A general-purpose tool for reliability and availability analysis of repairable systems

Mestrado de dupla diplomação com a UTFPR - Universidade Tecnológica Federal do ParanáThis thesis covers general mathematical and simulation models for the reliability and availability analysis of repairable systems along with estimation methods and model selection criterion. A combined mathematical and simulation model called the Failure-Repair Process is proposed, based on the trend-renewal process. This model is based on a binary state system, where the system may only be in one of two states: working or failed. This model is then integrated into a general-purpose tool, for automated modelling of repairable systems. The classical Akaike information criterion is used to automate the choice of failure and repair models that best fit the available data. Estimators for different performance measures of the systems are studied, such as point and mean availability, rate of occurrence of failures and a first order reliability estimator based on the Kaplan-Meier estimator. Numerical studies are conducted in the proposed non-analytical estimators for the availability, leading to a robust mean availability estimator and a intuitive but sample demanding point availability estimator. Furthermore, a complete quantitative study is conducted on real data from the food industry together with a presentation of the implemented tool functionalities. Overall, the proposed model is able to adapt very well to real data with different characteristics, and, consequently, the resulting performance indicators are befitting to practice.Esta tese aborda modelos matemáticos e de simulação para a análise de confiabilidade e disponibilidade de sistemas reparáveis, juntamente com métodos de estimação e critério de seleção de modelos. Um modelo matemático e de simulação combinados denominado Failure-Repair Process é proposto, baseado no trend-renewal process. Este modelo consiste em um sistema de caracterização binária, onde o sistema pode estar em apenas um de dois estados: em funcionamento ou falha. Este modelo é então integrado em uma ferramenta de uso geral, para modelagem automatizada de sistemas reparáveis. O clássico critério de informação de Akaike é usado para automatizar a escolha dos modelos de falha e reparo que melhor se ajustam aos dados disponíveis. São estudados estimadores para diferentes medidas de desempenho dos sistemas, tais como disponibilidade pontual e média, taxa de ocorrência de falhas e um estimador de confiabilidade de primeira ordem baseado no estimador Kaplan-Meier. Estudos numéricos são conduzidos nos estimadores nãoanalíticos propostos para a disponibilidade, levando a um estimador de disponibilidade média robusto e um estimador de disponibilidade puntual intuitivo, mas que demanda grandes amostras. Além disso, é realizado um estudo quantitativo completo sobre dados reais da indústria de alimentos juntamente com uma apresentação das funcionalidades da ferramenta implementada. De maneira geral, o modelo proposto é capaz de se adaptar muito bem a dados reais com diferentes características e, consequentemente, os indicadores de desempenho resultantes são adequados à prática

Reliability estimation of reinforced slopes to prioritize maintenance actions

Geosynthetics are extensively utilized to improve the stability of geotechnical structures and slopes in urban areas. Among all existing geosynthetics, geotextiles are widely used to reinforce unstable slopes due to their capabilities in facilitating reinforcement and drainage. To reduce settlement and increase the bearing capacity and slope stability, the classical use of geotextiles in embankments has been suggested. However, several catastrophic events have been reported, including failures in slopes in the absence of geotextiles. Many researchers have studied the stability of geotextile-reinforced slopes (GRSs) by employing different methods (analytical models, numerical simulation, etc.). The presence of source-to-source uncertainty in the gathered data increases the complexity of evaluating the failure risk in GRSs since the uncertainty varies among them. Consequently, developing a sound methodology is necessary to alleviate the risk complexity. Our study sought to develop an advanced risk-based maintenance (RBM) methodology for prioritizing maintenance operations by addressing fluctuations that accompany event data. For this purpose, a hierarchical Bayesian approach (HBA) was applied to estimate the failure probabilities of GRSs. Using Markov chain Monte Carlo simulations of likelihood function and prior distribution, the HBA can incorporate the aforementioned uncertainties. The proposed method can be exploited by urban designers, asset managers, and policymakers to predict the mean time to failures, thus directly avoiding unnecessary maintenance and safety consequences. To demonstrate the application of the proposed methodology, the performance of nine reinforced slopes was considered. The results indicate that the average failure probability of the system in an hour is 2.8 ≥ 105 during its lifespan, which shows that the proposed evaluation method is more realistic than the traditional methods

Mathematics in Software Reliability and Quality Assurance

This monograph concerns the mathematical aspects of software reliability and quality assurance and consists of 11 technical papers in this emerging area. Included are the latest research results related to formal methods and design, automatic software testing, software verification and validation, coalgebra theory, automata theory, hybrid system and software reliability modeling and assessment

Fault detection and correction modeling of software systems

Ph.DDOCTOR OF PHILOSOPH

Validation and Improvement of Reliability Methods for Air Force Building Systems

The United States Air Force manages its civil infrastructure resource allocation via a two-dimensional risk model consisting of the consequence of failure and reliability. Air Force civil engineers currently use the BUILDER® Sustainment Management System to estimate and predict reliability at multiple levels within its civil infrastructure systems. Alley (2015) developed and validated a probabilistic model to calculate reliability at the system level. The probabilistic model was found to be a significant improvement over the currently employed BUILDER® model for four major building systems (electrical, HVAC, fire protection, and electrical). This research assessed the performance and accuracy of both the probabilistic and BUILDER® model, focusing primarily on HVAC systems. This research used contingency analysis to assess the performance of each model for HVAC systems at six Air Force installations. Evaluating the contingency analysis results over the range of possible reliability thresholds, it was found that both the BUILDER® and probabilistic model produced inflated reliability calculations for HVAC systems. In light of these findings, this research employed a stochastic method, a Nonhomogenious Poisson Process (NHPP), in an attempt to produce accurate HVAC system reliability calculations. This effort ultimately concluded that the data did not fit a NHPP for the systems considered but posits that other stochastic process can provide more accurate reliability calculations when compared to the two models analyzed

Data-driven Decisions in Service Systems

This thesis makes contributions to help provide data-driven (or evidence-based) decision support to service systems, especially hospitals. Three selected topics are presented. First, we discuss how Little's Law, which relates average limits and expected values of stationary distributions, can be applied to service systems data that are collected over a finite time interval. To make inferences based on the indirect estimator of average waiting times, we propose methods for estimating confidence intervals and for adjusting estimates to reduce bias. We show our new methods are effective using simulations and data from a US bank call center. Second, we address important issues that need to be taken into account when testing whether real arrival data can be modeled by nonhomogeneous Poisson processes (NHPPs). We apply our method to data from a US bank call center and a hospital emergency department and demonstrate that their arrivals come from NHPPs. Lastly, we discuss an approach to standardize the Intensive Care Unit admission process, which currently lacks a well-defined criteria. Using data from nearly 200,000 hospitalizations, we discuss how we can quantify the impact of Intensive Care Unit admission on individual patient's clinical outcomes. We then use this quantified impact and a stylized model to discuss optimal admission policies. We use simulation to compare the performance of our proposed optimal policies to the current admission policy, and show that the gain can be significant

Software reliability modeling and analysis

Ph.DDOCTOR OF PHILOSOPH

Availability and Reliability Analysis of Computer Software Systems Considering Maintenance and Security Issues

Ph.DDOCTOR OF PHILOSOPH

Computing system reliability modeling, analysis, and optimization

Ph.DDOCTOR OF PHILOSOPH