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Добірка наукової літератури з теми "Model of reliability assessment"

Автор: Grafiati
Опубліковано: 30 травня 2022
Оновлено: 31 травня 2022

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Статті в журналах з теми "Model of reliability assessment"

1

Rebba, Ramesh, and Sankaran Mahadevan. "Computational methods for model reliability assessment." Reliability Engineering & System Safety 93, no. 8 (August 2008): 1197–207. http://dx.doi.org/10.1016/j.ress.2007.08.001.

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2

Alsharqawi, Mohammed, Tarek Zayed, Laya Parvizsedghy, Ahmed Senouci, and Hassan Al-Derham. "Reliability Assessment Model for Water Distribution Networks." Journal of Pipeline Systems Engineering and Practice 11, no. 2 (May 2020): 04019059. http://dx.doi.org/10.1061/(asce)ps.1949-1204.0000442.

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3

Perkin, Samuel, Arne Brufladt Svendsen, Trond Tollefsen, Ingrid Honve, Iris Baldursdottir, Hlynur Stefansson, Ragnar Kristjansson, and Pall Jensson. "Modelling weather dependence in online reliability assessment of power systems." Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 231, no. 4 (March 13, 2017): 364–72. http://dx.doi.org/10.1177/1748006x17694951.

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Probabilistic reliability assessment of power systems is an ongoing field of research, particularly in the development of tools to model the probability of exogenous threats and their potential consequences. This paper describes the application of a weather-dependent failure rate model to a region of the Icelandic transmission system, using 10 years of weather data and overhead line fault records. The studied failure rate model is compared with a constant failure rate model, in terms of variability and how well the models perform in a blind test over a 2 year period in reflecting the occurrence of outages. The weather-dependent and constant failure rate models are used as input to a state-of-the-art risk assessment tool to determine the sensitivity of such software to weather-dependent threats. The results show the importance of weather-dependent contingency probabilities in risk estimation, and in quantitative assessment of maintenance activities. The results also demonstrate that inclusion of weather dependence in power system reliability assessments affects the overall distribution of risk as a positively skewed distribution, with high-risk periods occurring at low frequency.
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4

Zhang, Bin, Dong Dong Yu, and Peng Su. "In Service Beam Bridge Reliability Assessment." Advanced Materials Research 838-841 (November 2013): 1034–41. http://dx.doi.org/10.4028/www.scientific.net/amr.838-841.1034.

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In order to evaluate the reliability of existing bridges ,based on maximum value of the load and model of probability calculation of resistance attenuation, according to the data of the bridge which actually collected, and by using the ADINA finite element software to build the finite element model of considering the damage detection of beam bridge. The component and system failure criteria are determined on the basis of the bridge load-bearing characteristics.The Time-dependent reliability of the system by using the probability calculation model for assessing the maximum value of the load and resistance attenuation can be obtained. The result shows Changes in the process of reliability prediction of bridge , the correctness of reliability calculation model be verified; the correctness of the ADINA model be validated; Also confirmed the feasibility of prediction model; to obtain the main influencing factors of reliability of existing bridges.
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DONG, Yun-Wei, Guang-Ren WANG, Fan ZHANG, and Lei GAO. "Reliability Analysis and Assessment Tool for AADL Model." Journal of Software 22, no. 6 (June 24, 2011): 1252–66. http://dx.doi.org/10.3724/sp.j.1001.2011.04014.

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Slot, René M. M., John D. Sørensen, Bruno Sudret, Lasse Svenningsen, and Morten L. Thøgersen. "Surrogate model uncertainty in wind turbine reliability assessment." Renewable Energy 151 (May 2020): 1150–62. http://dx.doi.org/10.1016/j.renene.2019.11.101.

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7

Castañeda, G. A. Pérez, J.-F. Aubry, and N. Brinzei. "Stochastic hybrid automata model for dynamic reliability assessment." Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 225, no. 1 (March 2011): 28–41. http://dx.doi.org/10.1177/1748006xjrr312.

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Huang, Jiale, Fei Xiao, and Yang Zhang. "Reliability Evaluation of Pavement Life-Cycle Assessment Model." Modelling and Simulation in Engineering 2018 (October 18, 2018): 1–8. http://dx.doi.org/10.1155/2018/4172519.

