Outputs (143)

Reliability sensitivity analysis of coherent systems based on survival signature (2018)
Journal Article
Huang, X., & Coolen, F. (2018). Reliability sensitivity analysis of coherent systems based on survival signature. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, 232(6), 627-634. https://doi.org/10.1177/1748006x18754974

The reliability sensitivity can be used to rank distribution parameters of system components concerning their impacts on the system’s reliability. Such information is essential to purposes such as component prioritization, reliability improvement, an... Read More about Reliability sensitivity analysis of coherent systems based on survival signature.

Mean residual life of coherent systems consisting of multiple types of dependent components (2018)
Journal Article
Eryilmaz, S., Coolen, F., & Coolen-Maturi, T. (2018). Mean residual life of coherent systems consisting of multiple types of dependent components. Naval Research Logistics, 65(1), 86-97. https://doi.org/10.1002/nav.21782

Mean residual life is a useful dynamic characteristic to study reliability of a system. It has been widely considered in the literature not only for single unit systems but also for coherent systems. This article is concerned with the study of mean r... Read More about Mean residual life of coherent systems consisting of multiple types of dependent components.

Marginal and joint reliability importance based on survival signature (2017)
Journal Article
Eryilmaz, S., Coolen, F., & Coolen-Maturi, T. (2018). Marginal and joint reliability importance based on survival signature. Reliability Engineering & System Safety, 172, 118-128. https://doi.org/10.1016/j.ress.2017.12.002

Marginal and joint reliability importance measures have been found to be useful in optimal system design. Various importance measures have been defined and studied for a variety of system models. The results in the literature are mostly based on the... Read More about Marginal and joint reliability importance based on survival signature.

Nonparametric predictive inference for future order statistics (2017)
Journal Article
Coolen, F., Coolen-Maturi, T., & Alqifari, H. (2018). Nonparametric predictive inference for future order statistics. Communications in Statistics - Theory and Methods, 47(10), 2527-2548. https://doi.org/10.1080/03610926.2017.1342834

This paper presents nonparametric predictive inference for future order statistics. Given data consisting of n real-valued observations, m future observations are considered and predictive probabilities are presented for the r-th ordered future obser... Read More about Nonparametric predictive inference for future order statistics.

An imprecise statistical method for accelerated life testing using the power-Weibull model (2017)
Journal Article
Yin, Y., Coolen, F., & Coolen-Maturi, T. (2017). An imprecise statistical method for accelerated life testing using the power-Weibull model. Reliability Engineering & System Safety, 167, 158-167. https://doi.org/10.1016/j.ress.2017.05.045

Accelerated life testing provides an interesting challenge for quantification of the uncertainties involved, in particular due to the required linking of the units’ failure times, or failure time distributions, at different stress levels. This paper... Read More about An imprecise statistical method for accelerated life testing using the power-Weibull model.

Mixture Models for Prediction from Time Series, with Application to Energy Use Data (2017)
Journal Article
Qarmalah, N. M., Einbeck, J., & Coolen, F. P. (2017). Mixture Models for Prediction from Time Series, with Application to Energy Use Data. Archives of data science. Series A, 2(1), 1-15. https://doi.org/10.5445/ksp/1000058749/07

This paper aims to use mixture models to produce predictions from time series data. Given data of the form (ti, yi), i = 1, . . . , T , we propose a mix- ture model localized at time point tT with the k-th component as yi = mk (ti) + εik with mixing... Read More about Mixture Models for Prediction from Time Series, with Application to Energy Use Data.

Simulation Methods for System Reliability Using the Survival Signature (2017)
Journal Article
Patelli, E., Feng, G., Coolen, F., & Coolen-Maturi, T. (2017). Simulation Methods for System Reliability Using the Survival Signature. Reliability Engineering & System Safety, 167, 327-337. https://doi.org/10.1016/j.ress.2017.06.018

Recently, the survival signature has been presented as a summary of the structure function which is sufficient for computation of common reliability metrics and has the crucial advantage that it can be applied to systems with components whose failure... Read More about Simulation Methods for System Reliability Using the Survival Signature.

Optimisation of maintenance policy under parameter uncertainty using portfolio theory (2016)
Journal Article
Wu, S., Coolen, F., & Liu, B. (2017). Optimisation of maintenance policy under parameter uncertainty using portfolio theory. IISE Transactions, 49(7), 711-721. https://doi.org/10.1080/24725854.2016.1267881

In reliability mathematics, optimisation of maintenance policy is derived based on reliability indexes such as the reliability or its derivatives (e.g., the cumulative failure intensity or the renewal function) and the associated cost information. Th... Read More about Optimisation of maintenance policy under parameter uncertainty using portfolio theory.

On the structure function and survival signature for system reliability (2016)
Journal Article
Coolen, F., & Coolen-Maturi, T. (2016). On the structure function and survival signature for system reliability. Safety and Reliability, 36(2), 77-87. https://doi.org/10.1080/09617353.2016.1219936

The quantification of reliability of systems has, for decades, been based on the structure function, which expresses functioning of a system given the states of its components. One problem of the structure function is that, in its simplest form, for... Read More about On the structure function and survival signature for system reliability.

Bayesian nonparametric system reliability using sets of priors (2016)
Journal Article
Walter, G., Aslett, L., & Coolen, F. (2017). Bayesian nonparametric system reliability using sets of priors. International Journal of Approximate Reasoning: Uncertainty in Intelligent Systems, 80(1), 67-88. https://doi.org/10.1016/j.ijar.2016.08.005

An imprecise Bayesian nonparametric approach to system reliability with multiple types of components is developed. This allows modelling partial or imperfect prior knowledge on component failure distributions in a flexible way through bounds on the f... Read More about Bayesian nonparametric system reliability using sets of priors.