Test-analyze-and-fix (TAAF) is the most commonly recognized method of improving system reliability. The work presented here addresses the question of when to stop testing during TAAF programs involving one-shot systems when the number of systems to be produced is predetermined and the probabilities of identifying and successfully correcting each failure mode are less than one. The goal here is to determine when to cease testing to maximize utility where utility is defined as the number of systems expected to perform successfully in the field after deployment of the lot. Two TAAF stopping rules are presented. Simulation is used to model TAAF execution under different reliability growth conditions. Four discrete reliability growth models (DRGM's) are used to generate "real world" reliability growth and to estimate reliability growth using hypothetical observed success/failure data. Ranges for the following parameters are considered: starting reliability, growth rate, maximum achievable reliability, number of systems to be produced, probability of incorrectly identifying a failure mode, and probability of an unsuccessful design modification. Conclusions are drawn regarding stopping rule performance in terms of stopping rule signal location, utility loss, achieved reliability, and fraction tested. Both rules perform well and are implementable from a practical standpoint. Specific recommendations for stopping rule implementation are given based on the controllable factors, estimation methodology and lot size. / Master of Science

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The current method of determining component failure rates for probabilistic risk assessment (PRA) in the nuclear industry is to take the total number of failures divided by the time over which the failures occurred. The method proposed in this study is the reliability growth method and involves taking into account the fact that the amount of failures per additional year of operation generally decreases yearly because the operational staff becomes familiar with the equipment. The reliability growth method will result in lower component failure rates which when used in PRA studies could result in a lower core melt frequency value. The component failure rate would be expected to be higher in the early stages and should gradually decrease as time goes on. This study will compare the final core melt frequency of the Trojan Nuclear Power Plant using both methods. The Nuclear Power Reactor Data System (NPRDS) data base from the Institute of Nuclear Power Operations (INPO) was used in this study. The components which were examined for the reliability growth method are motor operated valves, service water pump/motors and emergency diesel generator air chargers. These data were screened to ensure that only true failures were reported. A comparison was made of the overall core melt frequency between the conventional failure rate method and reliability growth method for the motor operated valves. The overall core melt frequency was decreased by 1.8 % when using the reliability growth method compared to the conventional method. / Graduation date: 1993

In 2004, the NASA Astronaut Office produced a memo regarding the safety of next generation launch vehicles. The memo requested that these vehicles have a probability of loss of crew of at most 1 in 1000 flights, which represents nearly an order of magnitude decrease from current vehicles. The goal of LOC of 1 in 1000 flights has since been adopted by the launch vehicle design community as a requirement for the safety of future vehicles. This research addresses the gap between current vehicles and future goals by improving the capture of vehicle architecture effects on reliability and safety. Vehicle architecture pertains to the physical description of the vehicle itself, which includes manned or unmanned, number of stages, number of engines per stage, engine cycle types, redundancy, etc. During the operations phase of the vehicle life-cycle it is clear that each of these parameters will have an inherent effect on the reliability and safety of the vehicle. However, the vehicle architecture is typically determined during the early conceptual design phase when a baseline vehicle is selected. Unless a great amount of money and effort is spent, the architecture will remain relatively constant from conceptual design through operations. Due to the fact that the vehicle architecture is essentially “locked-in” during early design, it is expected that much of the vehicle's reliability potential will also be locked-in. This observation leads to the conclusion that improvement of vehicle reliability and safety in the area of vehicle architecture must be completed during early design. Evaluation of the effects of different architecture decisions must be performed prior to baseline selection, which helps to identify a vehicle that is most likely to meet the reliability and safety requirements when it reaches operations. Although methods exist for evaluating reliability and safety during early design, weaknesses exist when trying to evaluate all architecture effects simultaneously. The goal of this research was therefore to formulate and implement a method that is capable of quantitatively evaluating vehicle architecture effects on reliability and safety during early conceptual design. The ConcepTual Reliability Growth Approach for CompariSon of Launch Vehicle ArchiTectures (CONTRAST) was developed to meet this goal. Using the strengths of existing techniques a hybrid approach was developed, which utilizes a reliability growth projection to evaluate the vehicles. The growth models are first applied at the subsystem level and then a vehicle level projection is generated using a simple system level fault tree. This approach allows for the capture of all trades of interest at the subsystem level as well as many possible trades at the assembly level. The CONTRAST method is first tested on an example problem, which compares the method output to actual data from the Space Transportation System (STS). This example problem illustrates the ability of the CONTRAST method to capture reliability growth trends seen during vehicle operations. It also serves as a validation for the development of the reliability growth model assumptions for future applications of the method. The final chapter of the thesis applies the CONTRAST method to a relevant launch vehicle, the Space Launch System (SLS), which is currently under development. Within the application problem, the output of the method is first used to check that the primary research objective has been met. Next, the output is compared to a state-of-the-art tool in order to demonstrate the ability of the CONTRAST method to alleviate one of the primary consequences of using existing techniques. The final section within this chapter presents an analysis of the booster and upper stage block upgrade options for the SLS vehicle. A study of the upgrade options was carried out because the CONTRAST method is uniquely suited to look at the effects of such strategies. The results from the study of SLS block upgrades give interesting observations regarding the desired development order and upgrade strategy. Ultimately this application problem demonstrates the merits of applying the CONTRAST method during early design. This approach provides the designer with more information in regard to the expected reliability of the vehicle, which will ultimately enable the selection of a vehicle baseline that is most likely to meet the future requirements.

The criticality of the applications modeled by the real-time software places stringent requirements on software quality before deploying into real use. Though automated test tools can be used to run a large number of tests efficiently, the functionality of any test tool is not complege without providing a means for analyzing the test results to determine potential problem sub-domains and sub-domains that need to be covered, and estimating the reliability of the modeled system. This thesis outlines a solution strategy and implementation of that strategy for deriving quantitative metrics from domain testing of real-time control software tested via simulation. The key portion of this thesis addresses the combinatorial problems involved with effective evaluation of test coverage and provides the developer with reliability metrics from testing of the software to gain confidence in the test phase of development. The two approaches for reliability analysis- time domain and input domain approaches are studied and a hybrid approach that combines the strengths of both these approaches is proposed. A Reliability analysis Test Tool (RATT) has been developed to implement the proposed strategies. The results show that the metrics are practically feasible to compute and can be applied to most real-time software.

Esta dissertação apresenta a importância do gerenciamento da confiabilidade de um produto durante seu desenvolvimento. Tal gerenciamento consiste em um processo sistemático que resulta no alcance dos requisitos governamentais, do cliente e da empresa. Estudou-se a metodologia de gerenciamento da confiabilidade através do planejamento da confiabilidade meta, alocação ótima de confiabilidade, avaliação e modelagem da confiabilidade, planejamento do crescimento da confiabilidade, controle do crescimento da confiabilidade até a obtenção dos resultados planejados. Através dessa metodologia, em um estudo aplicado contemplando um produto médico-hospitalar, foi possível: (a) identificar os componentes críticos do produto, (b) identificar a lacuna existente entre o valor de confiabilidade no início do projeto quando comparado à meta, (c) planejar o crescimento da confiabilidade ao longo das fases do projeto, (d) realizar melhorias ou reprojetos nos componentes do produto, (e) controlar o crescimento da confiabilidade e (f) alcançar a confiabilidade planejada. Os resultados obtidos foram de um aumento de 24% para 100% na confiabilidade do produto para cinco anos de utilização e de 0% para 83% para dez anos de utilização. Os valores obtidos de confiabilidade superaram os valores planejados, confirmando a eficiência da metodologia adotada. / This study discusses the relevance of Product Reliability Management during the product development phase. It consists of a systematic process that results in achieving the requirements of government regulations, customer satisfaction and companies goals. We studied the Product Reliability Management methodology through five aspects, including: target planning, optimal allocation, evaluation and modeling of product reliability, growth planning, and growth control to achieve the planned results. Using this methodology, applied in a study comprising the analysis of a medical product, it was possible to: (a) identify the critical components of the product (b) identify the gap between the reliability rate at the beginning of the project when compared to the final target (c) plan the growth of reliability throughout the phases of the project (d) make improvements or redesign product components (e) control the growth of reliability and (f) achieve the planned product reliability. The obtained results show an increase of 24% to 100% on five years product reliability, and from 0% to 83% for ten years product reliability. The values of reliability exceeded the planned values, confirming the effectiveness of the Product Reliability Management methodology.

Esta dissertação apresenta a importância do gerenciamento da confiabilidade de um produto durante seu desenvolvimento. Tal gerenciamento consiste em um processo sistemático que resulta no alcance dos requisitos governamentais, do cliente e da empresa. Estudou-se a metodologia de gerenciamento da confiabilidade através do planejamento da confiabilidade meta, alocação ótima de confiabilidade, avaliação e modelagem da confiabilidade, planejamento do crescimento da confiabilidade, controle do crescimento da confiabilidade até a obtenção dos resultados planejados. Através dessa metodologia, em um estudo aplicado contemplando um produto médico-hospitalar, foi possível: (a) identificar os componentes críticos do produto, (b) identificar a lacuna existente entre o valor de confiabilidade no início do projeto quando comparado à meta, (c) planejar o crescimento da confiabilidade ao longo das fases do projeto, (d) realizar melhorias ou reprojetos nos componentes do produto, (e) controlar o crescimento da confiabilidade e (f) alcançar a confiabilidade planejada. Os resultados obtidos foram de um aumento de 24% para 100% na confiabilidade do produto para cinco anos de utilização e de 0% para 83% para dez anos de utilização. Os valores obtidos de confiabilidade superaram os valores planejados, confirmando a eficiência da metodologia adotada. / This study discusses the relevance of Product Reliability Management during the product development phase. It consists of a systematic process that results in achieving the requirements of government regulations, customer satisfaction and companies goals. We studied the Product Reliability Management methodology through five aspects, including: target planning, optimal allocation, evaluation and modeling of product reliability, growth planning, and growth control to achieve the planned results. Using this methodology, applied in a study comprising the analysis of a medical product, it was possible to: (a) identify the critical components of the product (b) identify the gap between the reliability rate at the beginning of the project when compared to the final target (c) plan the growth of reliability throughout the phases of the project (d) make improvements or redesign product components (e) control the growth of reliability and (f) achieve the planned product reliability. The obtained results show an increase of 24% to 100% on five years product reliability, and from 0% to 83% for ten years product reliability. The values of reliability exceeded the planned values, confirming the effectiveness of the Product Reliability Management methodology.

Esta dissertação apresenta a importância do gerenciamento da confiabilidade de um produto durante seu desenvolvimento. Tal gerenciamento consiste em um processo sistemático que resulta no alcance dos requisitos governamentais, do cliente e da empresa. Estudou-se a metodologia de gerenciamento da confiabilidade através do planejamento da confiabilidade meta, alocação ótima de confiabilidade, avaliação e modelagem da confiabilidade, planejamento do crescimento da confiabilidade, controle do crescimento da confiabilidade até a obtenção dos resultados planejados. Através dessa metodologia, em um estudo aplicado contemplando um produto médico-hospitalar, foi possível: (a) identificar os componentes críticos do produto, (b) identificar a lacuna existente entre o valor de confiabilidade no início do projeto quando comparado à meta, (c) planejar o crescimento da confiabilidade ao longo das fases do projeto, (d) realizar melhorias ou reprojetos nos componentes do produto, (e) controlar o crescimento da confiabilidade e (f) alcançar a confiabilidade planejada. Os resultados obtidos foram de um aumento de 24% para 100% na confiabilidade do produto para cinco anos de utilização e de 0% para 83% para dez anos de utilização. Os valores obtidos de confiabilidade superaram os valores planejados, confirmando a eficiência da metodologia adotada. / This study discusses the relevance of Product Reliability Management during the product development phase. It consists of a systematic process that results in achieving the requirements of government regulations, customer satisfaction and companies goals. We studied the Product Reliability Management methodology through five aspects, including: target planning, optimal allocation, evaluation and modeling of product reliability, growth planning, and growth control to achieve the planned results. Using this methodology, applied in a study comprising the analysis of a medical product, it was possible to: (a) identify the critical components of the product (b) identify the gap between the reliability rate at the beginning of the project when compared to the final target (c) plan the growth of reliability throughout the phases of the project (d) make improvements or redesign product components (e) control the growth of reliability and (f) achieve the planned product reliability. The obtained results show an increase of 24% to 100% on five years product reliability, and from 0% to 83% for ten years product reliability. The values of reliability exceeded the planned values, confirming the effectiveness of the Product Reliability Management methodology.

Software prediction is a complex area as there are no accurate models to represent reliability throughout the use of software, unlike hardware reliability. In the context of the software reliability of on-board train systems, ensuring good software reliability over time is all the more critical given the current density of rail traffic and the risk of accidents resulting from a software malfunction. This thesis proposes to use soft computing methods and historical failure data to predict the software reliability of on-board train systems. For this purpose, four machine learning models (Multi-Layer Perceptron, Imperialist Competitive Algorithm Multi-Layer Perceptron, Long Short-Term Memory Network and Convolutional Neural Network) are compared to determine which has the best prediction performance. We also study the impact of having one or more features represented in the dataset used to train the models. The performance of the different models is evaluated using the Mean Absolute Error, Mean Squared Error, Root Mean Squared Error and the R Squared. The report shows that the Long Short-Term Memory Network is the best performing model on the data used for this project. It also shows that datasets with a single feature achieve better prediction. However, the small amount of data available to conduct the experiments in this project may have impacted the results obtained, which makes further investigations necessary. / Att förutsäga programvara är ett komplext område eftersom det inte finns några exakta modeller för att representera tillförlitligheten under hela programvaruanvändningen, till skillnad från hårdvarutillförlitlighet. När det gäller programvarans tillförlitlighet i fordonsbaserade tågsystem är det ännu viktigare att säkerställa en god tillförlitlighet över tiden med tanke på den nuvarande tätheten i järnvägstrafiken och risken för olyckor till följd av ett programvarufel. I den här avhandlingen föreslås att man använder mjuka beräkningsmetoder och historiska data om fel för att förutsäga programvarans tillförlitlighet i fordonsbaserade tågsystem. För detta ändamål jämförs fyra modeller för maskininlärning (Multi-Layer Perceptron, Imperialist Competitive Algorithm Mult-iLayer Perceptron, Long Short-Term Memory Network och Convolutional Neural Network) för att fastställa vilken som har den bästa förutsägelseprestandan. Vi undersöker också effekten av att ha en eller flera funktioner representerade i den datamängd som används för att träna modellerna. De olika modellernas prestanda utvärderas med hjälp av medelabsolut fel, medelkvadratfel, rotmedelkvadratfel och R-kvadrat. Rapporten visar att Long Short-Term Memory Network är den modell som ger bäst resultat på de data som använts för detta projekt. Den visar också att dataset med en enda funktion ger bättre förutsägelser. Den lilla mängd data som fanns tillgänglig för att genomföra experimenten i detta projekt kan dock ha påverkat de erhållna resultaten, vilket gör att ytterligare undersökningar är nödvändiga.