447,616 research outputs found

    Modelling and managing reliability growth during the engineering design process

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    [This is a keynote speech presented at the 2nd International Conference on Design Engineering and Science, discussing modelling and managing reliability growth during the engineering process.] Reliability is vital for safe and efficient operation of systems. Decisions about the configuration and selection of parts within a system, and the development activities to prove the chosen design, will influence the inherent reliability. Modelling provides a mechanism for explicating the relationship between the engineering activities and the statistical measures of reliability so that useful estimates of reliability can be obtained. Reliability modelling should be aligned to support the decisions taken during design and development. We examine why and how a reliability growth model can be structured, the type of data required and available to populate them, the selection of relevant summary measures, the process for updating estimates and feeding back into design to support planning decisions. The modelling process described is informed by our theoretical background in management science and our practical experience of working with UK industry

    Cost-benefit modelling for reliability growth

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    Decisions during the reliability growth development process of engineering equipment involve trade-offs between cost and risk. However slight, there exists a chance an item of equipment will not function as planned during its specified life. Consequently the producer can incur a financial penalty. To date, reliability growth research has focussed on the development of models to estimate the rate of failure from test data. Such models are used to support decisions about the effectiveness of options to improve reliability. The extension of reliability growth models to incorporate financial costs associated with 'unreliability' is much neglected. In this paper, we extend a Bayesian reliability growth model to include cost analysis. The rationale of the stochastic process underpinning the growth model and the cost structures are described. The ways in which this model can be used to support cost-benefit analysis during product development are discussed and illustrated through a simple case

    Statistical modelling of software reliability

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    During the six-month period from 1 April 1991 to 30 September 1991 the following research papers in statistical modeling of software reliability appeared: (1) A Nonparametric Software Reliability Growth Model; (2) On the Use and the Performance of Software Reliability Growth Models; (3) Research and Development Issues in Software Reliability Engineering; (4) Special Issues on Software; and (5) Software Reliability and Safety

    Multi-scale reliability analysis of composite structures – Application to the Laroin footbridge

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    This work aims at developing a new methodology for the reliability assessment of composite structures and their design optimization. It relies on the coupling of well established methods: homogenization scheme for the mechanical modelling of composite materials and reliability methods to account for their inherent variability. Moreover, such approach is based on an accurate treatment of inherent uncertainties of these mechanical systems at various scales, including microscopic and macroscopic levels, that provides newperspectives for structural design. As an illustration, we propose to apply the multi-scale reliability analysis on the case of the Laroin footbridge (France) with carbon–epoxy stay cables. Since the reliability assessment of such structure is evaluated through the fibre failure, numerical simulations require the coupling of reliability methods, finite element modelling to derive macroscopic loading within cables and micromechanics to estimate the effective elastic properties of composite and local responses within constituents. Results demonstrate the feasibility of the coupled approach at a structure scale and its main interests for the optimization phase of materials and engineering structures

    Multivariate reliability modelling with empirical Bayes inference

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    Recent developments in technology permit detailed descriptions of system performance to be collected and stored. Consequently, more data are available about the occurrence, or non-occurrence, of events across a range of classes through time. Typically this implies that reliability analysis has more information about the exposure history of a system within different classes of events. For highly reliable systems, there may be relatively few failure events. Thus there is a need to develop statistical inference to support reliability estimation when there is a low ratio of failures relative to event classes. In this paper we show how Empirical Bayes methods can be used to estimate a multivariate reliability function for a system by modelling the vector of times to realise each failure root cause

    Construction of asymmetric copulas and its application in two-dimensional reliability modelling

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    Copulas offer a useful tool in modelling the dependence among random variables. In the literature, most of the existing copulas are symmetric while data collected from the real world may exhibit asymmetric nature. This necessitates developing asymmetric copulas that can model such data. In the meantime, existing methods of modelling two-dimensional reliability data are not able to capture the tail dependence that exists between the pair of age and usage, which are the two dimensions designated to describe product life. This paper proposes two new methods of constructing asymmetric copulas, discusses the properties of the new copulas, and applies the method to fit two-dimensional reliability data that are collected from the real world

    Towards Financial Cloud Framework - Modelling and Benchmarking of Financial Assets in Public and Private Clouds

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    Literature identifies two problems in clouds: (i) there are few financial clouds and (ii) portability of financial modelling from desktop to cloud is challenging. To address these two problems, we propose the Financial Cloud Framework (FCF), which contains business models, forecasting, sustainability, modelling, simulation and benchmarking of financial assets. We select Monte Carlo Methods for pricing and Black Scholes Model for risk analysis. Our objective is to demonstrate portability, speed, accuracy and reliability of financial models in the clouds, and present how modelling, simulation and benchmarking fit into FCF. Experiments and benchmark are performed in public and private clouds, where portability, speed, accuracy and reliability from desktop to clouds are successfully demonstrated

    Modelling network travel time reliability under stochastic demand

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    A technique is proposed for estimating the probability distribution of total network travel time, in the light of normal day-to-day variations in the travel demand matrix over a road traffic network. A solution method is proposed, based on a single run of a standard traffic assignment model, which operates in two stages. In stage one, moments of the total travel time distribution are computed by an analytic method, based on the multivariate moments of the link flow vector. In stage two, a flexible family of density functions is fitted to these moments. It is discussed how the resulting distribution may in practice be used to characterise unreliability. Illustrative numerical tests are reported on a simple network, where the method is seen to provide a means for identifying sensitive or vulnerable links, and for examining the impact on network reliability of changes to link capacities. Computational considerations for large networks, and directions for further research, are discussed
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