Vehicle System Integration, Optimization, and Robustness Recent Developments in Time-dependent Reliability and Design for Lifecycle Cost

Reliability is an important engineering requirement for consistently delivering acceptable product performance through time. As time progresses, the product may fail prematurely due to component degradation and uncertainty in design and/or operating conditions. The degradation of reliability with time increases the lifecycle cost due to potential warranty costs, repairs and loss of market share. In design for lifecycle cost, we must account for the product quality, and time-dependent reliability. Quality is a measure of our confidence that the product conforms to specifications as it leaves the factory. Reliability depends on 1) the probability that the system will perform its intended function successfully for a specified interval of time (no hard failure), and 2) on the probability that the system response will not exceed an objectionable by the customer or operator, threshold for extended periods of time (no soft failure). Quality is time-independent, while reliability is time-dependent. Both quality and reliability are greatly affected by variability and uncertainty. In this talk, recent developments of a design methodology considering the product lifecycle cost, will be presented. Future work will be also summarized. The method determines the optimal design of time-dependent and often degrading, multi-response systems, by minimizing the cost during the life of the product. The constraints account for both hard and soft failures. The conformance of multiple responses is treated in a series-system fashion. The lifecycle cost includes the production cost, the inspection cost and an expected variable cost. These costs depend on quality and reliability. The key to our approach is the calculation of the so-called system cumulative distribution function for a series of time-variant limit-state functions. This is done using an equivalent time-invariant “composite” limit state which is then used to calculate all significant most probable points and the probability of failure.