6,419 research outputs found
Warranty Data Analysis: A Review
Warranty claims and supplementary data contain useful information about product quality and reliability. Analysing such data can therefore be of benefit to manufacturers in identifying early warnings of abnormalities in their products, providing useful information about failure modes to aid design modification, estimating product reliability for deciding on warranty policy and forecasting future warranty claims needed for preparing fiscal plans. In the last two decades, considerable research has been conducted in warranty data analysis (WDA) from several different perspectives. This article attempts to summarise and review the research and developments in WDA with emphasis on models, methods and applications. It concludes with a brief discussion on current practices and possible future trends in WDA
On the Statistical Modeling and Analysis of Repairable Systems
We review basic modeling approaches for failure and maintenance data from
repairable systems. In particular we consider imperfect repair models, defined
in terms of virtual age processes, and the trend-renewal process which extends
the nonhomogeneous Poisson process and the renewal process. In the case where
several systems of the same kind are observed, we show how observed covariates
and unobserved heterogeneity can be included in the models. We also consider
various approaches to trend testing. Modern reliability data bases usually
contain information on the type of failure, the type of maintenance and so
forth in addition to the failure times themselves. Basing our work on recent
literature we present a framework where the observed events are modeled as
marked point processes, with marks labeling the types of events. Throughout the
paper the emphasis is more on modeling than on statistical inference.Comment: Published at http://dx.doi.org/10.1214/088342306000000448 in the
Statistical Science (http://www.imstat.org/sts/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Modelling and Optimizing Imperfect Maintenance of Coatings on Steel Structures
Steel structures such as bridges, tanks and pylons are exposed to outdoor weathering conditions. In order to prevent them from corrosion they are protected by an organic coating system. Unfortunately, the coating system itself is also subject to deterioration. Imperfect maintenance actions such as spot repair and repainting can be done to extend the lifetime of the coating. In this paper we consider the problem of finding the set of actions that minimizes the expected maintenance costs over a bounded horizon. To this end we model the size of the area affected by corrosion by a non-stationary gamma process. An imperfect maintenance action is to be done as soon as a fixed threshold is exceeded. The direct effect of such an action on the condition of the coating is assumed to be random. On the other hand, maintenance may also change the parameters of the gamma deterioration process. It is shown that the optimal maintenance decisions related to this problem are a solution of a continuous-time renewal-type dynamic programming equation. To solve this equation time is discretized and it is verified theoretically that this discretization induces only a small error. Finally, the model is illustrated with a numerical example.non-stationary gamma process;condition-based maintenance;degradation modelling;imperfect maintenance;life-cycle management;renewal-type dynamic programming equation
Techniques for the Fast Simulation of Models of Highly dependable Systems
With the ever-increasing complexity and requirements of highly dependable systems, their evaluation during design and operation is becoming more crucial. Realistic models of such systems are often not amenable to analysis using conventional analytic or numerical methods. Therefore, analysts and designers turn to simulation to evaluate these models. However, accurate estimation of dependability measures of these models requires that the simulation frequently observes system failures, which are rare events in highly dependable systems. This renders ordinary Simulation impractical for evaluating such systems. To overcome this problem, simulation techniques based on importance sampling have been developed, and are very effective in certain settings. When importance sampling works well, simulation run lengths can be reduced by several orders of magnitude when estimating transient as well as steady-state dependability measures. This paper reviews some of the importance-sampling techniques that have been developed in recent years to estimate dependability measures efficiently in Markov and nonMarkov models of highly dependable system
Dynamic Modeling and Statistical Analysis of Event Times
This review article provides an overview of recent work in the modeling and
analysis of recurrent events arising in engineering, reliability, public
health, biomedicine and other areas. Recurrent event modeling possesses unique
facets making it different and more difficult to handle than single event
settings. For instance, the impact of an increasing number of event occurrences
needs to be taken into account, the effects of covariates should be considered,
potential association among the interevent times within a unit cannot be
ignored, and the effects of performed interventions after each event occurrence
need to be factored in. A recent general class of models for recurrent events
which simultaneously accommodates these aspects is described. Statistical
inference methods for this class of models are presented and illustrated
through applications to real data sets. Some existing open research problems
are described.Comment: Published at http://dx.doi.org/10.1214/088342306000000349 in the
Statistical Science (http://www.imstat.org/sts/) by the Institute of
Mathematical Statistics (http://www.imstat.org
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