Crossover of Failure Time Distributions in a Model of Time-Dependent Fracture

An important question in the theory of fracture is what kind of lifetime distributions may exist for materials under load. Here, this is studied in the context of a one-dimensional fracture model with local load sharing under a constant external load, “creep.” Simulations of the system with Weibull distributed initial lifetimes for the elements show that the limiting distribution follows from extreme statistics and takes the Gumbel form eventually, with longer and longer crossovers in the system size from a Weibull-like distribution, depending on the initial Weibull exponent.

Resource Allocation for System Reliability Assessment Using Accelerated Life Testing

Accelerated life test (ALT) has been widely used to accelerate the product reliability assessment process by testing a product at higher than nominal stress conditions. For a system with multiple components, the tests can be performed at component-level or system-level. The data at these two levels require different amount of resources to collect and carry different values of information for system reliability assessment. Even though component-level tests are cheap to perform, they cannot account for the correlations between the failure time distributions of different components. While system-level tests can naturally account for the complicated dependence between component failure time distributions, the required testing efforts are much higher than that of component-level tests. This research proposes a novel resource allocation framework for ALT-based system reliability assessment. A physics-informed load model is first employed to bridge the gap between component-level tests and system-level tests. An optimization framework is then developed to effectively allocate testing resources to different types of tests. The information fusion of component-level and system-level tests allows us to accurately estimate the system reliability with a minimized requirement on the testing resources. Results of two numerical examples demonstrate the effectiveness of the proposed framework.

Resource Allocation for System Reliability Analysis Using Accelerated Life Testing

Accelerated life test (ALT) has been widely used to accelerate the product reliability assessment process by testing product at higher than nominal stress conditions. For a system with multiple components, the tests can be performed at component-level or system-level. The data at these two levels require different amount of resources to collect and carry different values of information for system reliability assessment. Even though component-level tests are cheap to perform, they cannot account for the correlations between the failure time distributions of different components. While system-level tests can naturally account for the complicated dependence between component failure time distributions, the required testing efforts are much higher than that of component-level tests. This research proposes a novel resource allocation framework for ALT-based system reliability assessment. A physics-informed load model is first employed to bridge the gap between component-level tests and system-level tests. An optimization framework is then developed to effectively allocate testing resources to different types of tests. The information fusion of component-level and system-level tests allows us to accurately estimate the system reliability with a minimized requirement on the testing resources. Results of one numerical example demonstrate the effectiveness of the proposed framework.

Reliability Analysis for Degrading Systems with 100% Quality Inspection after Burn-In

Integrated quality and reliability models should be developed to improve system performance simultaneously, because quality and reliability are inherently related in a sense that quality inspection and monitoring decisions impact anticipated reliability and failure time distributions. Especially for degrading systems, important decisions including burn-in, quality inspection and preventive maintenance should be incorporated into an integrated model considering manufacturing variability and associated failure mechanisms. For various linear and non-linear degradation models, this paper develops conditional reliability functions and truncated failure time distributions considering the impacts of burn-in and quality inspection at manufacturing phase. It shows that burn-in and quality inspection policies have significant impacts on reliability performance of products in field operation. Numerical examples are provided to demonstrate the results. The developed reliability models can be readily used for optimizing burn-in, quality inspection and maintenance decisions simultaneously.