The full potential of predictive maintenance has not yet been utilised. Current solutions focus on individual steps of the predictive maintenance cycle and only work for very specific settings. The overarching challenge of predictive maintenance is to leverage these individual building blocks to obtain a framework that supports optimal maintenance and asset management. The PrimaVera project has identified four obstacles to tackle in order to utilise predictive maintenance at its full potential: lack of orchestration and automation of the predictive maintenance workflow, inaccurate or incomplete data and the role of human and organisational factors in data-driven decision support tools. Furthermore, an intuitive generic applicable predictive maintenance process model is presented in this paper to provide a structured way of deploying predictive maintenance solutions https://doi.org/10.3390/app10238348 LinkedIn: https://www.linkedin.com/in/john-bolte-0856134/
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Optimization of aviation maintenance, repair, and overhaul (MRO) operations has been of high interest in recent years for both the knowledge institutions and the industrial community as a total of approximately $70 billion has been spent on MRO activities in 2018 which represents around 10% of an airline’s annual operational cost (IATA, 2019). Moreover, the aircraft MRO tasks vary from routine inspections to heavy overhauls and are typically characterized by unpredictable process times and material requirements. Especially nowadays due to the unprecedent COVID-19 crisis, the aviation sector is facing significant challenges, and the MRO companies strive to strengthen their competitive position and respond to the increasing demand for more efficient, cost-effective, and sustainable processes. Currently, most maintenance strategies employ preventive maintenance as an industrial standard, which is based on fixed and predetermined schedules. Preventive maintenance is a long-time preferred strategy, due to increased flight safety and relatively simple implementation (Phillips et al., 2010). However, its main drawback stems from the fact that the actual time of failure and the replacement interval of a component are hard to predict resulting in an inevitable suboptimal utilization of material and labor. This has two repercussions: first, the reduced availability of assets, the reduced capacity of maintenance facilities, and the increased costs for both the MRO provider and the operator. Second, the increased waste from an environmental standpoint, as the suboptimal use of assets, is also associated with wasted remaining lifetime for aircraft parts which are replaced, while this isn’t yet necessary (e.g., Nguyen et al., 2019).The recently introduced, condition-based maintenance (CBM) and predictive maintenance (PdM) data-driven strategies aim to reduce maintenance costs, maxi-mize availability, and contribute to sustainable operations by offering tailored pro-grams that can potentially result in optimally planned, just-in-time maintenance meaning reduction in material waste and unneeded inspections.
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Europe faces significant challenges in maintaining its aging infrastructure due to extreme weather events, fluctuating groundwater levels, and rising sustainability demands. Ensuring the safety and longevity of infrastructure is a critical priority, especially for public organizations responsible for asset management. Digital technologies have the potential to facilitate the scaling and automation of infrastructure maintenance while enabling the development of a data-driven standardized inspection methodology. This extended abstract is the first phase of a study that examines current structural inspection methods and lifecycle monitoring activities of the Dutch public and private entities. The preliminary findings presented here indicate a preference for data-driven approaches, though challenges in data collection, processing, personnel resources and analysis remain. The future work will experiment integrating advanced tools, such as artificial intelligence supported visual inspection, on the existing inspection datasets of these authorities for quantifying their readiness levels to the fully automated digital inspections.
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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.Research-00764
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.
