Articles
Applied Stochastic Models in Business and Industry (15264025)41(1)
This article compares the information content of a sample for two competing Bayesian approaches. One approach follows Dennis Lindley's Bayesian standpoint, where one begins by formulating a prior for a parameter related to the problem in question and incorporates a likelihood to transition to a posterior. This contrasts with the usual Bayesian approach, where one starts with a likelihood model, formulates a prior distribution for its parameters, and derives the corresponding posterior. In both cases, the sample information content is measured using the difference between the prior and posterior entropies. We investigate this contrast in the context of learning about the moments of a variable. The maximum entropy principle is used to construct the likelihood model consistent with the given moment parameters. This likelihood model is then combined with the prior information on the parameters to derive the posterior. The model parameters are the Lagrange multipliers for the moment constraints. A prior for the moments induces a prior for the model parameters; however, the data provides differing amounts of information about them. The results obtained for several problems show that the information content using the two formulations can differ significantly. Additional information measures are derived to assess the effects of operating environments on the lifetimes of system components. © 2025 John Wiley & Sons Ltd.
Reliability Engineering and System Safety (18790836)257
An essential goal of reliability engineering is maintaining technical systems optimally, ensuring continuous operation. Random inspections of working systems are crucial in some industries to meet safety and quality standards. This paper proposes an opportunistic optimal age-based preventive maintenance (PM) strategy for n-component (n>1) coherent systems compromising redundant components. The system begins operating at t=0, with a PM time scheduled at TPM. To reduce the risk of unexpected and catastrophic failures, the system is inspected at a random time X before TPM. Based on the information about the number of failed components, m, the operator decides whether to perform the PM action early at X or to allow the system to continue operating on (X,TPM). By incorporating a cost function that considers cost parameters related to failures, we determine the optimal values for the decision variables TPM and m. The paper's results rely on the notion of the system signature as a powerful tool to represent the reliability of n-component systems. To evaluate the effectiveness of the proposed model, we conduct a comprehensive analysis of coherent systems using graphical and numerical examples. In particular, we consider a well-investigated parallel system related to the generator parts in a wind turbine. Using a data set related to the failure times of generators, the applicability of the proposed PM policy is illustrated. © 2025 Elsevier Ltd
