The following article on explainable machine learning under the leadership of Sercan Alipek has been accepted for publication in Sensors journal.
Abstract:
This paper evaluates a data-driven classification approach of operational wind turbine blades based on consecutive tower-radar measurements that are each compressed in a two-dimensional slow-time to range representation (radargram). Like many real-world machine learning systems, installed tower-radar systems face some key challenges: (i) transferability to new operational contexts, (ii) impediments due to evolving environmental and operational conditions (EOCs), and (iii) limited explainability of their deep neural decisions. These challenges are addressed here with a set of structured machine learning studies. The unique field data comes from a sensor box equipped with a frequency-modulated continuous wave (FMCW) radar (33.4-36 GHz frequency range). Relevant parts of the radargram that contribute to a decision of the used convolutional neural networks were identified by a class-sensitive visualization technique named GuidedGradCAM (Guided Gradient-weighted Class Activation Mapping). The following main contributions are provided to the field of tower-radar monitoring (TRM) in the context of wind energy applications: (i) every individual rotor blade holds an amount of characteristic structural features revealed by the radar sensor which can be used to discriminate rotor blades from the same turbine via neural networks (ii) those unique features are not agnostic to changing EOCs (iii) pixel-level distortions reveal the necessity of low-level information for a precise rotor blade classification.
More information:
Alipek, S. ; Kexel, C. & Moll, J., Explainable Machine Learning for Tower-Radar Monitoring of Wind Turbine Blades: Fine-grained Blade Recognition under Changing Operational Conditions, 2026, Sensors (accepted: January 2026)