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Inventory reliability of the life-cycle assessment (LCA) model highly depends on the data quality and normally exhibits significant uncertainty. A rigorous statistical methodology was established to capture and quantify the inherent uncertainties linked to the results of the LCA model. Two sources of uncertainty, data quality and model, were identified. The former was captured by converting the deterministic value to probability density function using beta distribution according to the evaluation matrix of data quality; the latter was assessed by prescribing variation interval through defining uncertainty factor. The functional equivalent pavement structures were designed, and the corresponding energy consumption and CO2 emission were calculated by the LCA model. A 10% variation was observed for the LCA results and within 30-year analysis span, at the 95% confidence level, and environmental burdens of cement pavement are higher than those of asphalt pavements while the comparison between the two asphalt pavements is not significant statistically. Therefore, the established statistical methodology is capable of capturing the uncertainty of the LCA model and quantifying the reliability the LCA results.
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Gandhi, Parul, Mohammad Zubair Khan, Ravi Kumar Sharma, Omar H. Alhazmi, Surbhi Bhatia, and Chinmay Chakraborty. "Software Reliability Assessment Using Hybrid Neuro-Fuzzy Model." Computer Systems Science and Engineering 41, no. 3 (2022): 891–902. http://dx.doi.org/10.32604/csse.2022.019943.

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Hoduń, Piotr, and Michał Borecki. "Reliability Assessment of MV Power Connections." Energies 14, no. 21 (October 23, 2021): 6965. http://dx.doi.org/10.3390/en14216965.

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This article presents an analysis of the reliability of the power grid using data on the failure rate of the medium voltage (MV) power grid from the last five years. The analysis of the state of the power grid was based on the data provided by the grid operator. The purpose of this article is to analyze the reliability assessment of the medium voltage (MV) power connections using various analytical methods, a simulation model and reliability indicators. The analysis was performed based on the defined categories of power outages in terms of their duration. This made it possible to determine the energy quality indicators in a selected power grid. Then, a more complex analysis was carried out to assess the convergence of the applied analytical models of reliability assessment with the actual results obtained for the power grid. Moreover, using ANSYS Multiphysics, a numerical model of the cable head was developed to analyze the processes taking place in this element of the power grid for various exploitation cases.
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Дисертації з теми "Model of reliability assessment"

1

Williamson, Jamie D. "Reliability of the Brief Assessment Model." Miami University / OhioLINK, 2004. http://rave.ohiolink.edu/etdc/view?acc_num=miami1101994832.

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Moon, Min-Yeong. "Confidence-based model validation for reliability assessment and its integration with reliability-based design optimization." Diss., University of Iowa, 2017. https://ir.uiowa.edu/etd/5816.

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Conventional reliability analysis methods assume that a simulation model is able to represent the real physics accurately. However, this assumption may not always hold as the simulation model could be biased due to simplifications and idealizations. Simulation models are approximate mathematical representations of real-world systems and thus cannot exactly imitate the real-world systems. The accuracy of a simulation model is especially critical when it is used for the reliability calculation. Therefore, a simulation model should be validated using prototype testing results for reliability analysis. However, in practical engineering situation, experimental output data for the purpose of model validation is limited due to the significant cost of a large number of physical testing. Thus, the model validation needs to be carried out to account for the uncertainty induced by insufficient experimental output data as well as the inherent variability existing in the physical system and hence in the experimental test results. Therefore, in this study, a confidence-based model validation method that captures the variability and the uncertainty, and that corrects model bias at a user-specified target confidence level, has been developed. Reliability assessment using the confidence-based model validation can provide conservative estimation of the reliability of a system with confidence when only insufficient experimental output data are available. Without confidence-based model validation, the designed product obtained using the conventional reliability-based design optimization (RBDO) optimum could either not satisfy the target reliability or be overly conservative. Therefore, simulation model validation is necessary to obtain a reliable optimum product using the RBDO process. In this study, the developed confidence-based model validation is integrated in the RBDO process to provide truly confident RBDO optimum design. The developed confidence-based model validation will provide a conservative RBDO optimum design at the target confidence level. However, it is challenging to obtain steady convergence in the RBDO process with confidence-based model validation because the feasible domain changes as the design moves (i.e., a moving-target problem). To resolve this issue, a practical optimization procedure, which terminates the RBDO process once the target reliability is satisfied, is proposed. In addition, the efficiency is achieved by carrying out deterministic design optimization (DDO) and RBDO without model validation, followed by RBDO with the confidence-based model validation. Numerical examples are presented to demonstrate that the proposed RBDO approach obtains a conservative and practical optimum design that satisfies the target reliability of designed product given a limited number of experimental output data. Thus far, while the simulation model might be biased, it is assumed that we have correct distribution models for input variables and parameters. However, in real practical applications, only limited numbers of test data are available (parameter uncertainty) for modeling input distributions of material properties, manufacturing tolerances, operational loads, etc. Also, as before, only a limited number of output test data is used. Therefore, a reliability needs to be estimated by considering parameter uncertainty as well as biased simulation model. Computational methods and a process are developed to obtain confidence-based reliability assessment. The insufficient input and output test data induce uncertainties in input distribution models and output distributions, respectively. These uncertainties, which arise from lack of knowledge – the insufficient test data, are different from the inherent input distributions and corresponding output variabilities, which are natural randomness of the physical system.
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Hernandez, Hernandez Erika. "SEGMENT-BASED RELIABILITY ASSESSMENT FOR WATER DISTRIBUTION NETWORKS." UKnowledge, 2017. https://uknowledge.uky.edu/ce_etds/59.

