During its complete service life, a turbo jet engine undergoes a continuous change of condition due to environmentally caused deterioration. The description of this change for an individual engine or even a fleet of engines results in a computational challenge requiring tailor-made parallelization. A digital service twin model is presented, which is capable to predict the engine condition on part level at any time of its life cycle. Deterioration is expressed on the level of relevant engine parts, whereby the interaction of multiple damage phenomena can be considered. Changes in component efficiency and capacity are deduced from part deterioration, allowing to map the gradual deterioration depending on its operational conditions. Flights based on Engine Health Monitoring data, ADS-B - data or synthetically created flights are used to run the model. Deviations, which arise due to these different data bases, are discussed. Besides the validation of the digital service twin, a sensitivity analysis is presented for input parameters which are not prescribed by commonly used data bases. This embraces the effects of time resolution, model quality and selected assumptions of unknown input parameters. Finally, the outcome of the digital service twin is presented for a reference deterioration phenomenon.

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