Artículos de revistas: "Risk and Reliability Analysis"

1

McCormick, Norman J. "Reliability and Risk Analysis". IEEE Transactions on Reliability 35, n.º 3 (1986): 300–303. http://dx.doi.org/10.1109/tr.1986.4335437.

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2

Olwell, David. "Reliability Engineering and Risk Analysis". Technometrics 43, n.º 1 (febrero de 2001): 104–5. http://dx.doi.org/10.1198/tech.2001.s556.

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3

Ellyin, Fernand. "Systems reliability and risk analysis". Canadian Journal of Civil Engineering 12, n.º 3 (1 de septiembre de 1985): 724–25. http://dx.doi.org/10.1139/l85-083.

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4

Wen, Y. K. "System reliability and risk analysis". Structural Safety 4, n.º 2 (enero de 1986): 166. http://dx.doi.org/10.1016/0167-4730(86)90031-7.

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5

Aven, Terje y Bjørnar Heide. "Reliability and validity of risk analysis". Reliability Engineering & System Safety 94, n.º 11 (noviembre de 2009): 1862–68. http://dx.doi.org/10.1016/j.ress.2009.06.003.

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6

Gandomi, Amir H. y Amir H. Alavi. "Metaheuristics in Reliability and Risk Analysis". ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering 4, n.º 3 (septiembre de 2018): 02018001. http://dx.doi.org/10.1061/ajrua6.0000978.

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7

Koduru, Smitha D. y Terje Haukaas. "Uncertain reliability index in finite element reliability analysis". International Journal of Reliability and Safety 1, n.º 1/2 (2006): 77. http://dx.doi.org/10.1504/ijrs.2006.010691.

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8

Furuta, Kazuo y Shunsuke Kondo. "Group reliability analysis". Reliability Engineering & System Safety 35, n.º 2 (enero de 1992): 159–67. http://dx.doi.org/10.1016/0951-8320(92)90035-j.

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9

Singpurwalla, Nozer D. "Foundational Issues in Reliability and Risk Analysis". SIAM Review 30, n.º 2 (junio de 1988): 264–82. http://dx.doi.org/10.1137/1030047.

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10

Mahsuli, M. y T. Haukaas. "Seismic risk analysis with reliability methods, part II: Analysis". Structural Safety 42 (mayo de 2013): 63–74. http://dx.doi.org/10.1016/j.strusafe.2013.01.004.

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11

Putcha, Chandrasekhar y Binod Tiwari. "Interdisciplinary Applications of Reliability Analysis, Risk Analysis and Optimization". ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering 4, n.º 1 (marzo de 2018): 02017003. http://dx.doi.org/10.1061/ajrua6.0000958.

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12

Harnpornchai, N. "Genetic algorithm-aided reliability analysis". Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 225, n.º 1 (marzo de 2011): 62–80. http://dx.doi.org/10.1177/1748006xjrr302.

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13

Ansell, J. I. y M. J. Phillips. "Practical reliability data analysis". Reliability Engineering & System Safety 28, n.º 3 (enero de 1990): 337–56. http://dx.doi.org/10.1016/0951-8320(90)90119-8.

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14

Yacoub, S. M. y H. H. Ammar. "A methodology for architecture-level reliability risk analysis". IEEE Transactions on Software Engineering 28, n.º 6 (junio de 2002): 529–47. http://dx.doi.org/10.1109/tse.2002.1010058.

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15

Haining, F. W., R. F. Shaul, R. W. Keim y R. M. Murcko. "Improved Printed Circuit Reliability by Risk Site Analysis". Circuit World 15, n.º 4 (marzo de 1989): 31–38. http://dx.doi.org/10.1108/eb044006.

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16

Guedes Soares, Carlos. "Reliability engineering and risk analysis: a practical guide". Reliability Engineering & System Safety 77, n.º 2 (agosto de 2002): 207–8. http://dx.doi.org/10.1016/s0951-8320(02)00008-x.

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17

Wang, Ying, Zhiliang Zhu, Bo Yang, Fangda Guo y Hai Yu. "Using reliability risk analysis to prioritize test cases". Journal of Systems and Software 139 (mayo de 2018): 14–31. http://dx.doi.org/10.1016/j.jss.2018.01.033.