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In recent years, water utilities have placed a greater emphasis on the reliability and resilience of their water distribution networks. This focus has increased due to the continuing aging of such infrastructure and the potential threat of natural or man-made disruptions. As a result, water utilities continue to look for ways to evaluate the resiliency of their systems with a goal of identifying critical elements that need to be reinforced or replaced. The simulation of pipe breaks in water reliability studies is traditionally modeled as the loss of a single pipe element. This assumes that each pipe has an isolation valve on both ends of the pipe that can be readily located and operated under emergency conditions. This is seldom the case. The proposed methodology takes into account that multiple pipes may be impacted during a single failure as a result of the necessity to close multiple isolation valves in order to isolate the “segment” of pipes necessary to contain the leak. This document presents a simple graphical metric for use in evaluating the performance of a system in response to a pipe failure. The metrics are applied to three different water distribution systems in an attempt to illustrate the fact that different pipe segments may impact system performance in different ways. This information is critical for use by system managers in deciding which segments to prioritize for upgrades or replacement.
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4

Zhao, Liang. "Reliability-based design optimization using surrogate model with assessment of confidence level." Diss., University of Iowa, 2011. https://ir.uiowa.edu/etd/1194.

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The objective of this study is to develop an accurate surrogate modeling method for construction of the surrogate model to represent the performance measures of the compute-intensive simulation model in reliability-based design optimization (RBDO). In addition, an assessment method for the confidence level of the surrogate model and a conservative surrogate model to account the uncertainty of the prediction on the untested design domain when the number of samples are limited, are developed and integrated into the RBDO process to ensure the confidence of satisfying the probabilistic constraints at the optimal design. The effort involves: (1) developing a new surrogate modeling method that can outperform the existing surrogate modeling methods in terms of accuracy for reliability analysis in RBDO; (2) developing a sampling method that efficiently and effectively inserts samples into the design domain for accurate surrogate modeling; (3) generating a surrogate model to approximate the probabilistic constraint and the sensitivity of the probabilistic constraint with respect to the design variables in most-probable-point-based RBDO; (4) using the sampling method with the surrogate model to approximate the performance function in sampling-based RBDO; (5) generating a conservative surrogate model to conservatively approximate the performance function in sampling-based RBDO and assure the obtained optimum satisfy the probabilistic constraints. In applying RBDO to a large-scale complex engineering application, the surrogate model is commonly used to represent the compute-intensive simulation model of the performance function. However, the accuracy of the surrogate model is still challenging for highly nonlinear and large dimension applications. In this work, a new method, the Dynamic Kriging (DKG) method is proposed to construct the surrogate model accurately. In this DKG method, a generalized pattern search algorithm is used to find the accurate optimum for the correlation parameter, and the optimal mean structure is set using the basis functions that are selected by a genetic algorithm from the candidate basis functions based on a new accuracy criterion. Plus, a sequential sampling strategy based on the confidence interval of the surrogate model from the DKG method, is proposed. By combining the sampling method with the DKG method, the efficiency and accuracy can be rapidly achieved. Using the accurate surrogate model, the most-probable-point (MPP)-based RBDO and the sampling-based RBDO can be carried out. In applying the surrogate models to MPP-based RBDO and sampling-based RBDO, several efficiency strategies, which include: (1) using local window for surrogate modeling; (2) adaptive window size for different design candidates; (3) reusing samples in the local window; (4) using violated constraints for surrogate model accuracy check; (3) adaptive initial point for correlation parameter estimation, are proposed. To assure the accuracy of the surrogate model when the number of samples is limited, and to assure the obtained optimum design can satisfy the probabilistic constraints, a conservative surrogate model, using the weighted Kriging variance, is developed, and implemented for sampling-based RBDO.
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5

Lyons, Melinda. "Evaluation of a task performance resource constraint model to assess the impact of offshore emergency management on risk reduction." Thesis, Cranfield University, 2000. http://dspace.lib.cranfield.ac.uk/handle/1826/4056.

