TY - RPRT A1 - Krankenhagen, Rainer A1 - Chaudhuri, Somsubhro A1 - Stamm, Michael A1 - Lapšanská, Ivana A1 - Aderhold, J. A1 - Schlüter, F. T1 - EvalTherm – Evaluierung der passiven Thermografie für die Zustandsbewertung von Rotorblättern an Windenergieanlagen N2 - Der Bericht gibt einen Überblick über im Rahmen des Projektes durchgeführte Forschungsarbeiten sowie ausgewählte Ergebnisse. Er wurde zusammen mit dem FhI für Holzforschung (WKI) erstellt. KW - Rotorblattinspektion KW - Passive Thermografie KW - Feldmessungen KW - Windturbine PY - 2025 N1 - Schlussbericht des Projektes N1 - Laufzeit: 1.9.2020 – 31.8.2024 N1 - Das Verbundprojekt wurde im Rahmen des 7. Energieforschungsprogramms "Innovationen für die Energiewende" gefördert N1 - Das Vorhaben wurde mit Mitteln des Bundesministeriums für Wirtschaft und Klima unter dem Förderkennzeichen 03EE3035A/B gefördert SP - 1 EP - 67 PB - Technische Informationsbibliothek (TIB) CY - Hannover AN - OPUS4-62647 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Stamm, Michael T1 - The KI-VISIR Reference Dataset: A Compilation of Thermal and Visual Inspection Data for Quantifying Leading Edge Rain Erosion N2 - This presentation introduces thermography (thermal imaging) as a key method for non-destructive testing (NDT) of wind turbine rotor blades. By leveraging solar heating and aerodynamic effects, passive thermography detects internal defects, delaminations, and surface issues like leading edge erosion. We discuss field measurements, the role of AI/Machine Learning (LATODA) for automated defect classification, and its importance for improving O&M efficiency and lifetime extension in the wind energy sector. T2 - WindEurope Annual Event 2025 CY - Copenhagen, Denmark DA - 08.04.2025 KW - Thermography KW - Wind Turbine Blades KW - AI KW - NDT KW - KI-VISIR PY - 2025 AN - OPUS4-64721 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Stamm, Michael T1 - Einführung thermografischer Methoden in die Ermüdungsprüfung unter Laborbedingungen sowie bei Ganzblatt-Rotorblattprüfungen N2 - Der Vortrag der Bundesanstalt für Materialforschung und -prüfung (BAM) stellt die Einführung und Anwendung thermografischer Methoden zur Ermüdungsprüfung und Ganzblatt-Rotorblattprüfung vor. Die BAM, tätig in Forschung, Prüfung und Beratung, trägt zur technischen Sicherheit bei und unterstützt die Energiewende. Die Abteilung 8.3 "Thermographic Methods" arbeitet an der Forschung, Entwicklung und Anwendung thermografischer Prüf- und Überwachungstechniken. Passive Thermografie ist eine bildgebende Messung der Oberflächentemperatur, die Informationen über das Innere von Bauteilen liefert. Durch Nutzung von Sonneneinstrahlung und Temperaturschwankungen können auch große Strukturen wie Rotorblätter geprüft werden. Thermische Inspektionen visualisieren Temperaturunterschiede, die durch Material-, Struktur-, Aerodynamik- und Oberflächeneigenschaften sowie Reibungswärme entstehen. Bei rotierenden Rotorblättern wird die Thermografie eingesetzt, um Strömungsphänomene (laminar/turbulent), Oberflächendefekte (z.B. Regen-Erosion) und innere Defekte zu erkennen. Ein Schlüsselprojekt ist die AI-gestützte Bildklassifizierung (LATODA) von über 1200 Roh-Infrarotbildern für die Mangeldetektion. Ziel ist die Entwicklung von zuverlässigen, automatisierten, robotergestützten Verfahren, um Schäden schnell zu erkennen und so Effizienzverluste (AEP-loss) zu vermeiden und die Lebensdauer von Windenergieanlagen zu verlängern. T2 - Mitgliederversammlung des BWE- Sachverständigenbeirates CY - Hannover, Germany DA - 5.9.2025 KW - Thermografie KW - Wind KW - Rotorblätter KW - Inspektion PY - 2025 AN - OPUS4-64719 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Stamm, Michael T1 - Thermografische Ferninspektion von Windenergieanlagen im Betrieb –� Potenziale und Herausforderungen N2 - Rotorblätter zählen zu den kostenintensivsten Komponenten von Windenergieanlagen in Bezug auf Wartung und Ausfallzeiten. Mit der zunehmenden Blattlänge – aktuell bis zu 115 m – steigen auch die Anforderungen an effektive Inspektionsmethoden. Die Bundesanstalt für Materialforschung und -prüfung (BAM) entwickelt hierfür eine passive thermografische Ferninspektion vom Boden aus, die die klassische visuelle Prüfung durch Industriekletterer ergänzen oder perspektivisch ersetzen soll. Feldmessungen und Laborexperimente belegen das Potenzial dieser Technik, sowohl strömungsbedingte thermische Signaturen als auch strukturelle Anomalien innerhalb der Rotorblätter zu erfassen. Eine besondere Herausforderung liegt in der Trennung dieser überlagerten Effekte sowie in der eingeschränkten Kenntnis der inneren Blattstruktur aufgrund fehlender Designdaten. Um die Methode zur Marktreife zu führen, sind Fortschritte in der Bildverarbeitung, etwa durch eine patentierte Differenzbildung, erforderlich. Der Beitrag stellt die zugrunde liegende