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18

Crespo, Luis G., Sean P. Kenny y Daniel P. Giesy. "Staircase predictor models for reliability and risk analysis". Structural Safety 75 (noviembre de 2018): 35–44. http://dx.doi.org/10.1016/j.strusafe.2018.05.002.

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19

You, Kesi, Lu Sun y Wenjun Gu. "Reliability-Based Risk Analysis of Roadway Horizontal Curves". Journal of Transportation Engineering 138, n.º 8 (agosto de 2012): 1071–81. http://dx.doi.org/10.1061/(asce)te.1943-5436.0000402.

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20

Momeni, Ehsan y Danial Jahed Armaghani. "Risk Management and Reliability Analysis in Civil Engineering". Open Construction & Building Technology Journal 14, n.º 1 (23 de agosto de 2018): 196–97. http://dx.doi.org/10.2174/1874836802014010196.

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21

Izabela, Zimoch. "Reliability Analysis of Water Distribution Subsystem". Journal of Konbin 7, n.º 4 (1 de enero de 2008): 307–26. http://dx.doi.org/10.2478/v10040-008-0094-7.

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Reliability Analysis of Water Distribution Subsystem This paper presents results of detailed reliability analysis of water distribution subsystem operation of Krakow city. Basis of the research was wide base of information of occurred failures during exploitation (1996-2006). These analysis included evaluation of basic factors such as: failure and renovation intensities, mean recovery time and mean time to failure, availability factor and probability of failure-free operation at any time. Moreover, it was performed wide analysis of failure capability of pipes as a function of its diameter and material. The paper consists also of research results of occurred piping failures reasons and consequences.

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22

Merlet, Jean Pierre. "Interval analysis and reliability in robotics". International Journal of Reliability and Safety 3, n.º 1/2/3 (2009): 104. http://dx.doi.org/10.1504/ijrs.2009.026837.

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23

SOSZYNSKA, JOANNA. "SYSTEMS RELIABILITY ANALYSIS IN VARIABLE OPERATION CONDITIONS". International Journal of Reliability, Quality and Safety Engineering 14, n.º 06 (diciembre de 2007): 617–34. http://dx.doi.org/10.1142/s0218539307002830.

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The semi-markov model of the system operation process is proposed and its selected parameters are defined. There are found reliability and risk characteristics of the multi-state series- "m out of k" system. Next, the joint model of the semi-markov system operation process and the considered multi-state system reliability and risk is constructed. The asymptotic approach to reliability and risk evaluation of this system in its operation process is proposed as well.

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24

Gabriška, D. "The block diagram of reliability analysis usage for analysis of safety critical systems". Journal of Applied Mathematics, Statistics and Informatics 13, n.º 2 (20 de diciembre de 2017): 29–38. http://dx.doi.org/10.1515/jamsi-2017-0007.

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Abstract Reliability of the technological processes or reliability of devices used in different industries is an important part of designing safety critical systems. The failure of such systems leads to economic losses, health damage or environmental pollution. An important role in the development of safety critical systems is therefore the reliability analysis, the assessment of the risks associated with the use of the technical means and the consequent reduction of this risk. The actual level of risk considered tolerable will vary depending on a number of factors such as the level of human control over the circumstances, the voluntary or unintentional nature of the risk, the number of people at risk in each individual case, the degree of responsibility placed on safety and critical systems reflects the need for quality design and ensure of software safety. Various standards and methods are used to achieve the desired level of safety. One of the methods used for reliability analysis is the use of a block diagram of reliability.

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25

Xia, Xiong, Lin Lin Li, Yi Huang, Sai Ying Xi y Han Dong Xu. "Anchored Slope Risk Analysis under Earthquake Effect". Advanced Materials Research 1051 (octubre de 2014): 786–90. http://dx.doi.org/10.4028/www.scientific.net/amr.1051.786.

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Horizontal earthquake acceleration is used for slope risk analysis, and the relationship between the dynamical safety factors and corresponding static safety factors is obtained. The reliability of anchored slope is expressed with the safety factor. The synthesized risk evaluation index, which included the dynamical, statically mechanics and reliability analysis, is established. The main procedure of calculation is provided by a practical project in this paper, and the computed example has shown it is worthy to study the method further.

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26

Hansson, Sven Ove y Terje Aven. "Is Risk Analysis Scientific?" Risk Analysis 34, n.º 7 (11 de junio de 2014): 1173–83. http://dx.doi.org/10.1111/risa.12230.

