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Добірка наукової літератури з теми "Reliability (Engineering)"

Автор: Grafiati

Опубліковано: 4 червня 2021

Оновлено: 29 липня 2024

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Статті в журналах з теми "Reliability (Engineering)"

1

Suzuki, Yoshihisa. "Reliability Engineering." Journal of SHM 10, no. 4 (1994): 2–8. http://dx.doi.org/10.5104/jiep1993.10.4_2.

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2

Butler, Ronald W., and Richard E. Barlow. "Engineering Reliability." Journal of the American Statistical Association 95, no. 450 (June 2000): 682. http://dx.doi.org/10.2307/2669424.

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3

Larrucea, Xabier, Fabien Belmonte, Adam Welc, and Tao Xie. "Reliability Engineering." IEEE Software 34, no. 4 (2017): 26–29. http://dx.doi.org/10.1109/ms.2017.89.

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4

DAY, BESSE B. "RELIABILITY ENGINEERING." Journal of the American Society for Naval Engineers 73, no. 2 (March 18, 2009): 251–56. http://dx.doi.org/10.1111/j.1559-3584.1961.tb03296.x.

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5

O’Connor, P. D. T., and Ranga Komanduri. "Reliability Engineering." Journal of Engineering Materials and Technology 110, no. 4 (October 1, 1988): 401–2. http://dx.doi.org/10.1115/1.3226070.

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6

Khamis, Imad H. "Reliability Engineering." Technometrics 37, no. 2 (May 1995): 234–35. http://dx.doi.org/10.1080/00401706.1995.10484317.

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7

Lynch, James. "Reliability Engineering." Technometrics 39, no. 2 (May 1997): 226–27. http://dx.doi.org/10.1080/00401706.1997.10485088.

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8

Mccool, John I. "Engineering Reliability." Technometrics 41, no. 1 (February 1999): 75–76. http://dx.doi.org/10.1080/00401706.1999.10485602.

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9

Pelz, Wolfgang. "Reliability Engineering." Journal of Quality Technology 29, no. 1 (January 1997): 118–19. http://dx.doi.org/10.1080/00224065.1997.11979736.

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10

Jensen, Willis A., and Laura J. Freeman. "Reliability Engineering." Journal of Quality Technology 47, no. 4 (October 2015): 416–17. http://dx.doi.org/10.1080/00224065.2015.11918143.

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Дисертації з теми "Reliability (Engineering)"

1

Sasse, Guido Theodor. "Reliability engineering in RF CMOS." Enschede : University of Twente [Host], 2008. http://doc.utwente.nl/59032.

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2

Heineman, Judie A. "A software reliability engineering case study." Thesis, Monterey, California. Naval Postgraduate School, 1996. http://hdl.handle.net/10945/8975.

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Approved for public release; distribution is unlimited
Handling, identifying, and correcting faults are significant concerns for the software maanger because (1) the presence of faults in the operational software can put human life and mission success at risk in a safety critical application and (2) the entire software reliability process is expensive. Designing an effective Software Reliability Engineering (SRE) process is one method to increase reliability and reduce costs. This thesis describes a process that is being implemented at Marine Corps Tactical System Support Activity (MCTSSA), using the Schneidewind Reliability Model and the SRE process described in the American Institute of Aeronautics and Astronautics Recommended Practice in Software Reliability. In addition to applying the SRE process to single node systems, its applicability to multi-node LAN-based distributed systems is explored. Each of the SRE steps is discussed, with practical examples provided, as they would apply to a testing facility. Special attention is directed to data collection methodologies and the application of model results. in addition, a handbook and training plan are provided for use by MCTSSA during the transition to the SRE process

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3

Bolgren, Daniel (Daniel Reade). "High reliability performance in Amgen Engineering." Thesis, Massachusetts Institute of Technology, 2012. http://hdl.handle.net/1721.1/73439.

