As every new generation of civil aircraft creates more on-wing data and fleets gradually become more connected with the ground, an increased number of opportunities can be identified for more effective Maintenance, Repair and Overhaul (MRO) operations. Data are becoming a valuable asset for aircraft operators. Sensors measure and record thousands of parameters in increased sampling rates. However, data do not serve any purpose per se. It is the analysis that unleashes their value. Data analytics methods can be simple, making use of visualizations, or more complex, with the use of sophisticated statistics and Artificial Intelligence algorithms. Every problem needs to be approached with the most suitable and less complex method. In MRO operations, two major categories of on-wing data analytics problems can be identified. The first one requires the identification of patterns, which enable the classification and optimization of different maintenance and overhaul processes. The second category of problems requires the identification of rare events, such as the unexpected failure of parts. This cluster of problems relies on the detection of meaningful outliers in large data sets. Different Machine Learning methods can be suggested here, such as Isolation Forest and Logistic Regression. In general, the use of data analytics for maintenance or failure prediction is a scientific field with a great potentiality. Due to its complex nature, the opportunities for aviation Data Analytics in MRO operations are numerous. As MRO services focus increasingly in long term contracts, maintenance organizations with the right forecasting methods will have an advantage. Data accessibility and data quality are two key-factors. At the same time, numerous technical developments related to data transfer and data processing can be promising for the future.
The aim of this paper is to show the benefits of enhancing classic Risk Based Inspection (without fatigue monitoring data) with an Advisory Hull Monitoring System (AHMS) to monitor and justify lifetime consumption to provide more thorough grounds for operational, inspection, repair and maintenance decisions whilst demonstrating regulatory compliance.
The digitalization of railroad infrastructure is aimed at the improvement of maintenance and construction activities. Currently, inspections are done manually, with a domain expert classifying objects. This is a challenging task, considering the Netherlands has more than 3,400 km of railways that need to be inspected and maintained.
In 2024, the Dutch government set a new plan for offshore wind farms to become the Netherlands' largest power source by 2032, aiming for 21 GW of installed capacity. By 2050, they expect between 38 and 72 GW of offshore wind power to meet climate-neutral energy goals. Achieving this depends heavily on efficient wind turbines (WTs) operation, but WTs face issues like cavitation, bird strikes, and corrosion, all of which reduce energy output. Regular Inspection and Maintenance (I&M) of WTs is crucial but remains underdeveloped in current wind farms. Presently, I&M tasks are done by on-site workers using rope access, which is time-consuming, costly, and dangerous. Moreover, weather conditions and personnel availability further hinder the efficiency of these operations. The number of operational WTs is expected to rise in the coming years, while the availability of service personnel will keep on declining, highlighting the need for safer and more cost-effective solutions. One promising innovation is the use of aerial robots, or drones, for I&M tasks. Recent developments show that they can perform tasks requiring physical interaction with the environment, such as WT inspections and maintenance. However, the current design of drones is often task-specific, making it financially unfeasible for small and medium-sized enterprises (SMEs) – providing services in WT inspection and maintenance- to adopt. Together with knowledge institutes, SMEs and innovation clusters, this project addresses these urgent challenges by exploring the question of how to develop a modular aerial robot that can be easily and intuitively deployed in offshore environments for inspecting and maintaining WTs to facilitate SMEs adoption of this technology? The goal is to create a modular drone that can be equipped with various tools for different tasks, reducing financial burdens for SMEs, improving worker safety, and facilitating efficient green energy production to support the renewable energy transition.