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27

Haas, Charles. "Coronavirus and Risk Analysis". Risk Analysis 40, n.º 4 (abril de 2020): 660–61. http://dx.doi.org/10.1111/risa.13481.

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28

Drożyner, Przemysław. "Risk analysis in maintenance processes". Engineering Management in Production and Services 12, n.º 4 (1 de diciembre de 2020): 64–76. http://dx.doi.org/10.2478/emj-2020-0028.

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Abstract The article aims to present practical methods for prioritising the activities of maintenance departments based on the Pareto analysis and the failure risk analysis. Based on the collected data on the number of observed failures and their removal times, commonly known reliability indicators were determined, which were then used to estimate the probabilities and consequences of failures in terms of the risk of loss of production continuity. Based on commonly collected failure data, the developed methods allow proposing to the maintenance departments the sequence of maintenance and repair work to be undertaken in terms of minimising the risk of failure. Risk analysis is somewhat commonly used in the practice of maintenance departments (e.g. RBI, FMEA, ETA, FTE, HIRA). The added value of this work is the use of reliability indicators for estimating the values of risk components, i.e., probability and consequences. The method was developed on the basis of operational data collected in one of the plants of the dairy cooperative and, after assessing the effects of its implementation, it was implemented in other enterprises of the cooperative.

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29

TODINOV, M. T. "RELIABILITY ANALYSIS AND SETTING RELIABILITY REQUIREMENTS BASED ON THE COST OF FAILURE". International Journal of Reliability, Quality and Safety Engineering 11, n.º 03 (septiembre de 2004): 273–99. http://dx.doi.org/10.1142/s0218539304001518.

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A theoretical framework and models are proposed for reliability analysis and setting reliability requirements based on the cost of failure. It is demonstrated that a high availability target does not necessarily limit the risk of failure or minimize the total losses. The proposed models include: (i) models for determining the value from the reliability investment, (ii) optimization models for minimizing the total losses, (iii) models for limiting the risk of failure below a maximum acceptable level, (iv) a model for guaranteeing an availability target and (v) a model for guaranteeing a minimum failure-free operating interval before each random failure in a finite time interval. The models related to the value from the reliability investment can be used to determine the effect from reducing early-life failures on the financial revenue. On the basis of a counterexample it is demonstrated that altering the hazard rates of the components may lead to decreasing the probability of failure of the system and a simultaneous increase of the risk of failure, which shows that the cost-of-failure reliability analysis requires new reliability tools, different from the conventional tools. A new closed-form relationship has been derived related to reliability associated with an overstress failure mechanism. On its basis, a method for setting reliability requirements has been proposed, which limits the risk of impact failure within a maximum acceptable level. On the basis of counterexamples, it has also been demonstrated that for a load and strength not following a normal distribution, the standard reliability measures "reliability index" and "loading roughness" can be misleading. A new reliability integral has been proposed, based on integration performed only within the region of the upper tail of the load distribution and the lower tail of the strength distribution.

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30

Inoue, Takeru. "Reliability Analysis for Disjoint Paths". IEEE Transactions on Reliability 68, n.º 3 (septiembre de 2019): 985–98. http://dx.doi.org/10.1109/tr.2018.2877775.

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31

Nannapaneni, Saideep y Sankaran Mahadevan. "Reliability analysis under epistemic uncertainty". Reliability Engineering & System Safety 155 (noviembre de 2016): 9–20. http://dx.doi.org/10.1016/j.ress.2016.06.005.

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32

Dougherty, Ed. "Context and human reliability analysis". Reliability Engineering & System Safety 41, n.º 1 (enero de 1993): 25–47. http://dx.doi.org/10.1016/0951-8320(93)90016-r.

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33

Schultz, Robert, Ahmad Sarfaraz y Kouroush Jenab. "Analysis of Risk and Reliability in Project Delivery Methods". International Journal of Strategic Decision Sciences 4, n.º 3 (julio de 2013): 54–65. http://dx.doi.org/10.4018/jsds.2013070103.

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Risk and reliability are two main factors that must be studied in order to measure the successful rate of a project. As a result, innovative project delivery methods have been proposed to mitigate the risk and improve reliability of a project. The intent of this study is to compare the use of the Analytical Hierarchical Process (AHP) and fuzzy AHP for decisions surrounding the early stages of construction projects based on risk and reliability measures. Financial risk is especially high during the early design stages of a project due to the unknown obstacles that will follow. The case study uses the selection of a project delivery method as an example, and provides a sample project to highlight the project-specific variability of the multi-criteria decision analysis.

