448 research outputs found
Wind turbine maintenance optimisation: principles of quantitative maintenance optimisation.
Maintenance optimisation is a crucial issue for industries that utilise physical assets due to its impact on costs, risks and performance. Current quantitative maintenance optimisation techniques include Modelling System Failures MSF (using monte-carlo simulation) and Delay-Time Maintenance Model (DTMM). The MSF investigates equipment failure patterns by using failure distribution, resource availability and spareholdings to determine optimum maintenance requirements. The DTMM approach examines equipment failure patterns by considering failure consequences, inspection costs and the period to determine optimum inspection intervals. This paper discusses the concept, relevance and applicability of the MSF and DTMM techniques to the wind energy industry. Institutional consideration as well as the benefits of practical implementation of the techniques are highlighted and discussed
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
Reliability and Condition-Based Maintenance Analysis of Deteriorating Systems Subject to Generalized Mixed Shock Model
For successful commercialization of evolving devices (e.g., micro-electro-mechanical systems, and biomedical devices), there must be new research focusing on reliability models and analysis tools that can assist manufacturing and maintenance of these devices. These advanced systems may experience multiple failure processes that compete against each other. Two major failure processes are identified to be deteriorating or degradation processes (e.g., wear, fatigue, erosion, corrosion) and random shocks. When these failure processes are dependent, it is a challenging problem to predict reliability of complex systems. This research aims to develop reliability models by exploring new aspects of dependency between competing risks of degradation-based and shock-based failure considering a generalized mixed shock model, and to develop new and effective condition-based maintenance policies based on the developed reliability models.
In this research, different aspects of dependency are explored to accurately estimate the reliability of complex systems. When the degradation rate is accelerated as a result of withstanding a particular shock pattern, we develop reliability models with a changing degradation rate for four different shock patterns. When the hard failure threshold reduces due to changes in degradation, we investigate reliability models considering the dependence of the hard failure threshold on the degradation level for two different scenarios. More generally, when the degradation rate and the hard failure threshold can simultaneously transition multiple times, we propose a rich reliability model for a new generalized mixed shock model that is a combination of extreme shock model, δ-shock model and run shock model. This general assumption reflects complex behaviors associated with modern systems and structures that experience multiple sources of external shocks.
Based on the developed reliability models, we introduce new condition-based maintenance strategies by including various maintenance actions (e.g., corrective replacement, preventive replacement, and imperfect repair) to minimize the expected long-run average maintenance cost rate. The decisions for maintenance actions are made based on the health condition of systems that can be observed through periodic inspection. The reliability and maintenance models developed in this research can provide timely and effective tools for decision-makers in manufacturing to economically optimize operational decisions for improving reliability, quality and productivity.Industrial Engineering, Department o
DECISION SUPPORT MODEL IN FAILURE-BASED COMPUTERIZED MAINTENANCE MANAGEMENT SYSTEM FOR SMALL AND MEDIUM INDUSTRIES
Maintenance decision support system is crucial to ensure maintainability and reliability of equipments in production lines. This thesis investigates a few decision support models to aid maintenance management activities in small and medium industries. In order to improve the reliability of resources in production lines, this study introduces a conceptual framework to be used in failure-based maintenance. Maintenance strategies are identified using the Decision-Making Grid model, based on two important factors, including the machines’ downtimes and their frequency of failures. The machines are categorized into three downtime criterions and frequency of failures, which are high, medium and low. This research derived a formula based on maintenance cost, to re-position the machines prior to Decision-Making Grid analysis. Subsequently, the formula on clustering analysis in the Decision-Making Grid model is improved to solve multiple-criteria problem. This research work also introduced a formula to estimate contractor’s response and repair time. The estimates are used as input parameters in the Analytical Hierarchy Process model. The decisions were synthesized using models based on the contractors’ technical skills such as experience in maintenance, skill to diagnose machines and ability to take prompt action during troubleshooting activities. Another important criteria considered in the Analytical Hierarchy Process is the business principles of the contractors, which includes the maintenance quality, tools, equipments and enthusiasm in problem-solving. The raw data collected through observation, interviews and surveys in the case studies to understand some risk factors in small and medium food processing industries. The risk factors are analysed with the Ishikawa Fishbone diagram to reveal delay time in machinery maintenance. The experimental studies are conducted using maintenance records in food processing industries. The Decision Making Grid model can detect the top ten worst production machines on the production lines. The Analytical Hierarchy Process model is used to rank the contractors and their best maintenance practice. This research recommends displaying the results on the production’s indicator boards and implements the strategies on the production shop floor. The proposed models can be used by decision makers to identify maintenance strategies and enhance competitiveness among contractors in failure-based maintenance. The models can be programmed as decision support sub-procedures in computerized maintenance management systems
Markovian arrivals in stochastic modelling: a survey and some new results
This paper aims to provide a comprehensive review on Markovian arrival processes (MAPs),
which constitute a rich class of point processes used extensively in stochastic modelling. Our
starting point is the versatile process introduced by Neuts (1979) which, under some simplified
notation, was coined as the batch Markovian arrival process (BMAP). On the one hand, a general
point process can be approximated by appropriate MAPs and, on the other hand, the MAPs
provide a versatile, yet tractable option for modelling a bursty flow by preserving the Markovian
formalism. While a number of well-known arrival processes are subsumed under a BMAP as
special cases, the literature also shows generalizations to model arrival streams with marks, nonhomogeneous
settings or even spatial arrivals. We survey on the main aspects of the BMAP,
discuss on some of its variants and generalizations, and give a few new results in the context of a
recent state-dependent extension.Peer Reviewe
Reliability applied to maintenance
The thesis covers studies conducted during 1976-79 under a
Science Research Council contract to examine the uses of reliability
information in decision-making in maintenance in the process industries.
