Standard techniques used to analyze a system's response with uncertain system parameters or inputs, are generally Importance sampling methods. Sampling methods require a large number of simulation runs before the system output statistics can be analyzed. As model fidelity increases, sampling techniques become computationally infeasible, and Reliability methods have gained popularity as an analysis method that requires significantly fewer simulation runs. Reliability analysis is an analytic technique which finds a particular point in the design space that can accurately be related to the probability of system failure. However, application to dynamic systems have remained limited. In the following thesis a First Order Reliability Method (FORM) is used to determine the failure probability of a dynamic system due to system/input uncertainties. A pendulum cart system is used as a case study to demonstrate the FORM on a dynamic system. Three failure modes are discussed which correspond to the maximum pendulum angle, the maximum system velocity, and a combined requirement that neither the maximum pendulum angle or system velocity are exceeded. An explicit formulation is generated from the implicit formulation using a Response Surface Methodology, and the FORM is performed using the explicit estimate. Although the analysis converges with minimal simulation computations, attempts to verify FORM results illuminate current limitations of the methodology. The results of this initial study conclude that, currently, sampling techniques are necessary to verify the FORM results, which restricts the potential applications of the FORM methodology. Suggested future work focuses on result verification without the use of Importance sampling which would allow Reliability methods to have widespread applicability. / text

There is a large amount of geotechnical data. By putting it into a database, it can be applied to design reliable offshore foundations. The goal of this research is to improve the efficiency and transparency of the implementation of the previously developed reliability-based framework to streamline the process for analyzing and developing an offshore site in the Gulf of Mexico by looking at spatial variations among data sets. This thesis documents how to store soil behavior information in the database and how to use that information for offshore foundation design. The process is illustrated through observing the steps with figures provided directly from the database so the user can more readily use the database to produce results. This makes the database more transparent for the user to follow the flow of information from input to analysis and to follow the calculation process as well. Enhancements were also made to the database to provide a more readily accessible interface. There is now an allowance of data to streamline the data input process. There is also a set amount of fifty data points to be used in each spatially conditioned analysis. These detailed explanations and consistencies in data collection help the user to understand the models. This database provides a synthetic image of the site using both physical and statistical parameters where there might not be exact data at a desired foundation location. By providing the industry with a database that uses reliability-based design from actual data and spatial variation analysis, this project will continue to provide a more efficient design process. / text

Reliability evaluation by analytic modelling constitute an important issue of designing a reliable multiprocessor system. In this thesis, a model for reliability and fault tolerance analysis of the interconnection network is presented, based on graph theory. Reliability and fault tolerance are considered as deterministic and probabilistic measures of connectivity. Exact techniques for reliability evaluation fail for large multiprocessor systems because of the enormous computational resources required. Therefore, approximation techniques have to be used. Three approaches are proposed, the first by simplifying the symbolic expression of reliability; the other two by applying a hierarchical decomposition to the system. All these methods give results close to those obtained by exact techniques.

Thesis (MEng)--Stellenbosch University, 2015. / ENGLISH ABSTRACT: In recent years, the management of physical assets has become increasingly important, even more so, in asset intensive organisations. This research work presents an overall approach to quantify reliability of rolling stock assets in the rail environment. The current maintenance management system in the case studied is over-reliant on cancellations and delays as reliability measure. The objectives of this study were, therefore, to develop a scientific approach to quantify the reliability of the rolling stock fleet and to develop a maintenance planning model based on system reliability. The research methodology followed made use of failure statistics, failure distributions and the interdependence of different systems to determine the impact of component failures on the overall system reliability. This could then be used to determine the reliability of individual train sets in order to better understand their performance. The reliability measure could be used for predicting component and train set failures as well as to better understand the contribution of maintenance towards reliability, hence the term Reliability Based Maintenance. The model, validated with real data, illustrates how the reliability measure can be used to determine maintenance intervals of different train sets. Based on the results, recommendations are made in relation to future planning of the maintenance strategy. / AFRIKAANSE OPSOMMING: Die bestuur van fisiese bates het in die afgelope tyd meer belangrik geword, veral in organisasies wat afhanklik is van hulle fisiese bates. Hierdie navorsing stel ‘n metode voor wat die betroubaarheid van rollende materiaal bates in die spoor bedryf kwantifiseer deur gebruik te maak van falingstatistiek. In die huidige instandhouding bestuurstelsel van die gevallestudie word daar te veel gesteun op kansellasies en vertragings van treine as ‘n betroubaarheids meting. Daarom was die doelwitte van die navorsing om ‘n wetenskaplike benadering te ontwikkel om betroubaarheid van rollende materiaal the kwantifiseer, en om ‘n instandhouding beplannings model voor te stel, gebaseer op sisteem betroubaarheid. Die navorsingsmetodologie is gebaseer op falingstatistieke, falingverspreidings, en die interafhanklikheid van stelsels word gebruik om die invloed van komponent falings op die betroubaarheid van die totale stelsel te bepaal. Hierdie benadering word dan gebruik om die betroubaarheid van individuele treinstelle en die werksverrigting van individuele trainstelle te bepaal. Hierdie instandhouding meting kan gebruik word om falings van komponente en treinstelle te voorspel, asook om die bydrae van instandhouding tot betroubaarheid beter te verstaan, daarom genoem Betroubaarheids Gebaseerde Instandhouding. Dit is ook geïllustreer hoe die betroubaarheid meting gebruik kan word om instandhouding intervalle te bepaal. Die betroubaarheid model is met werklike data gevalideer en aanbevelings word gemaak hoe om betroubaarheid te gebruik om die toekomstige beplanning van instandhouding te doen.

