Research & scientific contributions
SHM is an interdisciplinary subject that encompasses the fields of physics (structural mechanics), statistics and data science (system identification and machine learning), and electrical engineering (sensors and actuators, sensor networks, internet of things).
While developing these techniques, I aim at helping us understand how structures operate through their lifespan and how natural deterioration processes lead to damage and failures, while improving our capability to detect, locate and assess the severity of damage in a structure.
Long-term analysis of the vibration response of structures
When observed over long analysis intervals, structures will display large variations in their dynamic properties due to the constantly evolving environmental and operational conditions. This may have dramatic effects on their lifetime performance (fatigue accumulation) and damage diagnosis. Back from my PhD years, I’ve been working on different topics associated with this type of slowly-evolving non-stationary behavior. Some of these topics are discussed further below.
Anomaly detection and useful lifetime assessment based on operational data
Complex structures are often instrumented with several sensors monitoring hundreds of variables related to the operation of structural components and systems. The obtained data is characterized by large volumes, but also is heterogeneous and complex. However, important information on the structural condition and remaining lifetime of components could be extracted with the application of proper data analysis algorithms. The selected topics below are examples of such methods, where we use different machine learning algorithms to make sense of the operational data acquired in wind turbines and marine engines.
Machine Learning for Condition Monitoring of Wind Turbine Drivetrains
Vibration-based Condition Monitoring (CM) is based on the analysis of the vibration responses measured on rotating machinery. Such vibration responses are quite complex, and as a result, modern CM algorithms make use of advanced machine learning methods based on deep learning to obtain reliable damage assessment. The construction and training of ML architectures is not straightforward, particularly for wind turbine drivetrains, where the highly variable operational conditions and the lack of information on damages are main challenges. The topics below correspond to recent project ideas, where we attempt to overcome these hurdles.