Publisher's Synopsis
Reliability, in general, is the ability of an object (or process or service) to fulfil the demanded tasks and meet the specifications under given conditions. The specifications (i.e. technical parameters) must be written in the accompanying technical documentation. The conditions of use must also be specified, for example, the temperature range, in which the object will keep the assumed parameters. Reliable operation is interrupted or terminated by failures. Failure is an event leading to the loss of the ability to fulfil the demanded tasks and meet the specifications. Examples are fracture of a component due to overloading or fatigue, collapse of a structure, loss of electric contact, unacceptable deformations or wear, or some parameters out of the allowable limits. Reliability is usually characterized by the probability of failure or by the time to failure. If failure is considered as a single event (e.g. collapse of a bridge), regardless of the time, only its probability is of interest. Early product development phases are characterized by fundamental uncertainties in the reliability modeling process. Such uncertainties may have several reasons: uncertain or incomplete component data, uncertainty about the influencing factors, vague estimations of failure functions and coarse-grained system models. On the other hand, just this phase allows design changes without the loss of a substantial amount of time and money. Methods that help to calculate system reliability from sparse and uncertain data therefore can be a great support for product designers. Methods for Reliability Assessment and Optimization explains the fundamental concepts and tools for the processes and services. Several probabilistic frameworks have been proposed that model and preserve uncertainty and disagreement in reliability analysis. This Book gives some of the commercial programs for reliability assessment. They range from simple ones at moderate prices and suitable for a limited range of problems to program systems for the analysis of complex problems. For getting practice and for the solution of simple problems, the reader can create own programs based on universal software such as Excel, although their possibilities are limited. A simple Monte Carlo simulation program VaP enables the probabilistic analysis of a user-defined function G(x) depending on one or more random input variables. Several types of probability distributions can be used. Methods for reliability assessment and optimization are thus very important.
