Tracht, KirstenKirstenTrachtGoch, GertGertGochSchuh, PeterPeterSchuhSorg, MichaelMichaelSorgWesterkamp, Jan F.Jan F.Westerkamp2024-08-052024-08-052013-04-280007-8506https://media.suub.uni-bremen.de/handle/elib/816210.26092/elib/3196The feasibility of maintenance processes relies on the availability of spare parts. Spare part inventory planning is capital intensive. It is based on demand forecasting, which possesses a high potential in reducing inventories. Even if condition monitoring systems are installed in technical systems, condition monitoring information is barely used to predict the failure probability of units. Therefore, an enhanced forecast model, which integrates SCADA information, has been developed. This leads to more accurate spare part demand forecasts. The approach presented in the paper is based on data mining, the proportional hazards model (PHM) and a binomial distribution. It has been validated with maintenance data of wind energy systems.enCC BY-NC-ND 4.0 (Attribution-NonCommercial-NoDerivatives)https://creativecommons.org/licenses/by-nc-nd/4.0/MaintenancePredictive modelReliability600Artikel/Aufsatzurn:nbn:de:gbv:46-elib81629