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Thesis (M.B.A.)--Massachusetts Institute of Technology, Sloan School of Management; and, (S.M.)--Massachusetts Institute of Technology, Dept. of Chemical Engineering; in conjunction with the Leaders for Global Operations Program at MIT, 2012.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 90).
Amgen is in the midst of a transformative initiative to become operationally more efficient. For Amgen Engineering, this initiative has prompted a reevaluation of the entire organization and brought to light the need to standardize, define processes, and promote a culture wherein reliable outcomes are both possible and expected. One way to accomplish this is by evaluating and then implementing the concepts of High Reliability Organization (HRO). This thesis focuses on using concepts such as HRO to evaluate the Engineering organization at Amgen and then provide tools, frameworks, and recommendations for driving increased reliability and greater process maturity across Amgen's entire asset lifecycle (Plan, Build/Lease, Operate/Maintain, Reinvest/Dispose). Three main deliverables resulted from this project's reliability efforts. The first deliverable is a set of recommendations and strategies to help the Engineering organization operate as an HRO. The second deliverable is an enhanced process maturity model that implements reliability concepts to drive the maturity of Engineering's business processes. The model better defines criteria for each level of maturity and will be used as a guidance tool for organizational advancement in the coming years. The last deliverable focuses on the maintain portion of the asset lifecycle, and is a Maintenance Excellence Roadmap that defines what maintenance excellence looks like and provides a strategy to best utilize the systems and tools that Amgen has in place, and will need in the future, to get there.
by Daniel Bolgren.
S.M.
M.B.A.

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4

Lanning, David Bruce. "Fatigue reliability of cracked engineering structures /." The Ohio State University, 1997. http://rave.ohiolink.edu/etdc/view?acc_num=osu148794501561685.

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5

Saini, Gagandeep Singh. "Reliability-based design with system reliability and design improvement." Diss., Rolla, Mo. : Missouri University of Science and Technology, 2009. http://scholarsmine.mst.edu/thesis/pdf/Saini_09007dcc8070d586.pdf.

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Thesis (M.S.)--Missouri University of Science and Technology, 2009.
Vita. The entire thesis text is included in file. Title from title screen of thesis/dissertation PDF file (viewed November 23, 2009) Includes bibliographical references (p. 66-68).

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6

ROBINSON, DAVID GERALD. "MODELING RELIABILITY IMPROVEMENT DURING DESIGN (RELIABILITY GROWTH, BAYES, NON PARAMETRIC)." Diss., The University of Arizona, 1986. http://hdl.handle.net/10150/183971.

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Past research into the phenomenon of reliability growth has emphasised modeling a major reliability characteristic in terms of a specific parametric function. In addition, the time-to-failure distribution of the system was generally assumed to be exponential. The result was that in most cases the improvement was modeled as a nonhomogeneous Poisson process with intensity λ(t). Major differences among models centered on the particular functional form of the intensity function. The popular Duane model, for example, assumes that λ(t) = β(1 – α)t ⁻ᵅ. The inability of any one family of distributions or parametric form to describe the growth process resulted in a multitude of models, each directed toward answering problems encountered with a particular test situation. This thesis proposes two new growth models, neither requiring the assumption of a specific function to describe the intensity λ(t). Further, the first of the models only requires that the time-to-failure distribution be unimodal and that the reliability become no worse as development progresses. The second model, while requiring the assumption of an exponential failure distribution, remains significantly more flexible than past models. Major points of this Bayesian model include: (1) the ability to encorporate data from a number of test sources (e.g. engineering judgement, CERT testing, etc.), (2) the assumption that the failure intensity is stochastically decreasing, and (3) accountability of changes that are incorporated into the design after testing is completed. These models were compared to a number of existing growth models and found to be consistently superior in terms of relative error and mean-square error. An extension to the second model is also proposed that allows system level growth analysis to be accomplished based on subsystem development data. This is particularly significant, in that, as systems become larger and more complex, development efforts concentrate on subsystem levels of design. No analysis technique currently exists that has this capability. The methodology is applied to data sets from two actual test situations.

