5,462 research outputs found
The safety case and the lessons learned for the reliability and maintainability case
This paper examine the safety case and the lessons learned for the reliability and maintainability case
Expert Elicitation for Reliable System Design
This paper reviews the role of expert judgement to support reliability
assessments within the systems engineering design process. Generic design
processes are described to give the context and a discussion is given about the
nature of the reliability assessments required in the different systems
engineering phases. It is argued that, as far as meeting reliability
requirements is concerned, the whole design process is more akin to a
statistical control process than to a straightforward statistical problem of
assessing an unknown distribution. This leads to features of the expert
judgement problem in the design context which are substantially different from
those seen, for example, in risk assessment. In particular, the role of experts
in problem structuring and in developing failure mitigation options is much
more prominent, and there is a need to take into account the reliability
potential for future mitigation measures downstream in the system life cycle.
An overview is given of the stakeholders typically involved in large scale
systems engineering design projects, and this is used to argue the need for
methods that expose potential judgemental biases in order to generate analyses
that can be said to provide rational consensus about uncertainties. Finally, a
number of key points are developed with the aim of moving toward a framework
that provides a holistic method for tracking reliability assessment through the
design process.Comment: This paper commented in: [arXiv:0708.0285], [arXiv:0708.0287],
[arXiv:0708.0288]. Rejoinder in [arXiv:0708.0293]. Published at
http://dx.doi.org/10.1214/088342306000000510 in the Statistical Science
(http://www.imstat.org/sts/) by the Institute of Mathematical Statistics
(http://www.imstat.org
Modelo de apoio à decisão para a manutenção condicionada de equipamentos produtivos
Doctoral Thesis for PhD degree in Industrial and Systems EngineeringIntroduction: This thesis describes a methodology to combine Bayesian control chart
and CBM (Condition-Based Maintenance) for developing a new integrated model. In
maintenance management, it is a challenging task for decision-maker to conduct an
appropriate and accurate decision. Proper and well-performed CBM models are
beneficial for maintenance decision making. The integration of Bayesian control chart
and CBM is considered as an intelligent model and a suitable strategy for forecasting
items failures as well as allow providing an effectiveness maintenance cost. CBM
models provides lower inventory costs for spare parts, reduces unplanned outage, and
minimize the risk of catastrophic failure, avoiding high penalties associated with losses
of production or delays, increasing availability. However, CBM models need new
aspects and the integration of new type of information in maintenance modeling that can
improve the results. Objective: The thesis aims to develop a new methodology based on
Bayesian control chart for predicting failures of item incorporating simultaneously two
types of data: key quality control measurement and equipment condition parameters. In
other words, the project research questions are directed to give the lower maintenance
costs for real process control. Method: The mathematical approach carried out in this
study for developing an optimal Condition Based Maintenance policy included the
Weibull analysis for verifying the Markov property, Delay time concept used for
deterioration modeling and PSO and Monte Carlo simulation. These models are used for
finding the upper control limit and the interval monitoring that minimizes the
(maintenance) cost function. Result: The main contribution of this thesis is that the
proposed model performs better than previous models in which the hypothesis of using
simultaneously data about condition equipment parameters and quality control
measurements improve the effectiveness of integrated model Bayesian control chart for
Condition Based Maintenance.Introdução: Esta tese descreve uma metodologia para combinar Bayesian control chart
e CBM (Condition- Based Maintenance) para desenvolver um novo modelo integrado.
Na gestão da manutenção, é importante que o decisor possa tomar decisões apropriadas
e corretas. Modelos CBM bem concebidos serão muito benéficos nas tomadas de
decisão sobre manutenção. A integração dos gráficos de controlo Bayesian e CBM é
considerada um modelo inteligente e uma estratégica adequada para prever as falhas de
componentes bem como produzir um controlo de custos de manutenção. Os modelos
CBM conseguem definir custos de inventário mais baixos para as partes de substituição,
reduzem interrupções não planeadas e minimizam o risco de falhas catastróficas,
evitando elevadas penalizações associadas a perdas de produção ou atrasos, aumentando
a disponibilidade. Contudo, os modelos CBM precisam de alterações e a integração de
novos tipos de informação na modelação de manutenção que permitam melhorar os
resultados.Objetivos: Esta tese pretende desenvolver uma nova metodologia baseada
Bayesian control chart para prever as falhas de partes, incorporando dois tipos de
dados: medições-chave de controlo de qualidade e parâmetros de condição do
equipamento. Por outras palavras, as questões de investigação são direcionadas para
diminuir custos de manutenção no processo de controlo.Métodos: Os modelos
matemáticos implementados neste estudo para desenvolver uma polÃtica ótima de CBM
incluÃram a análise de Weibull para verificação da propriedade de Markov, conceito de
atraso de tempo para a modelação da deterioração, PSO e simulação de Monte Carlo.
Estes modelos são usados para encontrar o limite superior de controlo e o intervalo de
monotorização para minimizar a função de custos de manutenção.Resultados: A
principal contribuição desta tese é que o modelo proposto melhora os resultados dos
modelos anteriores, baseando-se na hipótese de que, usando simultaneamente dados dos
parâmetros dos equipamentos e medições de controlo de qualidade. Assim obtém-se
uma melhoria a eficácia do modelo integrado de Bayesian control chart para a
manutenção condicionada
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