Explainable Machine Learning for Tower-Radar Monitoring of Wind Turbine Blades: Fine-Grained Blade Recognition Under Changing Operational Conditions
Sercan Alipek, Christian Kexel, Jochen Moll
TL;DR
This paper explores how radar data can be used to identify wind turbine blades using machine learning, even as conditions change.
Contribution
The study reveals unique blade features detectable by radar and shows these features remain useful despite changing environmental conditions.
Findings
Each rotor blade has unique structural features detectable by radar.
These features remain identifiable under changing environmental and operational conditions.
Low-level radar information is crucial for accurate blade classification.
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…
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Taxonomy
TopicsWind Energy Research and Development · Structural Health Monitoring Techniques · Machine Fault Diagnosis Techniques