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7

Brunelle, Russell Dedric. "Customer-centered reliability measures for flexible multistate reliability models /." Thesis, Connect to this title online; UW restricted, 1998. http://hdl.handle.net/1773/10691.

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8

Wickstrom, Larry E. "Reliability of Electronics." Thesis, University of North Texas, 2014. https://digital.library.unt.edu/ark:/67531/metadc700024/.

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Анотація:

The purpose of this research is not to research new technology but how to improve existing technology and understand how the manufacturing process works. Reliability Engineering fall under the category of Quality Control and uses predictions through statistical measurements and life testing to figure out if a specific manufacturing technique will meet customer satisfaction. The research also answers choice of materials and choice of manufacturing process to provide a device that will not only meet but exceed customer demand. Reliability Engineering is one of the final testing phases of any new product development or redesign.

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9

Hwang, Sungkun. "Predicting reliability in multidisciplinary engineering systems under uncertainty." Thesis, Georgia Institute of Technology, 2016. http://hdl.handle.net/1853/54955.

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The proposed study develops a framework that can accurately capture and model input and output variables for multidisciplinary systems to mitigate the computational cost when uncertainties are involved. The dimension of the random input variables is reduced depending on the degree of correlation calculated by relative entropy. Feature extraction methods; namely Principal Component Analysis (PCA), the Auto-Encoder (AE) algorithm are developed when the input variables are highly correlated. The Independent Features Test (IndFeaT) is implemented as the feature selection method if the correlation is low to select a critical subset of model features. Moreover, Artificial Neural Network (ANN) including Probabilistic Neural Network (PNN) is integrated into the framework to correctly capture the complex response behavior of the multidisciplinary system with low computational cost. The efficacy of the proposed method is demonstrated with electro-mechanical engineering examples including a solder joint and stretchable patch antenna examples.

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10

Abujaafar, Khalifa Mohamed. "Quantitative human reliability assessment in marine engineering operations." Thesis, Liverpool John Moores University, 2012. http://researchonline.ljmu.ac.uk/6115/.

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Marine engineering operations rely substantially on high degrees of automation and supervisory control. This brings new opportunities as well as the threat of erroneous human actions, which account for 80-90% of marine incidents and accidents. In this respect, shipping environments are extremely vulnerable. As a result, decision makers and stakeholders have zero tolerance for accidents and environmental damage, and require high transparency on safety issues. The aim of this research is to develop a novel quantitative Human Reliability Assessment (HRA) methodology using the Cognitive Reliability and Error Analysis Method (CREAM) in the maritime industry. This work will facilitate risk assessment of human action and its applications in marine engineering operations. The CREAM model demonstrates the dynamic impact of a context on human performance reliability through Contextual Control Model controlling modes (COCOM-CMs). CREAM human action analysis can be carried out through the core functionality of a method, a classification scheme and a cognitive model. However, CREAM has exposed certain practical limitations in its applications especially in the maritime industry, including the large interval presentation of Human Failure Probability (HFP) values and the lack of organisational factors in its classification scheme. All of these limitations stimulate the development of advanced techniques in CREAM as well as illustrate the significant gap between industrial needs and academic research. To address the above need, four phases of research study are proposed. In the first phase, the adequacy of organisation, one of the key Common Performance Conditions (CPCs) in CREAM, is expanded by identifying the associated Performance Influencing Factors (PIFs) and sub-PIFs in a Bayesian Network (BN) for realising the rational quantification of its assessment. In the second phase, the uncertainty treatment methods' BN, Fuzzy Rule Base (FRB) , Fuzzy Set (FS) theory are used to develop new models and techniques' that enable users to quantify HFP and facilitate the identification of possible initiating events or root causes of erroneous human action in marine engineering operations. In the third phase, the uncertainty treatment method's Evidential Reasoning (ER) is used in correlation with the second phase's developed new models and techniques to produce the solutions to conducting quantitative HRA in conditions in which data is unavailable, incomplete or ill-defined. In the fourth phase, the CREAM's prospective assessment and retrospective analysis models are integrated by using the established Multiple Criteria Decision Making (MCDM) method based on, the combination of Analytical Hierarchical Process (AHP), entropy analysis and Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS). These enable Decision Makers (DMs) to select the best developed Risk Control Option (RCO) in reducing HFP values. The developed methodology addresses human actions in marine engineering operations with the significant potential of reducing HFP, promoting safety culture and facilitating the current Safety Management System (SMS) and maritime regulative frameworks. Consequently, the resilience of marine engineering operations can be further strengthened and appreciated by industrial stakeholders through addressing the requirements of more safety management attention at all levels. Finally, several real case studies are investigated to show end users tangible benefits of the developed models, such as the reduction of the HFPs and optimisation of risk control resources, while validating the algorithms, models, and methods developed in this thesis.