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In this age of safety awareness, technological emergencies still happen, occasionally with catastrophic results. Often human intervention is the only way of averting disaster. Ensuring that the chosen emergency managers are competent requires a combination of training and assessment. However, assessment currently relies on expert judgement of behaviour as opposed to its impact on outcome, therefore it would be difficult to incorporate such data into formal Quantitative Risk Assessments (QRA). Although there is, as yet, no suitable alternative to expert judgement, there is a need for methods of quantifying the impact of emergency management on risk reduction in accident and incidents. The Task Performance Resource Constraint (TPRC) model is capable of representing the critical factors. It calculates probability of task success with respect to time based on uncertainties associated with the task and resource variables. The results can then be used to assess the management performance based on the physical outcome in the emergency, thereby providing a measure of the impact of emergency management on risk with a high degree of objectivity. Data obtained from training exercises for offshore and onshore emergency management were measured and successfully used with the TPRC model. The resulting probability of success functions also demonstrated a high level of external validity when used with improvements in emergency management or design changes or real data from the Piper Alpha disaster. It also appeared to have more external validity than other HRQ/QRA techniques as it uses physical data that are a greater influence on outcome than psychological changes - though this could be because the current HRA/QRA techniques view human unreliability as probability of error rather than probability of failure. The simulation data were also used to build up distributions of timings for simple emergency management tasks. Using additional theoretical data, this demonstrated the model's potential for assessing the probability of successf or novel situations and future designs.
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Zhuang, Xing. "A New Reliability Assessment Model for Power Electronic Modules Considering Failure Mechanism Interaction." Thesis, North Dakota State University, 2015. https://hdl.handle.net/10365/27615.

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A reliability prediction method is proposed to determine the lifetime of IGBTs (Insulated Gate Bipolar Transistors) under power cycling test based on the performance of solder joint and wire bond. The failure characteristics of solder joint and wire bond are captured via selected PoF model respectively. To provide precise reliability prediction, PoF models are converted into probabilistic models. In addition, the failure interaction between wire bond and solder joint is studied. Wire bond lift-off is treated as the predominant failure mode based upon experiments from literature and solder joint degradation process is triggered by wire bond degradation process. Increased junction temperature is captured as it is affected by the degradation process of both components. In the end, the system reliability is computed in a series system configuration.
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7

Gil, Sagás Esteban Manuel. "Integrated network flow model for a reliability assessment of the national electric energy system." [Ames, Iowa : Iowa State University], 2007.

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8

Bakouros, Y. L. "Offshore pipeline reliability prediction : An assessment of the breakdown characteristics of offshore pipelines and the development of a statistical technique to improve their reliability prediction with particular reference." Thesis, University of Bradford, 1988. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.233657.

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9

Buhari, Muhammad. "Reliability assessment of ageing distribution cable for replacement in 'smart' distribution systems." Thesis, University of Manchester, 2016. https://www.research.manchester.ac.uk/portal/en/theses/reliability-assessment-of-ageing-distribution-cable-for-replacement-in-smart-distribution-systems(e253c774-b5e3-4872-9139-894e7df553f0).html.