Messtechnik, Ergebnisse einer groß angelegten Feldstudie mit 30 Anlagen sowie die identifizierten physikalischen Einflussgrößen (Strömung, Struktur, Emissivität) vor und gibt einen Ausblick auf die nächsten Entwicklungsschritte. T2 - Thermo25 CY - Munich, Germany DA - 12.11.2025 KW - Thermography KW - Wind Turbine Blades KW - Wind KW - Rotorblätter PY - 2025 AN - OPUS4-64718 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Stamm, Michael A1 - Sridaran Venkat, Ramanan T1 - Harnessing the power of thermal imagery and visual inspection- a mean for reliable damage detection of wind turbine rotor blades N2 - Generation of green electricity as part of the energy transition is leading to a growing market in the wind energy sector all over the world. Maintenance and inspection are key to the reliability, safety and efficiency of wind turbines, the regular maintenance of rotor blades focuses on damage such as erosion on the leading edge of the profile, delamination and thermal cracks due to lightning strikes. To date, visual inspection by technicians (climbers) has been the state of the art and it is time consuming besides posing safety risk for themselves. Recently, drone-based inspections using visual cameras have become more common, enabling fast, reliable and cost-effective inspections. However, no internal damage to the rotor blades can be detected during such an inspection. Thermography is a recognised method for detecting damage beneath the surface of an object, which has been promoted and further developed at BAM for years. To enhance the accuracy and reliability of wind turbine blade inspection, the fusion o T2 - 11th European Workshop on Structural Health Monitoring CY - Potsdam, Germany DA - 10.06.2024 KW - Wind rotor blade inspection KW - Thermography KW - Data fusion KW - Drone inspection KW - Multi-sensors PY - 2024 AN - OPUS4-62515 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Stamm, Michael T1 - AI-Assisted thermographic and visual classification of leading-edge erosion of wind turbine blades N2 - The wind industry is crucial for carbon neutrality, with turbines featuring blades over 100 meters long. Regular inspections, often manual and visual, struggle to capture subsurface damage or airflow dynamics. Leading-edge erosion, caused by rain and hail, significantly reduces turbine efficiency. The Federal Institute for Materials Research and Testing (BAM) in Berlin is working with industry partners to classify leading-edge damage and estimate yield loss using ground-based thermographic images. These images visualize airflow disruptions caused by erosion. AI models, trained on 1500 thermographic images, can detect and classify this damage. BAM aims to create a reference dataset by 2024, using data from 30 wind turbines. This dataset will include simultaneous thermographic and high-resolution visual images. The project also explores predicting stall and calculating performance loss due to erosion. A secure data platform facilitates data exchange and federated learning, enhancing AI systems with diverse data. KW - NDT KW - Thermography KW - Wind Turbine Blades KW - AI KW - KI-VISIR PY - 2024 UR - https://source.asnt.org/226h005/ SN - 0025-5327 VL - 82 IS - 6 SP - 14 EP - 15 AN - OPUS4-62452 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Junker, Birgit A1 - Stamm, Michael T1 - No downtime thermographic rotor blade inspection N2 - In this presentation, the KI-VISIR Reference Dataset, created by the Bundesanstalt für Materialforschung und -prüfung (BAM) will be discussed. It includes thermographic and visual inspection data of 30 operational wind turbines. This dataset aims to support the maintenance and inspection of wind turbines, which are increasingly being built across Germany and Europe. The data is publicly available and helps in the development of digital methods for damage classification and inspection. T2 - AMI Wind Turbine Blades CY - Düsseldorf, Germany DA - 10.12.2024 KW - Thermography KW - Wind Turbine Blades KW - AI KW - KI-VISIR KW - NDT PY - 2024 AN - OPUS4-62498 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Stamm, Michael T1 - Damage detection of wind turbine blades with thermographic inspection and AI-based classification N2 - In this presentation, the possibilities, limitations and challenges of thermographic rotor blade inspection will be discussed. Among other things, the data from the KI-VISIR reference data set will be discussed. This data was also used to train an AI-based image recognition system. T2 - WindEurope Technology Workshop 2024 CY - Dublin, Ireland DA - 10.06.2024 KW - Thermography KW - Wind Turbine Blades KW - AI KW - KI-VISIR KW - NDT PY - 2024 AN - OPUS4-62499 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Lay, Vera A1 - Baensch, Franziska A1 - Skłodowska, Anna A1 - Fritsch, Tobias A1 - Stamm, Michael A1 - Prabhakara, Prathik