After a discussion of the ideal data system, four practical studies
of process plants are described involving both Pareto and distribution
analysis. In two of these studies the maintenance policy was changed
and the effect on failure modes and frequency observed. Hyper-exponentially
distributed failure intervals were found to be common and were explained
after observation of maintenance work practices and development of
theory as being due to poor workmanship and parts. The fallacy that
constant failure rate necessarily implies the optimality of maintenance
only at failure is discussed.
Two models for the optimisation of inspection intervals are
developed; both assume items give detectable warning of impending failure.
The first is based upon constant risk of failure between successive
inspections 'and Weibull base failure distribution~ Results show that
an inspection/on-condition maintenance regime can be cost effective
even when the failure rate is falling and may be better than periodiC
renewals for an increasing failure situation. The second model is first-order Markov. Transition rate matrices are developed and solved
to compare continuous monitoring with inspections/on-condition
maintenance an a cost basis. The models incorporate planning delay
in starting maintenance after impending failure is detected.
The relationships between plant output and maintenance policy
as affected by the presence of redundancy and/or storage between stages
are examined, mainly through the literature but with some original
theoretical proposals.
It is concluded that reliability techniques have many applications
in the improvement of plant maintenance policy. Techniques abound,
but few firms are willing to take the step of faith to set up, even
temporarily, the data-collection facilities required to apply them.
There are over 350 references, many of which are reviewed in the
text, divided into chapter-related sectionso
Appendices include a review of Reliability Engineering Theory,
based on the author's draft for BS 5760(2) a discussion of the 'bath-tub
curves' applicability to maintained systems and the theory connecting
hyper-exponentially distributed failures with poor maintenance
practices
Reliability Analysis And Optimal Maintenance Planning For Repairable Multi-Component Systems Subject To Dependent Competing Risks
Modern engineering systems generally consist of multiple components that interact in a complex manner. Reliability analysis of multi-component repairable systems plays a critical role for system safety and cost reduction. Establishing reliability models and scheduling optimal maintenance plans for multi-component repairable systems, however, is still a big challenge when considering the dependency of component failures. Existing models commonly make prior assumptions, without statistical verification, as to whether different component failures are independent or not. In this dissertation, data-driven systematic methodologies to characterize component failure dependency of complex systems are proposed. In CHAPTER 2, a parametric reliability model is proposed to capture the statistical dependency among different component failures under partially perfect repair assumption. Based on the proposed model, statistical hypothesis tests are developed to test the dependency of component failures. In CHAPTER 3, two reliability models for multi-component systems with dependent competing risks under imperfect assumptions are proposed, i.e., generalized dependent latent age model and copula-based trend-renewal process model. The generalized dependent latent age model generalizes the partially perfect repair model by involving the extended virtual age concept. And the copula-based trend renewal process model utilizes multiple trend functions to transform the failure times from original time domain to a transformed time domain, in which the repair conditions can be treated as partially perfect. Parameter estimation methods for both models are developed. In CHAPTER 4, based on the generalized dependent latent age model, two periodic inspection-based maintenance polices are developed for a multi-component repairable system subject to dependent competing risks. The first maintenance policy assumes all the components are restored to as good as new once a failure detected, i.e., the whole system is replaced. The second maintenance policy considers the partially perfect repair, i.e., only the failed component can be replaced after detection of failures. Both the maintenance policies are optimized with the aim to minimize the expected average maintenance cost per unit time. The developed methodologies are demonstrated by using applications of real engineering systems
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