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Majority of electricity networks have growing number of ageing elements. Critical network components, such as ageing underground cables, are very expensive to install and disruptive to replace. On the other hand, global climate changes have made connection of new low carbon technologies (LCT) into the grids increasingly necessary. These factors are contributing to the increasing complexity of the planning and management of power systems. Numerous techniques published on this subject tend to ignore the impact of LCT integration and the anchoring ꞌSmartꞌ solutions on ageing network assets, such as underground cables and transformers. This thesis presents the development procedures of an ageing underground cable reliability model (IEC-Arrhenius-Weibull model) and cable ranking models for replacement based on system wide effects and thermal loss-of-life metrics. In addition, a new concept of LCT integration and distribution network management was proposed using two optimization models. The first optimizes connection of new wind sources by minimizing the connection cost and the cost of cable thermal loss-of-lives in the planning period. In the second stage, the network is optimally reconfigured in such a way to minimize thermal-loss-of-life of ageing cable. Both optimization models are formulated as mixed integer non-linear programming (MINLP) problems applicable to radially operated medium voltage networks. To quantify the reliability benefits of the proposed approach, Sequential Monte Carlo Simulation (SMCS) procedure was formulated. Some of the main features of the SMCS procedure are the IEC-Arrhenius-Weibull model for ageing cable, optimal network reconfiguration, wind generation modelling using ARMA models and real time thermal ratings. The final outputs are reliability metrics, cable ranking lists for replacement, savings due to 'non-spend' cable thermal lives, etc. These studies have proven to be important in formulating an effective strategy for extending the lives of network cables, managing overall network reliability and planning cables replacement in power distribution networks.
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Tiassou, Kossi Blewoussi. "Aircraft operational reliability : A model-based approach and case studies." Thesis, Toulouse, INSA, 2013. http://www.theses.fr/2013ISAT0012/document.

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Lors de la conception des avions, il est courant que les constructeurs évaluent la sûreté de fonctionnement en utilisant des modèles stochastiques, mais l'évaluation de la fiabilité opérationnelle à l’aide de modèles en ligne, pendant la réalisation des missions, reste rarement effectuée. Souvent, l'évaluation stochastique concerne la sécurité des avions. Cette thèse porte sur la modélisation de la fiabilité opérationnelle des avions, pour aider à la planification des activités de maintenance et des missions, ainsi qu’à la bonne réalisation de ces dernières. Nous avons développé une approche de modélisation, basée sur un méta-modèle qui sert de base i) de structuration des informations nécessaires à l’évaluation de la fiabilité opérationnelle d’un avion et ii) pour la construction de modèles stochastiques pouvant être mis à jour dynamiquement. La mise à jour concerne l'état courant des systèmes avion, un profil de mission et les moyens de maintenance disponibles dans les diverses escales incluses dans le profil de la mission. L'objectif est de permettre l'évaluation de la fiabilité opérationnelle en ligne. Deux cas d’études, basés sur des sous-systèmes avion, sont considérés à titre d'illustration. Nous présentons des exemples de résultats qui montrent le rôle important de l’évaluation de la fiabilité opérationnelle pendant une mission d’avion
Dependability assessment, by system manufacturer, during aircraft design, based on stochastic modeling, is of common practice, but model based operational dependability assessment online, during missions' achievement, is seldom done. Usually, the stochastic assessment addresses aircraft safety.This thesis addresses aircraft operational dependability modeling to support mission and maintenance planning, as well as the achievement of the missions. We develop a modeling approach, based on a meta-model that is used as a basis i) to structure the information needed to assess aircraft operational reliability and ii) to build a stochastic model that can be updated dynamically. The update concerns the current state of the aircraft system, a mission profile and the maintenance facilities available at the flight stop locations involved in the mission. The aim is to enable operational reliability assessment online. Two case studies, based on aircraft subsystems, are considered for illustration. We present examples of evaluation results that show the valuable role of operational dependability assessment during aircraft mission
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Книги з теми "Model of reliability assessment"

1

Nash, Franklin R. Estimating device reliability: Assessment of credibility. Boston: Kluwer Academic Publishers, 1993.

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2

Workshop on Model Uncertainty, Its Characterization and Quantification (1993 Annapolis, Maryland). Proceedings of Workshop on Model Uncertainty, Its Characterization and Quantification: Annapolis, Maryland, USA, October 20-22, 1993. College Park, MD, U.S.A: University Printing Services, University of Maryland, 1995.

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3

Chan, Hing Kai. Fuzzy Hierarchical Model for Risk Assessment: Principles, Concepts, and Practical Applications. London: Springer London, 2013.

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4

Yuriy, Greg M. Discrete-event simulation of mine equipment systems combined with a reliability assessment model /by Greg M. Yuriy. Sudbury, Ont: Laurentian University, School of Graduate Studies, 2005.

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5

Sheppard, Marsha I. Methodology for evaluating the credibility and reliability of models for environmental impact assessment. Pinawa, Man: AECL, Whiteshell Laboratories, 1996.

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6

European Safety and Reliability Conference (10th 1999 Munich-Garching, Germany). Safety and reliability: Proceedings of ESREL'99. Rotterdam: A.A. Balkema, 1999.