A1 - Johann, Sergej A1 - Sturm, Patrick A1 - Kühne, Hans-Carsten A1 - Niederleithinger, Ernst T1 - Multi–sensory Monitoring and Non–destructive Testing of New Materials for Concrete Engineered Barrier Systems N2 - The crucial part of nuclear waste storage is the construction of sealing structures made of reliable, safe and well–understood materials. We present an extended analysis of long-term multi–sensory monitoring and non–destructive testing (NDT) inspection of two laboratory specimens aiming at potential materials for sealing structures for nuclear waste repositories. Specimens with a volume of 340 litres made from newly developed alkali–activated materials (AAM) and established salt concrete (SC) were analysed using embedded acoustic emission and wireless radio-frequency identification (RFID) sensors, ultrasonic echo imaging, active thermography, and X–ray computed tomography. The monitoring analysis showed lower heat of reaction and 50% less acoustic emission events in AAM compared to SC. However, due to the surface effects of the AAM material, the number of acoustic emission events increased significantly after approximately two months of monitoring. Subsequently performed NDT inspections reliably located embedded sensors and confirmed the absence of major cracks or impurities. The presented laboratory results show the feasibility and potential of comprehensive NDT monitoring and inspection to characterise cementitious and alternative materials as well as the need for multi–parameter long–term monitoring. Thus, our study demonstrates that tailored NDT investigations will help to develop safe sealing structures for nuclear waste repositories. KW - Radioactive waste KW - Barrier KW - Concrete KW - AAM KW - Non-destructive testing PY - 2024 DO - https://doi.org/10.3151/jact.22.516 SN - 1347-3913 VL - 22 IS - 9 SP - 516 EP - 529 PB - Japan Concrete Institute AN - OPUS4-61461 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Stamm, Michael T1 - Semi-automated detection of rain erosion damages on turbine blades with passive thermography and AI image processing N2 - The European Green Deal and the global fight against climate change call for more and larger wind turbines in Europe and around the world. To meet the increasing demand for maintenance and inspection, partly autonomous methods of remote inspection are increasingly being developed in addition to industrial climbers performing the inspection. Rotor blades are exposed to extreme weather conditions throughout their lifetime of 20 years, and the leading edge erodes over time. These erosion damages change the aerodynamic features of blades and can cause structural damages. The estimated annual energy production (AEP) losses caused by erosion damages are between 0.5% and 2% per year. The classification of the severity of a rain erosion damage and the quantification of the resulting AEP losses for cost efficient repair and maintenance efforts and improved power production of wind turbines are subject of scientific research. For the inspection of wind turbine rotor blades, passive thermography is an option that has been used to detect both internal damage [3, 4] as well as erosion on the surface [5, 6]. The advantage is that, given suitable boundary conditions, not only the rain erosion damage itself but also temperature differences caused by the resulting turbulences can be observed on the surface of the blade. Turbulences reduce the efficiency of the rotor blades and result in AEP losses. Optimised thermography inspections can contribute to detect and to evaluate rain erosion damages. The thermal inspection lasts 10 minutes per turbine and is performed while the turbine is in full operation, avoiding downtime and lost opportunities for the turbine owner which are usually caused by conventional blade inspections. The timely inspection procedure is complemented by an automatic data evaluation and results in a considerable number of inspected wind turbines in a certain period of time. A fully convolutional network (FCN) is implemented for the automated evaluation of thermal images. In the presented study, more than 1000 thermographic images of blades were annotated, augmented and used to train and test the FCN. The aim is the precise marking of thermal signatures caused by erosion damage at the leading edge. The area size of the detected temperature difference caused by turbulences was used to identify and categorise damages. Certain strategies were adopted to group small individual indications as one large damage, in order to develop simplification rules based on realistic thermal imaging resolution. T2 - Wind Energy Science Conference (WESC) 2023 CY - Glasgow, Scotland DA - 23.05.2023 KW - Non-destructive testing KW - Thermography KW - Wind turbine blade PY - 2023 AN - OPUS4-58498 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -