<p>When developing products, reliability is an important factor that has to be considered. For safety critical systems it is important to know the probability that an item will perform a required function without failure under stated conditions for a stated period of time. The main goal of a reliability prediction analysis is to predict the rate at which the product of a system will fail. To perform this prediction there are a number of methodologies available.</p><p>This Master Thesis proposes a model-based approach for reliability prediction calculations based on the physics of failure and supported by analysis of test-data field returns and physical models provided by the FIDES methodology. FIDES based reliability models have been integrated into a model-based diagnosis environment for seamless integration with other safety assessment analysis.</p><p>The model-based diagnosis environment used in this thesis is model-based reasoner RODON developed by Uptime Solutions AB. Components that uses the FIDES methodology have been developed in RODON, where components can be combined to systems by drag and drop method. Usage profiles that are defined according to the FIDES methodology in RODON are not system specific, which makes them reusable in other systems. The developed library of components and usage profiles makes it easy to model complex systems and perform reliability predictions according to the FIDES methodology.</p>

As technologies continue advancing, semiconductor devices with dimensions in nanometers have entered all spheres of human life. This research deals with both the statistical aspect of reliability and some electrical aspect of reliability characterization. As an example of nano devices, TaO_x-based high k dielectric thin &#64257;lms are studied on the failure mode identi&#64257;cation, accelerated life testing, lifetime projection, and failure rate estimation. Experiment and analysis on dielectric relaxation and transient current show that the relaxation current of high k dielectrics is distinctive to the trapping/detrapping current of SiO₂; high k &#64257;lms have a lower leakage current but a higher relaxation current than SiO₂. Based on the connection between polarization-relaxation and &#64257;lm integrity demonstrated in ramped voltage stress tests, a new method of breakdown detection is proposed. It monitors relaxation during the test, and uses the disappearing of relaxation current as the signal of a breakdown event. This research develops a Bayesian approach which is suitable to reliability estimation and prediction of current and future generations of nano devices. It combines the Weibull lifetime distribution with the empirical acceleration relationship, and put the model parameters into a hierarchical Bayesian structure. The value of the Bayesian approach lies in that it can fully utilize available information in modeling uncertainty and provide cogent prediction with limited resources in a reasonable period of time. Markov chain Monte Carlo simulation is used for posterior inference of the reliability projection and for sensitivity analysis over a variety of vague priors. Time-to-breakdown data collected in the accelerated life tests also are modeled with a bathtub failure rate curve. The decreasing failure rate is estimated with a non-parametric Bayesian approach, and the constant failure rate is estimated with a regular parametric Bayesian approach. This method can provide a fast and reliable estimation of failure rate for burn-in optimization when only a small sample of data is available.

When developing products, reliability is an important factor that has to be considered. For safety critical systems it is important to know the probability that an item will perform a required function without failure under stated conditions for a stated period of time. The main goal of a reliability prediction analysis is to predict the rate at which the product of a system will fail. To perform this prediction there are a number of methodologies available. This Master Thesis proposes a model-based approach for reliability prediction calculations based on the physics of failure and supported by analysis of test-data field returns and physical models provided by the FIDES methodology. FIDES based reliability models have been integrated into a model-based diagnosis environment for seamless integration with other safety assessment analysis. The model-based diagnosis environment used in this thesis is model-based reasoner RODON developed by Uptime Solutions AB. Components that uses the FIDES methodology have been developed in RODON, where components can be combined to systems by drag and drop method. Usage profiles that are defined according to the FIDES methodology in RODON are not system specific, which makes them reusable in other systems. The developed library of components and usage profiles makes it easy to model complex systems and perform reliability predictions according to the FIDES methodology.

ITC/USA 2005 Conference Proceedings / The Forty-First Annual International Telemetering Conference and Technical Exhibition / October 24-27, 2005 / Riviera Hotel & Convention Center, Las Vegas, Nevada / This paper suggests a reconfigurable sensor network (data bus) which is well suited for telemetry mission of rockets and launch vehicles. Reconfiguration of sensors’ channel information can be achieved without additional software and hardware. The proposed network consists of a central unit and multiple remote units with RS-485 data bus. Since those units need only an FPGA and a programmable ROM to be implemented, the network can provide minimum costs and high reliability. And this paper also presents reliability prediction result of a unit based on MIL-HDBK-217F and Monte Carlo simulation.

É cada vez maior a demanda pela eficiência dos sistemas ferroviários. A confiabilidade do material rodante é um dos fatores de desempenho mais importantes para a eficiência global dos trens de passageiros. Para que índices satisfatórios de confiabilidade, disponibilidade, mantenabilidade e segurança (CDMS) sejam alcançados, deve-se considerar esses fatores desde a elaboração da especificação técnica do material rodante até a operação da frota, incluindo projeto, fabricação, testes, comissionamento e garantia. No presente trabalho foram pesquisados os principais requisitos, normas e boas práticas empregadas na indústria ferroviária mundial para o gerenciamento da confiabilidade em projetos de material rodante, levando-se em conta os aspectos práticos e científicos. Para isso, foram inicialmente apresentadas algumas especificidades do setor ferroviário assim como alguns dos principais tipos de veículos ferroviários e seus sistemas, para que se tenha uma visão geral do contexto em que o trabalho está inserido. Em relação ao gerenciamento de CDMS propriamente dito, são mencionadas as principais tarefas a serem realizadas, de acordo com as referências consultadas. Foi dada uma atenção especial às especificações técnicas de CDMS, levando-se em conta a classificação das falhas e a determinação de parâmetros contratuais. Também foram abordados os aspectos teóricos e práticos das principais ferramentas e métodos de análise de CDMS empregados, tais como alocação de metas, predição de confiabilidade, diagrama de blocos, FMECA, FTA, monitoramento da confiabilidade em campo, etc. / Demand for efficiency in railway systems is increasing through the years. Rolling stock reliability is one of the most important performance factors for the global efficiency of passenger trains. To attain satisfactory reliability, availability, maintainability and safety (RAMS) figures, these factors must be considered from the elaboration of rolling stock technical specifications up to the operation of the fleet, including design, manufacturing, tests, commissioning and warranty. In the present work a research on main requirements, standards and good practices applied in worldwide railway industry for managing reliability in rolling stock designs was carried out, taking into account practical and scientific aspects. Some specificities of railway industry as well as some of the main railway vehicles types and related systems were presented, so that one can have an overview of the context where this work is inserted. Related to RAMS management, the main tasks to be done are presented, according to the references. Special attention was paid to RAMS technical specifications, considering failure classifications and determination of contractual parameters. The theoretical and practical aspects of the main tools and analysis methods of RAMS applied to the rolling stock design, such as apportionment of targets, reliability predictions, reliability block diagrams, FMECA, FTA, monitoring of field reliability, etc., were also covered.

Reliability is one of the most critical performance measures of guided missile systems. It is directly related to missile mission success. In order to have a high reliability value, reliability analysis should be carried out at all phases of the system design. Carrying out reliability analysis at all the phases of system design helps the designer to make reliability related design decisions in time and update the system design. In this study, reliability analysis process performed during the conceptual design phase of a Medium Range Anti-Tank Missile System Inertial Measurement Unit (IMU) was introduced. From the reliability requirement desired for the system, an expected IMU reliability value was derived by using reliability allocation methods. Then, reliability prediction for the IMU was calculated by using Relex Software. After that, allocated and predicted reliability values of the IMU were compared. It was seen that the predicted reliability value of the IMU did not meet the required reliability value. Therefore, reliability improvement analysis was carried out.