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7

Gardner, J. R. Independent Orbiter assessment: Assessment of the instrumental subsystem FMEA/CIL. Houston, Tex: McDonnell Douglas Astronautics Company, Houston Division, 1988.

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8

Stockholm, Sweden) Workshop on Human Reliability Models: Theoretical and Practical Challenges (1994. Proceedings of Workshop on Human Reliability Models Theoretical and Practical Challenges: Stockholm, Sweden, August 22-24, 1994. College Park, MD: Center for Reliability Engineering, University of Maryland, 1998.

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9

Li, Wenyuan. Risk Assessment Of Power Systems. New York: John Wiley & Sons, Ltd., 2005.

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10

Li, Wenyuan. Risk assessment of power systems: Models, methods, and applications. Piscataway, NJ: IEEE Press, 2005.

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Частини книг з теми "Model of reliability assessment"

1

Nowakowski, Tomasz. "Reliability Model of Combined Transportation System." In Probabilistic Safety Assessment and Management, 2012–17. London: Springer London, 2004. http://dx.doi.org/10.1007/978-0-85729-410-4_323.

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Solovjova, Irina, and Inna Romānova. "Bank Reliability Assessment Model: Case of Latvia." In Eurasian Business Perspectives, 285–98. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-35051-2_19.

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Kabir, Sohag, Martin Walker, and Yiannis Papadopoulos. "Reliability Analysis of Dynamic Systems by Translating Temporal Fault Trees into Bayesian Networks." In Model-Based Safety and Assessment, 96–109. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-12214-4_8.

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Aslansefat, Koorosh, Gholamreza Latif-Shabgahi, and Mehrdad Mohammadi. "Reliability Evaluation of Reconfigurable NMR Architecture Supported with Hot Standby Spare: Markov Modeling and Formulation." In Model-Based Safety and Assessment, 53–67. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58920-2_4.

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Hu, Yunwei, Wei Zhang, and Bin Li. "Benchmarking Software Reliability Growth Models." In Probabilistic Safety Assessment and Management, 908–13. London: Springer London, 2004. http://dx.doi.org/10.1007/978-0-85729-410-4_147.

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Zapata-Rivera, Luis Felipe, and Maria M. Larrondo-Petrie. "A Reliability Assessment Model for Online Laboratories Systems." In Smart Industry & Smart Education, 252–60. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-95678-7_28.

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Becker, Günter, Leonidas Camarinopoulos, and Dimitris Kabranis. "Adapting a Dynamic Reliability Model to Measurements using Bayesian Inference." In Probabilistic Safety Assessment and Management, 1694–701. London: Springer London, 2004. http://dx.doi.org/10.1007/978-0-85729-410-4_272.

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Balu, A. S., and B. N. Rao. "Multicut-High Dimensional Model Representation for Reliability Bounds Estimation." In Numerical Methods for Reliability and Safety Assessment, 499–510. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-07167-1_18.

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Faroz, Sharvil A., Devang B. Lad, and Siddhartha Ghosh. "Bayesian Model Calibration and Model Selection of Non-destructive Testing Instruments." In Reliability, Safety and Hazard Assessment for Risk-Based Technologies, 425–35. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-9008-1_34.

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Niemelä, I. M. "Plant and Control System Reliability and Risk Model." In Reliability Data Collection and Use in Risk and Availability Assessment, 564–72. Berlin, Heidelberg: Springer Berlin Heidelberg, 1986. http://dx.doi.org/10.1007/978-3-642-82773-0_57.

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Тези доповідей конференцій з теми "Model of reliability assessment"

1

Alonso, Pablo David, Angel Adrian Moreno, and Franco Luis Palmieri. "Petroleum Facilities Reliability Assessment Model." In Latin American & Caribbean Petroleum Engineering Conference. Society of Petroleum Engineers, 2007. http://dx.doi.org/10.2118/107012-ms.

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"Group assessment of Interview Ready Model reliability." In 21st International Congress on Modelling and Simulation (MODSIM2015). Modelling and Simulation Society of Australia and New Zealand, 2015. http://dx.doi.org/10.36334/modsim.2015.j5.coutts.

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Georgieva, Olga, and Aleksandar Dimov. "Software reliability assessment via fuzzy logic model." In the 12th International Conference. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/2023607.2023716.

