Due to various influences, product lifetime is a stochastic rather than a deterministic value. Therefore, it is commonly described by means of statistic measures, illustrated for example by the widely known model of the stress strength interference . A convergence of loading capacity and occurring stresses over time, e. g. due to degradation effects, leads to an interference, and thus a product failure. The resulting probability of when a failure occurs, the expected time period without failures respectively, is described as reliability. Reliability is consequently defined as “the probability that a product does not fail under given functional und environmental conditions during a defined period of time” .
A variety of qualitative and quantitative methods is available to assess the expected reliability of products and components  as well as for the acquisition of trustworthy data required for a meaningful stochastic description of the product lifetime [2, 3]. Complemented by the calculation of confidence intervals, which accommodates the uncertainty of how good the used samples reflect the true population parameters , these approaches are an essential part of Reliability Engineering in industrial practice.
However, in addition to the importance of a systematic reliability assessment, various authors also emphasize the relevance of design decisions for the assurance of the required product performance over time in case of wear, corrosion, etc. [1, 5, 6]. Elsayed  for example clarifies that reliability indicators “may be used as a measure of the system’s success in providing its function properly during its design Iife” indicating that reliability largely depends on the quality of designs .
The overall objective of Reliability Engineering consequently is the comprehensive analysis, assurance and improvement of the expected product reliability by means of suitable qualitative and quantitative methods. Including a variety of tasks, such as the systematic description of failure causes, the identification of relevant components, the assessment of failure probabilities extended by a calculation of confidence intervals for the drawn conclusions, Reliability Engineering is a highly complex but, at the same time, extremely important challenge for quality assurance purposes in industrial development projects.
Whereas Meeker  and Nelsen  offer an overview of suitable data acquisition approaches, further information on the calculation of confidence intervals can be found in O’Connor, Kleyner .
 Bertsche, B., Lechner, G.: Reliability in Automotive and Mechanical Engineering. Berlin, Heidelberg, New York: Springer-Verlag 2007.
 Meeker, W., Escobar, L.: Statistical Methods for Reliability Data. New Jersey: John Wiley & Sons, Inc. 1998.
 Nelson, W.: Applied Life Data Analysis. New Jersey: John Wiley & Sons, Inc. 2004.
 O’Connor, P., Kleyner, A.: Practical Reliability Engineering. New Jersey: John Wiley & Sons, Inc. 2012.
 Elsayed, A.: Reliabiltiy Engineering. New Jersey: John Wiley & Sons, Inc. 2012.
 Taguchi, G. et al.: Taguchi’s Quality Engineering Handbook. New Jersey: John Wiley & Sons, Inc. 2005.
 Yang, G.: Life Cycle Reliability Engineering. New Jersey: John Wiley & Sons, Inc. 2007.