É cada vez maior a demanda pela eficiência dos sistemas ferroviários. A confiabilidade do material rodante é um dos fatores de desempenho mais importantes para a eficiência global dos trens de passageiros. Para que índices satisfatórios de confiabilidade, disponibilidade, mantenabilidade e segurança (CDMS) sejam alcançados, deve-se considerar esses fatores desde a elaboração da especificação técnica do material rodante até a operação da frota, incluindo projeto, fabricação, testes, comissionamento e garantia. No presente trabalho foram pesquisados os principais requisitos, normas e boas práticas empregadas na indústria ferroviária mundial para o gerenciamento da confiabilidade em projetos de material rodante, levando-se em conta os aspectos práticos e científicos. Para isso, foram inicialmente apresentadas algumas especificidades do setor ferroviário assim como alguns dos principais tipos de veículos ferroviários e seus sistemas, para que se tenha uma visão geral do contexto em que o trabalho está inserido. Em relação ao gerenciamento de CDMS propriamente dito, são mencionadas as principais tarefas a serem realizadas, de acordo com as referências consultadas. Foi dada uma atenção especial às especificações técnicas de CDMS, levando-se em conta a classificação das falhas e a determinação de parâmetros contratuais. Também foram abordados os aspectos teóricos e práticos das principais ferramentas e métodos de análise de CDMS empregados, tais como alocação de metas, predição de confiabilidade, diagrama de blocos, FMECA, FTA, monitoramento da confiabilidade em campo, etc. / Demand for efficiency in railway systems is increasing through the years. Rolling stock reliability is one of the most important performance factors for the global efficiency of passenger trains. To attain satisfactory reliability, availability, maintainability and safety (RAMS) figures, these factors must be considered from the elaboration of rolling stock technical specifications up to the operation of the fleet, including design, manufacturing, tests, commissioning and warranty. In the present work a research on main requirements, standards and good practices applied in worldwide railway industry for managing reliability in rolling stock designs was carried out, taking into account practical and scientific aspects. Some specificities of railway industry as well as some of the main railway vehicles types and related systems were presented, so that one can have an overview of the context where this work is inserted. Related to RAMS management, the main tasks to be done are presented, according to the references. Special attention was paid to RAMS technical specifications, considering failure classifications and determination of contractual parameters. The theoretical and practical aspects of the main tools and analysis methods of RAMS applied to the rolling stock design, such as apportionment of targets, reliability predictions, reliability block diagrams, FMECA, FTA, monitoring of field reliability, etc., were also covered.

Submitted by Pedro Barros (pedro.silvabarros@ufpe.br) on 2018-06-26T22:26:10Z No. of bitstreams: 2 license_rdf: 811 bytes, checksum: e39d27027a6cc9cb039ad269a5db8e34 (MD5) DISSERTAÇÃO Caio Bezerra Souto Maior.pdf: 3924685 bytes, checksum: 6968386bf75059f45ee80306322d2a56 (MD5) / Made available in DSpace on 2018-06-26T22:26:10Z (GMT). No. of bitstreams: 2 license_rdf: 811 bytes, checksum: e39d27027a6cc9cb039ad269a5db8e34 (MD5) DISSERTAÇÃO Caio Bezerra Souto Maior.pdf: 3924685 bytes, checksum: 6968386bf75059f45ee80306322d2a56 (MD5) Previous issue date: 2017-02-21 / CAPES / The useful life time of equipment is an important variable related to reliability and maintenance. The knowledge about the useful remaining life of operation system by means of a prognostic and health monitoring could lead to competitive advantage to the corporations. There are numbers of models trying to predict the reliability’s variable behavior, such as the remaining useful life, from different types of signal (e.g. vibration signal), however several could not be realistic due to the imposed simplifications. An alternative to those models are the learning methods, used when exist many observations about the variable. A well-known method is Support Vector Machine (SVM), with the advantage that is not necessary previous knowledge about neither the function’s behavior nor the relation between input and output. In order to achieve the best SVM’s parameters, a Particle Swarm Optimization (PSO) algorithm is coupled to enhance the solution. Empirical Mode Decomposition (EMD) and Wavelets rise as two preprocessing methods seeking to improve the input data analysis. In this paper, EMD and wavelets are used coupled with PSO+SVM to predict the rolling bearing Remaining Useful Life (RUL) from a vibration signal and compare with the prediction without any preprocessing technique. As conclusion, EMD models presented accurate predictions and outperformed the other models tested. / O tempo de vida útil de um equipamento é uma importante variável relacionada à confiabilidade e à manutenção, e o conhecimento sobre o tempo útil remanescente de um sistema em operação, por meio de um monitoramento do prognóstico de saúde, pode gerar vantagens competitivas para as corporações. Existem diversos modelos utilizados na tentativa de prever o comportamento de variáveis de confiabilidade, tal como a vida útil remanescente, a partir de diferentes tipos de sinais (e.g. sinal de vibração), porém alguns podem não ser realistas, devido às simplificações impostas. Uma alternativa a esses modelos são os métodos de aprendizado, utilizados quando se dispõe de diversas observações da variável. Um conhecido método de aprendizado supervisionado é o Support Vector Machine (SVM), que gera um mapeamento de funções de entrada-saída a partir de um conjunto de treinamento. Para encontrar os melhores parâmetros do SVM, o algoritmo de Particle Swarm Optimization (PSO) é acoplado para melhorar a solução. Empirical Mode Decomposition (EMD) e Wavelets são usados como métodos pré-processamento que buscam melhorar a qualidade dos dados de entrada para PSO+SVM. Neste trabalho, EMD e Wavelets foram usadas juntamente com PSO+SVM para estimar o tempo de vida útil remanescente de rolamentos a partir de sinais de vibração. Os resultados obtidos com e sem as técnicas de pré-processamento foram comparados. Ao final, é mostrado que modelos baseados em EMD apresentaram boa acurácia e superaram o desempenho dos outros modelos testados.