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34

Elahi, Hassan, Khushboo Munir, Marco Eugeni y Paolo Gaudenzi. "Reliability Risk Analysis for the Aeroelastic Piezoelectric Energy Harvesters". Integrated Ferroelectrics 212, n.º 1 (11 de noviembre de 2020): 156–69. http://dx.doi.org/10.1080/10584587.2020.1819044.

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35

Rey, G., D. Clair, M. Fogli y F. Bernardin. "Reliability analysis of roadway departure risk using stochastic processes". Mechanical Systems and Signal Processing 25, n.º 4 (mayo de 2011): 1377–92. http://dx.doi.org/10.1016/j.ymssp.2010.11.015.

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36

Stewart, Mark G., David V. Rosowsky y Dimitri V. Val. "Reliability-based bridge assessment using risk-ranking decision analysis". Structural Safety 23, n.º 4 (octubre de 2001): 397–405. http://dx.doi.org/10.1016/s0167-4730(02)00010-3.

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37

Mahsuli, M. y T. Haukaas. "Seismic risk analysis with reliability methods, part I: Models". Structural Safety 42 (mayo de 2013): 54–62. http://dx.doi.org/10.1016/j.strusafe.2013.01.003.

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38

Hussein, Mohamed, Tarek Sayed, Karim Ismail y Adinda Van Espen. "Calibrating Road Design Guides Using Risk-Based Reliability Analysis". Journal of Transportation Engineering 140, n.º 9 (septiembre de 2014): 04014041. http://dx.doi.org/10.1061/(asce)te.1943-5436.0000694.

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39

Hamed, Maged M. "First-Order Reliability Analysis of Public Health Risk Assessment". Risk Analysis 17, n.º 2 (abril de 1997): 177–85. http://dx.doi.org/10.1111/j.1539-6924.1997.tb00857.x.

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40

Tyagi, Aditya y C. T. Haan. "Reliability, Risk, and Uncertainty Analysis Using Generic Expectation Functions". Journal of Environmental Engineering 127, n.º 10 (octubre de 2001): 938–45. http://dx.doi.org/10.1061/(asce)0733-9372(2001)127:10(938).

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41

Chowdhury, R. y P. Flentje. "Role of slope reliability analysis in landslide risk management". Bulletin of Engineering Geology and the Environment 62, n.º 1 (febrero de 2003): 41–46. http://dx.doi.org/10.1007/s10064-002-0166-1.

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42

Li, Yao y Frank PA Coolen. "Time-dependent reliability analysis of wind turbines considering load-sharing using fault tree analysis and Markov chains". Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 233, n.º 6 (3 de julio de 2019): 1074–85. http://dx.doi.org/10.1177/1748006x19859690.

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Due to the high failure rates and the high cost of operation and maintenance of wind turbines, not only manufacturers but also service providers try many ways to improve the reliability of some critical components and subsystems. In reality, redundancy design is commonly used to improve the reliability of critical components and subsystems. The load dependencies and failure dependencies among redundancy components and subsystems are crucial to the reliability assessment of wind turbines. However, the redundancy components are treated as a parallel system, and the load correlations among them are ignored in much literature, which may lead to the wrong system’s reliability and much higher costs. For this reason, this article explores the influences of load-sharing on system reliability. The whole system’s reliability is quantitatively evaluated using fault tree analysis and the Markov-chain method. Following this, the optimisation of the redundancy allocation problem considering the load-sharing is conducted to maximise the system reliability and reduce the total cost of the system subjecting to the available system cost and space. The results produced by this methodology can show a realistic reliability assessment of the entire wind turbine from a quantitative point of view. The realistic reliability assessment can help to design a cost-effective and more reliable system and significantly reduce the cost of wind turbines.

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43

Poursaeed, Mohammad Hossein. "Reliability analysis of an extended shock model". Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 235, n.º 5 (24 de enero de 2021): 845–52. http://dx.doi.org/10.1177/1748006x20987794.