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Meta-analytic reliability generalizations (RGs) are limited by the scarcity of reliability reporting in primary articles, and currently, RG investigators lack a method to quantify the impact of such nonreporting. This article introduces a stepwise procedure to address this challenge. First, the authors introduce a formula that allows researchers to estimate the lower bound population average reliability for a desired instrument. Second, they present an equation to determine the Fail-Safe N for RG. This equation estimates the number of ''file drawer'' studies required to drop the aggregate score reliability of an instrument below a specified criterion value. Finally, the authors demonstrate the utility of these equations using published RG studies. Comments on the conclusions drawn from each RG application are provided.

In intelligent transportation systems, the cooperation between vehicles and the road side units is essential to bring these systems to fruition. Vehicular ad hoc networks (VANETs) are a promising technology to enable the communications among vehicles on one hand and between vehicles and road side units on the other hand. However, it is a challenging task to develop a reliable routing algorithm for VANETs due to the high mobility and the frequent changes of the network topology. Communication links are highly vulnerable to disconnection in VANETs; hence, the routing reliability of these ever-changing networks needs to be paid special attention. In this paper, we propose a new vehicular reliability model to facilitate the reliable routing in VANETs. The link reliability is defined as the probability that a direct communication link between two vehicles will stay continuously available over a specified time period. Furthermore, the link reliability value is accurately calculated using the location, direction and velocity information of vehicles along the road. We extend the well-known ad hoc on-demand distance vector (AODV) routing protocol to propose our reliable routing protocol AODV-R. Simulation results demonstrate that AODV-R outperforms significantly the AODV routing protocol in terms of better delivery ratio and less link failures while maintaining a reasonable routing control overhead.

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It is a common practice when designing a system to apply safety factors to the critical failure load or event. These safety factors provide a buffer against failure due to the random or un-modeled behavior, which may lead the system to exceed these limits. However these safety factors are not directly related to the likelihood of a failure event occurring. If the safety factors are poorly chosen, the system may fail unexpectedly or it may have a design which is too conservative. Reliability-Based Design Optimization (RBDO) is an alternative approach which directly considers the likelihood of failure by incorporating a reliability analysis step such as the First-Order Reliability Method (FORM). The FORM analysis requires the solution of an optimization problem however, so implementing this approach into an RBDO routine creates a double-loop optimization structure. For large problems such as Reliability-Based Topology Optimization (RBTO), numeric sensitivity analysis becomes computationally intractable. In this thesis, a general approach to the sensitivity analysis of nested functions is developed from the Lagrange Multiplier Theorem and then applied to several Reliability-Based Design Optimization problems, including topology optimization. The proposed approach is computationally efficient, requiring only a single solution of the FORM problem each iteration. / Master of Science / It is a common practice when designing a system to apply safety factors to the critical failure load or event. These safety factors provide a buffer against failure due to the random or unmodeled behavior, which may lead the system to exceed these limits. However these safety factors are not directly related to the likelihood of a failure event occurring. If the safety factors are poorly chosen, the system may fail unexpectedly or it may have a design which is too conservative. Reliability-Based Design Optimization (RBDO) is an alternative approach which directly considers the likelihood of failure by incorporating a reliability analysis step such as the First-Order Reliability Method (FORM). The FORM analysis requires the solution of an optimization problem however, so implementing this approach into an RBDO routine creates a double-loop optimization structure. For large problems such as Reliability-Based Topology Optimization (RBTO), numeric sensitivity analysis becomes computationally intractable. In this thesis, a general approach to the sensitivity analysis of nested functions is developed from the Lagrange Multiplier Theorem and then applied to several Reliability-Based Design Optimization problems, including topology optimization. The proposed approach is computationally efficient, requiring only a single solution of the FORM problem each iteration.