This work deals with setting of dependability of static electricity meters. The first two chapters deal about electricity meters and dependability in general than there are introduced three possible ways for getting dependability parameters. The first methode is data collection from the field. The second methode is reliability prediction from component's reliability. The third methode is aging life tests. Conclusion contains evaluation of results and highligting of importance for practical usage.

This research has developed several models and methodologies with the aim of improving the accuracy and applicability of reliability predictions for complex repairable systems. A repairable system is usually defined as one that will be repaired to recover its functions after each failure. Physical assets such as machines, buildings, vehicles are often repairable. Optimal maintenance strategies require the prediction of the reliability of complex repairable systems accurately. Numerous models and methods have been developed for predicting system reliability. After an extensive literature review, several limitations in the existing research and needs for future research have been identified. These include the follows: the need for an effective method to predict the reliability of an asset with multiple preventive maintenance intervals during its entire life span; the need for considering interactions among failures of components in a system; and the need for an effective method for predicting reliability with sparse or zero failure data. In this research, the Split System Approach (SSA), an Analytical Model for Interactive Failures (AMIF), the Extended SSA (ESSA) and the Proportional Covariate Model (PCM), were developed by the candidate to meet the needs identified previously, in an effective manner. These new methodologies/models are expected to rectify the identified limitations of current models and significantly improve the accuracy of the reliability prediction of existing models for repairable systems. The characteristics of the reliability of a system will alter after regular preventive maintenance. This alternation makes prediction of the reliability of complex repairable systems difficult, especially when the prediction covers a number of imperfect preventive maintenance actions over multiple intervals during the asset's lifetime. The SSA uses a new concept to address this issue effectively and splits a system into repaired and unrepaired parts virtually. SSA has been used to analyse system reliability at the component level and to address different states of a repairable system after single or multiple preventive maintenance activities over multiple intervals. The results obtained from this investigation demonstrate that SSA has an excellent ability to support the making of optimal asset preventive maintenance decisions over its whole life. It is noted that SSA, like most existing models, is based on the assumption that failures are independent of each other. This assumption is often unrealistic in industrial circumstances and may lead to unacceptable prediction errors. To ensure the accuracy of reliability prediction, interactive failures were considered. The concept of interactive failure presented in this thesis is a new variant of the definition of failure. The candidate has made several original contributions such as introducing and defining related concepts and terminologies, developing a model to analyse interactive failures quantitatively and revealing that interactive failure can be either stable or unstable. The research results effectively assist in avoiding unstable interactive relationship in machinery during its design phase. This research on interactive failures pioneers a new area of reliability prediction and enables the estimation of failure probabilities more precisely. ESSA was developed through an integration of SSA and AMIF. ESSA is the first effective method to address the reliability prediction of systems with interactive failures and with multiple preventive maintenance actions over multiple intervals. It enhances the capability of SSA and AMIF. PCM was developed to further enhance the capability of the above methodologies/models. It addresses the issue of reliability prediction using both failure data and condition data. The philosophy and procedure of PCM are different from existing models such as the Proportional Hazard Model (PHM). PCM has been used successfully to investigate the hazard of gearboxes and truck engines. The candidate demonstrated that PCM had several unique features: 1) it automatically tracks the changing characteristics of the hazard of a system using symptom indicators; 2) it estimates the hazard of a system using symptom indicators without historical failure data; 3) it reduces the influence of fluctuations in condition monitoring data on hazard estimation. These newly developed methodologies/models have been verified using simulations, industrial case studies and laboratory experiments. The research outcomes of this research are expected to enrich the body of knowledge in reliability prediction through effectively addressing some limitations of existing models and exploring the area of interactive failures.