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Suppose that a system is subject to a sequence of shocks which occur with probability p in any period of time [Formula: see text], and suppose that [Formula: see text] and [Formula: see text] are two critical levels ([Formula: see text]). The system fails when the time interval between two consecutive shocks is less than [Formula: see text], and the time interval bigger than [Formula: see text] has no effect on the system activity. In addition, the system fails with a probability of, say, [Formula: see text], when the time interval varies between [Formula: see text] and [Formula: see text]. Therefore, this model can be regarded as an extension of discrete time version of [Formula: see text]-shock model, and such an idea can be also applied in the extension of other shock models. The present study obtains the reliability function and the probability generating function of the system’s lifetime under this model. The present study offers some properties of the system and refers to a generalization of the new model. In addition, the mean time of the system’s failure is obtained under reduced efficiency which is created when the time between two consecutive shocks varies between [Formula: see text] and [Formula: see text] for the first time.

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44

Wu, Shaomin, Rui Peng y Mahmood Shafiee. "Guest Editorial: Reliability analysis for infrastructure systems". Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 236, n.º 3 (11 de mayo de 2022): 375–76. http://dx.doi.org/10.1177/1748006x211070641.

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45

Gu, Shuang y Keping Li. "Reliability analysis of high-speed railway network". Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 233, n.º 6 (18 de junio de 2019): 1060–73. http://dx.doi.org/10.1177/1748006x19853681.

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The reliability of high-speed railway network is an important issue for the sustainable development of railway traffic. A high reliable railway network not only has a longer service life but also has a greater ability to resist destruction of the network. In this article, based on the theory of complex network, we construct a topological networked model to study and analyze the reliability of high-speed railway network with respect to the destruction caused by natural disasters, geological disasters, equipment failure, or man-made disasters. In real world, heavy rain and snow storms are frequent on a large scale. These destructed regions are represented by network communities. Here, we put forward an evaluation index to quantify the network reliability. Taking China high-speed railway network as an example, the results show that some key communities has great influence on the network reliability. When these key communities are destructed by some natural factors, the reliability of railway network would reduce greatly or even breakdown. In addition, we find that the network reliability with the number of deleted communities approximately shows an exponential law.

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46

Kloess, Artemis, Hui Ping Wang y Mark E. Botkin. "Usage of meshfree methods in reliability analysis". International Journal of Reliability and Safety 1, n.º 1/2 (2006): 120. http://dx.doi.org/10.1504/ijrs.2006.010693.

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47

Adduri, Phani R. y Ravi C. Penmetsa. "Fast Fourier transform based system reliability analysis". International Journal of Reliability and Safety 1, n.º 3 (2007): 239. http://dx.doi.org/10.1504/ijrs.2007.014964.

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48

SCHNEIDEWIND, NORMAN. "SOFTWARE RISK ANALYSIS". International Journal of Reliability, Quality and Safety Engineering 16, n.º 02 (abril de 2009): 117–36. http://dx.doi.org/10.1142/s0218539309003320.

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There has been a lack of attention to the subject of risk management in the design and operation of software. This is strange because the risk to reliability is a critical problem in attempts to achieve a safe operation of the software. To address this problem, we evaluate existing models and introduce a new model for software risk prediction. The new model — cumulative failures gradient function — is based on the principles of neural networks. This metric identifiers the minimum test time required to achieve maximum improvement in software quality. We used three NASA Space Shuttle software systems in the evaluation of both existing and new models. The results showed that it was not possible to consistently rank these systems because the validity of the risk predictions varied depending on the risk model that was used. Therefore, the results suggest that it is advisable to use a variety of models to comprehensively evaluate the software risk.

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49

Marseguerra, Marzio, Enrico Zio y Massimo Librizzi. "Human Reliability Analysis by Fuzzy "CREAM"". Risk Analysis 27, n.º 1 (febrero de 2007): 137–54. http://dx.doi.org/10.1111/j.1539-6924.2006.00865.x.

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50

Mizumura, Kazumasa, Masato Yamamoto, Taiji Endo y Naofumi Shiraishi. "RELIABILITY ANALYSIS OF RUBBLE-MOUND BREAKWATERS". Coastal Engineering Proceedings 1, n.º 21 (29 de enero de 1988): 152. http://dx.doi.org/10.9753/icce.v21.152.

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To verify the effect of wave period on the motion of concrete blocks in rubble-mound breakwaters, simple physical models are employed and their motion is investigated by the numerical simulation. Finally, risk or reliability are calculated and the weight of concrete blocks for given physical condition is discussed by them.

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