Component and system reliability assessment from degradation data.

摘要

Reliability estimation of highly reliable components, subsystems and systems has become very difficult using the traditional accelerated life tests. Therefore, there is a need to develop new models that will determine the reliability of such components, systems or subsystems, one of which is modeling a long term performance degradation.; The proposed method is more general than any of the existing ones. It can be applied to any system, subsystem or component whose degradation over time can be identified and measured. It is assumed that the performance degradation is caused by a number, d, of independent degradation mechanisms and each of them is separately modeled by a unique nonlinear, monotonically increasing or decreasing curve as a function of time. The parameters of a degradation model are partitioned into a subset of parameters which are constant for all units and a subset of parameters that vary among units, or a subset of random parameters. To accelerate the degradation processes, random samples of identical units are exposed to stress levels which are higher than use stress levels. To capture the variability among units exposed to the same stress level, the parameters of the degradation model for each unit are estimated first and then the population parameters for a given stress level are estimated. The random parameters are assumed to be multivariate normally distributed, correlated and stress dependent. The multivariate multiple linear regression is applied to the stress dependent parameters and the parameter values at use stress levels are determined. Then, the times to failure are obtained from the degradation model for given degradation mechanisms by extrapolation to the critical level of degradation at which the system, subsystem, or component is considered to be in a failure state. Since the reliability function can not be obtained in a closed form the bootstrap simulation methodology is applied to estimate the system's reliability and the mean life for a single and multiple degradation mechanisms. Two algorithms are developed to obtain the point estimates and confidence intervals for the system's reliability and mean life.