and develop condition indicators that remain meaningful under changing operating environments.
The broader focus is on how monitoring methodologies can move beyond detection under controlled conditions towards reliable, interpretable and deployable engineering decision support.
About the speaker
Dr Panpan Xu is a Research Engineer at the University of Sheffield Advanced Manufacturing Research Centre (AMRC), specialising in sensing, process monitoring and data-driven condition assessment for advanced manufacturing and engineering systems. She received her PhD in Mechanical Engineering from Imperial College London, where her research focused on ultrasonic guided-wave structural health monitoring and the development of digital twin and probabilistic approaches for assessing monitoring reliability under environmental variability.
At AMRC, her research has expanded into deployable industrial monitoring and intelligent manufacturing, including vibration-based tool condition monitoring, robotic machining, welding process monitoring and hydrogen storage monitoring. Her current research interests centre on translating multi-source sensing data into trusted and actionable engineering information through physics-informed modelling, signal processing, machine learning and digital twins.