The developing of maintenance programs (content, organisation, scheduling) within the developing phase of a new technical product generation – e.g. automobiles, electronic consumer goods – is based on experiences, prototype testing and the analysing of actual damage cases of the previous product generation in the use phase. Therefore, engineers analysing the field data regarding failure symptoms and root causes based on field data. The goal is the calculation of statistical models, e.g. Weibull model, for mapping the failure behaviour, estimation of failure probabilities and the prognosis of further damage cases. These risk analytics are an important contribution for the planning of preventive maintenance activities regarding the subsequently product generation. The problem is the documentation of the field data of the actual product fleet in the usage phase. As a rule, the manufacturer get the knowledge from the damaged products via trade organisation (e.g. automobiles: life span variable kilometrage driving distance, operating time, damage symptom). But often, the manufacturers get no information regarding the non-failed units. This problem of censored data has to be considered within risk analytics: Methods for handling censored data are published by Johnson, Nelson, Kaplan-Meier, Eckel and Pauli. However, these methods lead to different results regarding failure probabilities and subsequently fitted statistical models. This paper shows a fundamental research study regarding the methodologies for censored data handling within risk analytics as a fundament for scheduled maintenance strategies in automotive engineering. The overarching goal is the comparison of methods for the estimation of risks and failure probabilities within an automobile fleet based on censored data: The impact of the use of these methods regarding the expected failure probability is worked out. The research work is explained based on an automotive case study.