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Pearson, D. J. "Risk assessment model for distribution system reliability." In 14th International Conference and Exhibition on Electricity Distribution (CIRED 1997 - Distributing Power for the Millennium). IEE, 1997. http://dx.doi.org/10.1049/cp:19970650.

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Dong, Feng, and Lei Fan. "Power system reliability assessment with full topology model." In 2017 IEEE Power & Energy Society General Meeting (PESGM). IEEE, 2017. http://dx.doi.org/10.1109/pesgm.2017.8274473.

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Vikram Kumar Kamboj, Amit Bhardwaj, Harkamaljeet Singh Bhullar, Krishan Arora, and Kulraj Kaur. "Mathematical model of reliability assessment for generation system." In 2012 IEEE International Power Engineering and Optimization Conference (PEOCO). IEEE, 2012. http://dx.doi.org/10.1109/peoco.2012.6231118.

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Chen, He-tao, and Hong-jie Yuan. "Reliability assessment based on proportional degradation hazards model." In EM2010). IEEE, 2010. http://dx.doi.org/10.1109/icieem.2010.5646465.

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Lai, Peng, Weiwei Hu, and Yufeng Sun. "Anisotropic coupling damage model for reliability life assessment." In 2015 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM). IEEE, 2015. http://dx.doi.org/10.1109/ieem.2015.7385762.

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Gafurov, Tokhir, Milan Prodanovic, and Julio Usaola. "PV system model reduction for reliability assessment studies." In 2013 4th IEEE/PES Innovative Smart Grid Technologies Europe (ISGT EUROPE). IEEE, 2013. http://dx.doi.org/10.1109/isgteurope.2013.6695420.

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Yoo, D. G., D. S. Kang, and J. H. Kim. "Seismic Reliability Assessment Model of Water Supply Networks." In World Environmental and Water Resources Congress 2013. Reston, VA: American Society of Civil Engineers, 2013. http://dx.doi.org/10.1061/9780784412947.092.

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Звіти організацій з теми "Model of reliability assessment"

1

Melby, Jeffrey A., and Paul F. Mlakar. Reliability Assessment of Breakwaters. Fort Belvoir, VA: Defense Technical Information Center, May 1997. http://dx.doi.org/10.21236/ada327044.

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Nagarajan, Adarsh, Ramanathan Thiagarajan, Ingrid L. Repins, and Peter L. Hacke. Photovoltaic Inverter Reliability Assessment. Office of Scientific and Technical Information (OSTI), October 2019. http://dx.doi.org/10.2172/1573462.

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Havey, G., S. Lewis, and G. Seifert. Avionics/Electronics Quick Reliability Assessment. Fort Belvoir, VA: Defense Technical Information Center, March 1998. http://dx.doi.org/10.21236/ada342681.

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Ramuhalli, P., P. G. Heasler, T. L. Moran, A. E. Holmes, and M. T. Anderson. Reliability Assessment of Remote Visual Examination. Office of Scientific and Technical Information (OSTI), August 2018. http://dx.doi.org/10.2172/1468477.

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Hess, Paul E., Ayyub III, Knight Bilal M., and David E. Failure Definition for Structural Reliability Assessment. Fort Belvoir, VA: Defense Technical Information Center, September 2000. http://dx.doi.org/10.21236/ada417415.

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Bivens, Gretchen A. Reliability Assessment Using Finite Element Techniques. Fort Belvoir, VA: Defense Technical Information Center, November 1989. http://dx.doi.org/10.21236/ada216907.

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Georgiev, Daniel, Vijaya Devabhaktuni, and Roger King. Reliability Assessment of GaN Power Switches. Fort Belvoir, VA: Defense Technical Information Center, April 2015. http://dx.doi.org/10.21236/ada618872.

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KANSAS UNIV CENTER FOR RESEARCH INC LAWRENCE. Data Reliability Assessment Review of win.Compare2 Software. Fort Belvoir, VA: Defense Technical Information Center, May 2001. http://dx.doi.org/10.21236/ada385509.

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Singpurwalla, N. Research in Improved Assessment of Software Reliability. Fort Belvoir, VA: Defense Technical Information Center, January 1994. http://dx.doi.org/10.21236/ada296803.

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Winterstein, Steven R., and C. A. Cornell. Reliability Assessment of Ship Structures Against Fatigue. Fort Belvoir, VA: Defense Technical Information Center, November 2001. http://dx.doi.org/10.21236/ada390563.

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