TY - INPR A1 - Kimmerle, Sven-Joachim A1 - Dvorsky, Karl A1 - Liess, Hans-Dieter A1 - Avenhaus, Rudolf T1 - Time to Failure under Varying Thermal Stresses N2 - Varying thermal stresses influence significantly the time to failure of electric components as used, for instance, in automotive devices. For applications as autonomous driving a high reliability has to be guaranteed. In this article we discuss how to combine probability distributions for failure. Discrete and continuous changes of the probability distribution in time are both considered. It turns out that the temporal order of the distributions, corresponding to the succession of stresses in applications, is essential. The latter observation restricts the general applicability of the widely used temperature collectives where only the total time of a temperature stress is considered neglecting the order of the stresses. An application of our results are thermal overstress tests on electric components. We may explain yet not well understood measurements for automotive electric cables. KW - Reliability KW - Applied Probability KW - Data Science KW - Physics of Failure KW - Arrhenius Model Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:861-opus4-19126 ER - TY - GEN A1 - Kimmerle, Sven-Joachim A1 - Dvorsky, Karl A1 - Liess, Hans-Dieter T1 - Failure Rates from Data of Field Returns - How to Prove High Reliability for Electric Components? T2 - MATHMOD 2025 (11th Vienna International Conference on Mathematical Modelling) N2 - To determine failure rates is a challenge, if there are only a few failures and a low failure rate should be checked. As an application example, we are interested in failure rates of electrical automotive components for automated/autonomous driving. Basically, three methods are common: (i) exploitation of field data, (ii) standardized handbooks with failure rates, e.g. the FIDES guide, and (iii) laboratory long term exposure tests. We discuss shortly the (dis)advantages of each method and focus on the statistics behind method (i). Moreover, our poster sketches how this can be applied to data as available in industry. KW - Reliabilität KW - Statistik KW - Bordnetz KW - modelling uncertainties and stochastic systems KW - automotive vehicle electrical systems KW - reliability KW - autonomous driving/intelligent autonomous vehicles KW - field data Y1 - 2025 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:861-opus4-29421 ER - TY - GEN A1 - Kimmerle, Sven-Joachim A1 - Dvorsky, Karl A1 - Liess, Hans-Dieter T1 - Failure Rates from Data of Field Returns T2 - MATHMOD Short Contribution Volume 2025 N2 - To determine failure rates is a challenge, if there are only a few failures and typical failure rates are low. As an application example we are interested in failure rates of electrical automotive components for automated/autonomous driving. As method we focus here on the exploitation of field data. Our contribution classifies different approaches from statistics and shows how this can be applied to real-world production figures as available in industry. KW - Mathematische Modellierung KW - Entscheidung bei Unsicherheit KW - Bordnetz KW - Reliabilität KW - Konfidenzbereich KW - modelling uncertainties and stochastic systems KW - automotive vehicle electrical systems KW - reliability KW - failure rates KW - autonomous driving Y1 - 2025 U6 - https://doi.org/10.34726/9011 SP - 23 EP - 24 PB - reposiTUm, TU Wien CY - Wien ER -