Our research addresses critical challenges in structural dynamics and monitoring, with applications spanning multiple domains. Applications include:
Structural Health and Condition Monitoring
We develop data-driven and physics-informed models to monitor the integrity and performance of critical assets such as offshore wind turbines, marine engines, and civil infrastructure. Our methods enable early damage detection, predictive maintenance, and improved reliability under real-world operational and environmental conditions.
Data-driven fatigue life assessment
We develop advanced methods for predicting fatigue life in complex loading scenarios. Our approach combines surrogate modelling for efficient estimation of damage-equivalent loads with spectral techniques for fatigue analysis under uniaxial, multiaxial, and non-proportional conditions. These tools enable accurate, computationally efficient fatigue assessments for modern engineering structures.
Digitalization of physical systems
Digitalization transforms physical assets into intelligent virtual models, enabling real-time monitoring, predictive analytics, and lifecycle optimization. By integrating sensor data with our dynamic modelling methods, we create Digital Twins that bridge the gap between physical behaviour and digital insights for smarter engineering decisions.
Virtual sensing
Virtual sensing uses advanced models to estimate hard-to-measure structural responses from limited data. Here, we combine utilized both data-driven and updated physical models for accurate, uncertainty-aware predictions in monitoring and maintenance.
Pattern recognition and novelty detection in dynamic systems
We apply our advanced modelling techniques to identify patterns and detect anomalies in dynamic systems. Beyond engineering, these methods support medical signal processing, enabling pathology detection in heart and brain signals for improved diagnostic accuracy.