Resumo em Inglês:

ABSTRACT The geometric process (GP) is a stochastic process that was an extension of the renewal process. It was introduced by Lam (1988) in 1988 with an intention to model the failure process of a repairable system whose the times between failures become shorter and shorter after repairs and repair times become longer and longer. The GP has been widely studied in the literature of reliability and maintenance and applied in optimisation of maintenance policies. Some authors have proposed various versions of its extensions (or the GP-like models), including the α-series process, the threshold GP, the extended Poisson process, the doubly GP, and the doubly-ratio GP. Some papers also compare the performance of the GP with that of other models, but not with the performance of the extensions of the GP. This paper therefore reviews the GP-like models, compares the performance of the GP and its extensions in terms of the Akaike information criterion (AIC), the corrected AIC (AICc) and the maximum likelihood (ML) based on 25 real-world datasets. Besides, the least square methods for estimating the parameters in some models are discussed, which is used for model performance of GP and GP-like models. The finding is useful for practitioners in their selection of the GP-like models.

Resumo em Inglês:

ABSTRACT In the development of new designs for systems or improvement of existing ones, the search for effective methods of reliability allocation is fundamental to achieve the minimum requirements. The different allocation methods can involve the consideration of many criteria. Among these criteria, some are quantitative, and others are qualitative, but in both cases, there will be uncertainties associated with the metrics. This article presents an approach based on the probabilistic composition of preferences to allocate the reliability of subsystems considering the uncertainties associated with each criterion considered. The results obtained with application to a case described in the literature demonstrate the efficacy of the proposed approach to deal with this class of problems.

Resumo em Inglês:

ABSTRACT Problems of optimal reliability and redundancy allocation are not new. However, the literature describes two isolated situations, either when redundancy allocation is the target and the reliability metrics of the components are known, or focusing on the reliability allocation, that demands the metrics to be known in advance and the use of some optimization approach. In the present paper, we consider a combined situation, i.e., when both situations must be considered together. To guide the allocation process, we developed a cost function that considers the costs of acquisition, development and/or improvement as a function of monetary effort, along with the reliability target and expected failure costs. The results considering two classical test problems in the literature demonstrated the efficacy of the proposed approach to deal with similar situations.

Resumo em Inglês:

ABSTRACT Increasing human dependence on engineering systems has recently made maintenance a higher priority. Thus, it becomes essential to study the many developments in this field, paying attention to some proposed models that do not appear to be effective in real applications. In this paper, we present a literature review on “maintenance effectiveness”, here defined as the level to which a maintenance policy fulfills its pre-defined objectives or optimizes general maintenance actions, hence improving the productive activities of an organization. Some insights into the research so far developed - such as the relationship between interest in studying maintenance effectiveness in a sector and the severity of catastrophic failures in that sector - and possible future trends - such as the focus on case studies rather than more theoretical papers - are presented. We also found that there are still gaps in the literature that, if explored, will open new routes in potentially promising directions.

Resumo em Inglês:

ABSTRACT Maintenance policies must consider system reliability and the risk of accidents in systems where equipment failures represent a risk. In this context, this work proposes an age replacement policy with Bayesian imperfect repair and considers the “as low as reasonably practicable” (ALARP) principle. The policy determines the age of replacement that minimizes the long-run cost per unit time when the failure rate is ALARP. The model also supposes that failures are either minimally or perfectly repaired, depending on the skill of the maintainer. Numerical applications are performed with and without the disproportion factor in ALARP, both for infinite and one-replacement-cycle horizons. The results show that considering imperfect repair leads to an increase in replacement costs and a decrease in the optimal replacement age when considering the ALARP principle. The model applies to situations where there are conflicts of interest between maintenance management and risk; that is, cases where the aim is to reduce the cost of replacing equipment and minimize the risks. Maintenance policies must consider system reliability and the risk of accidents in systems where equipment failures represent a risk. In this context, this work proposes an age replacement policy with Bayesian imperfect repair and considers the “as low as reasonably practicable” (ALARP) principle. The policy determines the age of replacement that minimizes the long-run cost per unit time when the failure rate is ALARP. The model also supposes that failures are either minimally or perfectly repaired, depending on the skill of the maintainer. Numerical applications are performed with and without the disproportion factor in ALARP, both for infinite and one-replacement-cycle horizons. The results show that considering imperfect repair leads to an increase in replacement costs and a decrease in the optimal replacement age when considering the ALARP principle. The model applies to situations where there are conflicts of interest between maintenance management and risk; that is, cases where the aim is to reduce the cost of replacing equipment and minimize the risks.

Resumo em Inglês:

ABSTRACT We present a novel framework for diagnostics and prognostics for multi-component systems with wear interaction between components. The principal elements of this framework are: health-state indicator extraction using signal-processing; clustering of wear phases using a Gaussian mixture model; a stochastic multivariate wear model; and prediction of the remaining-useful-life of components using particle-filtering. These elements of the framework are illustrated and verified using an experimental platform that generates real data. Our diagnostics study shows that different clusters not only indicate the wear-state, but also the wear-rate of the components. Furthermore, our prognostics study shows that the wear-interaction between components has an significant impact in predicting the remaining-useful-life for components. Thus, we demonstrate, for prognostics and health management, the importance of modeling wear interactions in the prognostic process of multi-component systems.