CC BY 4.0Klein, SamuelSamuelKleinEmge, JuliaJuliaEmgeKoster, DirkDirkKoster2024-09-182024-09-202024-09-182024https://doi.org/10.24406/h-475287https://publica.fraunhofer.de/handle/publica/47528710.24406/h-475287Many of our critical infrastructures are old and heavily used, including bridges, roads, and utility systems. The traditional approach of inspecting and maintaining these infrastructures via fixed maintenance intervals is often outdated. Predictive maintenance, which is based on the evaluation of raw data, can increase maintenance efficiency, as continuous monitoring enables a faster response to changes in structures. There is a significant discrepancy between the current state of technological knowledge and the actual technological equipment of such structures. The project presented here aims to close this gap by using modern measurement technology, edge AI processing, and autonomous data evaluation. This should offer significant benefit to inspectors and operators by providing them with additional information and resources to save labor and costs while increasing safety. waste streams.enKünstliche IntelligenzÜberwachungkritische InfrastrukturDDC::600 Technik, Medizin, angewandte Wissenschaftenjournal article