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 política ótima de CBM incluíram 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

An Evaluation of End of Maintenance Dates and Lifetime Buy Estimations for Electronic Systems Facing Obsolescence

The business of supporting legacy electronic systems is challenging due to mismatches between the system support life and the procurement lives of the systems' constituent components. Legacy electronic systems are threatened with Diminishing Manufacturing Sources and Material Shortages (DMSMS)-type obsolescence, and the extent of their system support lives based on existing replenishable and non-replenishable resources may be unknown. This thesis describes the development of the End of Repair/End of Maintenance (EOR/EOM) model, which is a stochastic discrete-event simulation that follows the life history of a population of parts and cards and operates from time-to-failure distributions that are either user-defined, or synthesized from observed failures to date. The model determines the support life (and support costs) of the system based on existing inventories of spare parts and cards, and optionally harvesting parts from existing cards to further extend the life of the system. The model includes: part inventory segregation, modeling of part inventory degradation and periodic inventory inspections, and design refresh planning. A case study using a real legacy system comprised of 117,000 instances of 70 unique cards and 4.5 million unique parts is presented. The case study was used to evaluate the system support life (and support costs) through a series of different scenarios: obsolete parts with no failure history and never failing, obsolete parts with no failure history but immediately incurring their first failures with and without the use of part harvesting. The case study also includes analyses for recording subsequent EOM and EOR dates, sensitivity analyses for selected design refreshes that maximize system sustainment, and design refresh planning to ensure system sustainment to an end of support date. Lifetime buys refer to buying enough parts from the original manufacturer prior to the part's discontinuance in order to support all forecasted future part needs throughout the system's required support life. This thesis describes the development of the Lifetime Buy (LTB) model, a reverse-application of the EOR/EOM model, that follows the life history of an electronic system and determines the number of spares required to ensure system sustainment. The LTB model can generate optimum lifetime buy quantities of parts that minimizes the total life-cycle cost associated with the estimated lifetime buy quantity

Models of Transportation and Land Use Change: A Guide to the Territory

Modern urban regions are highly complex entities. Despite the difficulty of modeling every relevant aspect of an urban region, researchers have produced a rich variety models dealing with inter-related processes of urban change. The most popular types of models have been those dealing with the relationship between transportation network growth and changes in land use and the location of economic activity, embodied in the concept of accessibility. This paper reviews some of the more common frameworks for modeling transportation and land use change, illustrating each with some examples of operational models that have been applied to real-world settings.Transport, land use, models, review network growth, induced demand, induced supply

Power system reliability enhancement with reactive power compensation and operational risk assessment with smart maintenance for power generators

Modern power systems incorporate advanced contingency measures with the aim of enhancing system performance. Among them, the strategical installation of reactive power compensators into a power system is commonly practised to minimize power losses and improve system reliability. Such a practice requires a robust optimization technique that could reduce the computational burden and provide optimal planning and operation of the compensators. This thesis proposes an advanced optimization technique, named as Accelerated Quantum Particle Swarm Optimization (AQPSO) to determine the optimal placement, sizing and dispatch strategy of the reactive power compensators with the aim of improving the system level reliability. The uniqueness of the technique is the incorporation of the concept ‘best observation’, which accelerates the search towards the optimal solution. The implementation of advanced maintenance strategies is another common contingency measure used to enhance system performance. In this context, this thesis proposes a novel Smart Maintenance (SM) strategy for power generators that maximize the generation adequacy and provide increased economic benefits in a framework of system reliability. The uniqueness of the SM approach is the incorporation of the ‘obsolescence’ state through the stages of the bathtub curve and half-arch shape to model the aging process and then quantify the operational risk of the generators using fuzzy logic theory. Further, SM combines the proposed AQPSO and Sequential Median Latin Hypercube to obtain a comprehensive maintenance schedule. The investigation presented in this thesis contributes with novel AQPSO-based algorithms to enhance power system reliability with the operation of reactive power compensation; a more realistic and accurate aging reliability model of power generators; a detailed SM mathematical framework and an algorithm for the scheduling of proactive maintenance of generators of small and large-power systems. The proposed models are significant in the journey to the smart operation of a power system with diverse levels of applications