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Книги з теми "Reliability (Engineering)"

1

Bradley, Edgar. Reliability Engineering. Boca Raton : Taylor & Francis, a CRC title, part of the Taylor &: CRC Press, 2016. http://dx.doi.org/10.1201/9781315367422.

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2

Kapur, Kailash C., and Michael Pecht, eds. Reliability Engineering. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9781118841716.

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3

Aggarwal, K. K. Reliability Engineering. Dordrecht: Springer Netherlands, 1993. http://dx.doi.org/10.1007/978-94-011-1928-3.

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4

Birolini, Alessandro. Reliability Engineering. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-662-05409-3.

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Birolini, Alessandro. Reliability Engineering. Berlin, Heidelberg: Springer Berlin Heidelberg, 2017. http://dx.doi.org/10.1007/978-3-662-54209-5.

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Birolini, Alessandro. Reliability Engineering. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-39535-2.

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7

Lazzaroni, Massimo, Loredana Cristaldi, Lorenzo Peretto, Paola Rinaldi, and Marcantonio Catelani. Reliability Engineering. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-20983-3.

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8

Birolini, Alessandro. Reliability Engineering. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-662-03792-8.

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9

Birolini, Alessandro. Reliability Engineering. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-14952-8.

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10

Elsayed, Elsayed A. Reliability engineering. 2nd ed. Hoboken, N.J: John Wiley & Sons, Inc., 2012.

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Частини книг з теми "Reliability (Engineering)"

1

Tichý, Milík. "Reliability Engineering." In Topics in Safety, Reliability and Quality, 265–86. Dordrecht: Springer Netherlands, 1993. http://dx.doi.org/10.1007/978-94-011-1948-1_15.

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2

Tinga, T. "Reliability Engineering." In Springer Series in Reliability Engineering, 225–54. London: Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-4917-0_7.

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3

Sotoodeh, Karan. "Reliability Engineering." In Safety Engineering in the Oil and Gas Industry, 415–38. Boca Raton: CRC Press, 2023. http://dx.doi.org/10.1201/9781003387275-16.

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4

Bukowski, Lech A. "Reliability Engineering." In Cognitive Dependability Engineering, 79–98. Boca Raton: CRC Press, 2023. http://dx.doi.org/10.1201/9781003020752-8.

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5

Rhinehart, R. Russell, and Robert M. Bethea. "Reliability." In Applied Engineering Statistics, 411–36. 2nd ed. Boca Raton: CRC Press, 2021. http://dx.doi.org/10.1201/9781003222330-25.

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6

Jackson, Lisa, and Frank P. A. Coolen. "Reliability." In Uncertainty in Engineering, 81–94. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-83640-5_6.

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Анотація:

AbstractThis chapter introduces key concepts for quantification of system reliability. In addition, basics of statistical inference for reliability data are explained, in particular, the derivation of the likelihood function.

