About This Course
The module covers Structural Health Monitoring (SHM) systems for wind turbines, including fundamental concepts, sensing technologies, and data analysis methods for damage detection and condition assessment. It introduces the full SHM framework, from data acquisition and signal processing to feature extraction and statistical model development, with a focus on applications in onshore and offshore wind energy systems. Particular emphasis is placed on physics-based approaches, including structural dynamics, vibration-based monitoring, and modal analysis of turbine components such as blades, tower, and support structures. Advanced non-destructive techniques are also addressed, with a focus on ultrasonic and wave-based methods for detecting defects in composite materials and critical structural elements. The module also covers selected data-driven techniques, including pattern recognition and machine learning methods for anomaly detection. Practical aspects such as sensor selection, environmental variability, and integration with monitoring systems are discussed in the context of improving reliability and enabling condition-based maintenance of wind turbines.
Requirements
Bachelor degree in engineering or related technical fields. Basic knowledge of mechanics and signal analysis is recommended but not required.
Course Staff
Staff Member #1
Beata Zima is an Associate Professor at Gdańsk University of Technology. Her research focuses on Structural Health Monitoring (SHM) and non-destructive testing of engineering structures, with particular emphasis on wind turbine blades and other large-scale structures. Her expertise includes ultrasonic guided waves, vibration- and modal-based diagnostics, damage detection and localization, and data-driven SHM methods. She combines numerical modelling with experimental research, including full-scale and large-scale testing of composite wind turbine blades under controlled environmental conditions.
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