The fleet and breakdown data that is read in is cleaned up in several steps and prepared in such a way that it can be safely processed further. Not only invalid and duplicate values are removed, but also non-monotonic entries. The process has been robustified on the basis of many real test cases so that this step can be completely automated. This saves users time-consuming manual filtering of individual data.

An optimal model structure is automatically sought for the available failure data in order to determine the actual number of failure components. Various model candidates are trained with the Expectation Maximisation Algorithm and compared with each other. The optimum structure is determined on the basis of statistical criteria so that no manual intervention is necessary. This makes the application possible even for non-experts.

The probabilistic service life forecast determines an individual ageing model for each vehicle. This makes it possible to estimate the mileage distribution of a fleet much more accurately than with the widely used three-point estimate. As probabilistic methods, the underlying ageing models are able to depict future ageing as a probability distribution. Depending on the data available, vehicle-specific data or fleet information is used for this purpose.

The method is based on the consistent use of probabilistic models, both for mileage estimation and for the probability of failure. The underlying uncertainties in the data are taken into account within both models and offset against each other. As a result, the method provides not just a single point estimate, but the entire probability of occurrence.

When looking at the service life curve alone, it is only possible to make a generalised statement about the number of failures at a certain mileage. In reality, however, vehicles drive differently and there is no fleet in which all vehicles have the same mileage. To predict the number of failures, the probabilistic service life forecast therefore considers the vehicle-specific mileage and forecasts this into the future in order to draw conclusions about future failures from vehicle-specific failure risks.

If the mileage of the current fleet is still too low, a reference fleet can be used as the basis for the forecast. This makes it possible to compensate for seasonal effects, e.g. in the case of motorbikes or convertibles.

Users can specify whether defective parts are to be replaced by identical parts that can fail again or by new, improved parts. This allows the forecast to be adapted to the actual problem and optimised.

Users can specify whether the failures observed to date already describe all failure mechanisms or whether further failure mechanisms may occur in the future. In this way, expert knowledge or past experience can be taken into account for the forecast.

The user has the option of defining a warranty period within which the vehicles are categorised as relevant. As soon as individual vehicles fall outside the warranty period, they are no longer considered. On this basis, future warranty costs can be estimated more accurately.