The systems that statisticians are asked to assess, such as nuclear weapons, infrastructure networks, supercomputer codes and munitions, have become increasingly complex. It is often costly to conduct full system tests. As such, we present a review of methodology that has been proposed for addressing system reliability with limited full system testing. The first approaches presented in this paper are concerned with the combination of multiple sources of information to assess the reliability of a single component. The second general set of methodology addresses the combination of multiple levels of data to determine system reliability. We then present developments for complex systems beyond traditional series/parallel representations through the use of Bayesian networks and flowgraph models. We also include methodological contributions to resource allocation considerations for system relability assessment. We illustrate each method with applications primarily encountered at Los Alamos National Laboratory.

/pdf/advances-in-data-combination-analysis-and-collection-for-3nsbx6bqys.pdf

Advances in Data Combination, Analysis and Collection for System Reliability Assessment.

Reliability analysis of complex systems is a critical issue in reliability engineering. Motivated by practical needs, this paper investigates a Bayesian approach for system reliability assessment and prediction with multilevel heterogeneous data sets. Two major imperatives have been handled in the proposed approach, which provides a comprehensive Bayesian framework for the integration of multilevel heterogeneous data sets. In particular, the pass-fail data, lifetime data, and degradation data at different system levels are combined coherently for system reliability analysis. This approach goes beyond the alternatives that deal with solely multilevel pass-fail or lifetime data, and presents a more practical tool for real engineering applications. In addition, the indices for reliability assessment and prediction are constructed coherently within the proposed Bayesian framework. It gives rise to a natural manner of incorporating this approach into a decision-making procedure for system operation and management. The effectiveness of the proposed approach is illustrated with reliability analysis of a navigation satellite.

http://relialab.org/Upload/files/Bayesian%20method%20for%20system%20reliability%20-%20Peng%20Weiwen.pdf

A Bayesian Approach for System Reliability Analysis With Multilevel Pass-Fail, Lifetime and Degradation Data Sets

Some issues are discussed relative to the gap between theory and practice in the area of statistical process monitoring (SPM). Among other issues, it is argued that the collection and use of baseline data in Phase I needs a greater emphasis. Also, the use of sample ranges in practice to estimate process standard deviations deserves reconsideration. A discussion is given on the role of modeling in SPM. Then some work on profile monitoring and the effect of estimation error on Phase II chart performance is summarized. Finally, some ways that researchers could influence practice more effectively are discussed along with how SPM research could become more useful to practitioners.

Bridging the gap between theory and practice in basic statistical process monitoring

We propose a Bayesian approach for assessing the reliability of multicomponent systems. Our models allow us to evaluate system, subsystem, and component reliability using multilevel information. Data are collected over time, and include binary, lifetime, and degradation data. We illustrate the methodology through two examples and discuss extensions. Supplementary materials are available online.

Bayesian Methods for Estimating System Reliability Using Heterogeneous Multilevel Information

One of the most powerful features of Bayesian analyses is the ability to combine multiple sources of information in a principled way to perform inference. This feature can be particularly valuable in assessing the reliability of systems where testing is..

https://www.tandfonline.com/doi/pdf/10.1080/08982112.2016.1211889

Bayesian reliability: Combining information

A system with several components may undergo full system pass/fail testing as well as quality assurance testing at the single component level. The component tests are informative about system reliability, although they measure different things than the full system tests. We present a Bayesian framework for integrating the two types of test data for better reliability estimates. Our formulation allows the reliability to depend on covariates such as age. One result of the inference is a better understanding of the relationship between component tests and system performance. We illustrate the ideas using a small subsystem of a larger (proprietary) system.

http://www.researchgate.net/profile/Alyson_Wilson2/publication/255605039_RELIABILITY_MODELING_USING_BOTH_SYSTEM_TEST_AND_QUALITY_ASSURANCE_DATA/links/0f3175352d50d35207000000.pdf

Greg Anderson

Papers

Reliability Modeling using Both System Test and Quality Assurance Data