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7

Bradley, Edgar. "Reliability Fundamentals I: Component Reliability." In Reliability Engineering, 1–28. Boca Raton : Taylor & Francis, a CRC title, part of the Taylor &: CRC Press, 2016. http://dx.doi.org/10.1201/9781315367422-1.

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8

Bradley, Edgar. "Reliability Fundamentals II: System Reliability." In Reliability Engineering, 29–61. Boca Raton : Taylor & Francis, a CRC title, part of the Taylor &: CRC Press, 2016. http://dx.doi.org/10.1201/9781315367422-2.

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9

Gamweger, Jürgen, Oliver Jöbstl, Manfred Strohrmann, and Wadym Suchowerskyj. "Reliability Engineering – Zuverlässigkeitsanalysen." In Design for Six Sigma, 537–62. München, Germany: Carl Hanser Verlag GmbH & Co. KG, 2009. http://dx.doi.org/10.1007/978-3-446-42062-5_19.

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10

Bradley, Edgar. "Reliability Management." In Reliability Engineering, 327–55. 2nd ed. Boca Raton: CRC Press, 2022. http://dx.doi.org/10.1201/9781003326489-8.

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Тези доповідей конференцій з теми "Reliability (Engineering)"

1

Caers, J. F. J. M., X. J. Zhao, J. Mooren, L. Stulens, and E. Eggink. "Design for reliability - a Reliability Engineering Framework." In High Density Packaging (ICEPT-HDP). IEEE, 2010. http://dx.doi.org/10.1109/icept.2010.5582735.

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2

Khalid, K. "Reliability in engineering systems." In International Multi Topic Conference, 2002. Abstracts. INMIC 2002. IEEE, 2002. http://dx.doi.org/10.1109/inmic.2002.1310147.

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3

Fenton, Gordon A., and D. V. Griffiths. "Reliability-Based Geotechnical Engineering." In GeoFlorida 2010. Reston, VA: American Society of Civil Engineers, 2010. http://dx.doi.org/10.1061/41095(365)2.

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4

Han, Ming. "Interval estimation of reliability parameters in reliability engineering." In 2015 International Conference on Intelligent Systems Research and Mechatronics Engineering. Paris, France: Atlantis Press, 2015. http://dx.doi.org/10.2991/isrme-15.2015.189.

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5

Dube, Paul T., and Debra Greenhalgh. "NAVSEA reliability and maintainability engineering." In 2016 Annual Reliability and Maintainability Symposium (RAMS). IEEE, 2016. http://dx.doi.org/10.1109/rams.2016.7448050.

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6

Grosspietsch, K. E. "Special session "Software reliability engineering"." In Proceedings of Euromicro Workshop on Multimedia and Telecommunications. IEEE, 2000. http://dx.doi.org/10.1109/eurmic.2000.874417.

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7

Wessels, William R. "Mechanical Engineering Design-for-Reliability." In ASME 2011 International Mechanical Engineering Congress and Exposition. ASMEDC, 2011. http://dx.doi.org/10.1115/imece2011-63523.

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This paper presents a design-for-reliability approach for mechanical design. Reliability analysis in part design, indeed the very definition of reliability, has been focused towards the electronic and digital disciplines since the emergence of reliability engineering in the late 1940’s. That focus dictates that parts fail in time; that all parts have a constant failure rate, and that part failure is modeled by the exponential mass density function. This paper presents current research that proposes that reliability in mechanical design is not characterized by ‘best practices’ reliability analyses. One premise investigated is that time does not cause failure in mechanical design; only failure mechanisms do. Mechanical parts experience wear-out and fatigue, unlike electronic and digital parts. Mechanical design analysis for part design investigates material strength properties required to survive failure mechanisms induced by part operation and by part exposure to external failure mechanisms. Such failure mechanisms include physical loads, thermal loads, and reactivity/corrosion. Each failure mechanism acting on a mechanical part induces one or more part failure modes, and each part failure mode has one or more failure effects on the part and the upper design configurations in which the part is integrated. The second premise investigated is that mechanical part failure is modeled by the Weibull mass density function in terms of stress, not time. A reliability math model for tensile strength in composite materials is presented to illustrate the two premises.

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8

"Interactive-Computer Aided Reliability Engineering." In 5th Symposium on Multidisciplinary Analysis and Optimization. Reston, Virigina: American Institute of Aeronautics and Astronautics, 1994. http://dx.doi.org/10.2514/6.1994-4312.

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9

"Human reliability and resilience engineering." In 2010 3rd International Symposium on Resilient Control Systems (ISRCS 2010). IEEE, 2010. http://dx.doi.org/10.1109/isrcs.2010.5602462.

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10

Wassouf, Philip, Samyak Jain, and Aart van Kranenburg. "Engineering Gravel Packs for Reliability." In SPE Annual Technical Conference and Exhibition. Society of Petroleum Engineers, 2016. http://dx.doi.org/10.2118/181397-ms.

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Звіти організацій з теми "Reliability (Engineering)"

1

Sadlon, Richard J. Mechanical Applications in Reliability Engineering. Fort Belvoir, VA: Defense Technical Information Center, August 1993. http://dx.doi.org/10.21236/ada363860.

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2

Wolff, Thomas F., and Weijun Wang. Engineering Reliability of Navigation Structures. Fort Belvoir, VA: Defense Technical Information Center, December 1992. http://dx.doi.org/10.21236/ada329341.

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3

Author, Not Given. Photovoltaic Reliability and Engineering (Revised) (Fact Sheet). Office of Scientific and Technical Information (OSTI), June 2011. http://dx.doi.org/10.2172/1009255.

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4

Finkelstein, Maxim S. On engineering reliability concepts and biological aging. Rostock: Max Planck Institute for Demographic Research, August 2006. http://dx.doi.org/10.4054/mpidr-wp-2006-021.

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5

Li, Zhongmin. Knowledge Engineering Report: An Expert System for Selecting Reliability Index. Fort Belvoir, VA: Defense Technical Information Center, April 1988. http://dx.doi.org/10.21236/ada232821.

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6

Shapiro, Harvey T., and Donald R. Loose. Prospects for Integrating Reliability and Maintainability into Undergraduate Engineering Curricula. Fort Belvoir, VA: Defense Technical Information Center, May 1990. http://dx.doi.org/10.21236/ada221379.

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7

Warren, Randy. TA55-PMDS: Process Maintenance & Decontamination Services Reliability Engineering Program. Office of Scientific and Technical Information (OSTI), January 2023. http://dx.doi.org/10.2172/1922010.

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8

Klie, Robert, Maria Chan, Moon Kim, Angus Rockett, and Marco Nardone. Improving reliability and reducing cost in CdTe photovoltaics via grain boundary engineering. Office of Scientific and Technical Information (OSTI), October 2019. http://dx.doi.org/10.2172/1574992.

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9

Amarkoon, Vasantha R., and Brian C. LaCourse. Reliability and Reproducibility Achieved via Grain Boundary Engineering of High Performance Electronic Ceramics. Fort Belvoir, VA: Defense Technical Information Center, January 1996. http://dx.doi.org/10.21236/ada308736.

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10

Palmer. NR199506 Introduction to Limit-State Reliability Based Pipeline Design. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), July 1995. http://dx.doi.org/10.55274/r0011200.

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Анотація:

�The practical objective of this study is to demonstrate to the pipeline industry that the limit-state and reliability based design methods have a sound and respectable basis which has generated genuinely valuable application. This study outlines the history and background to limit-state and reliability-based design, develops a design methodology and then demonstrates it by application to some real pipeline engineering problems. The study highlights the benefits and identifies any limitations in the methodology and discusses how they might be resolved.

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