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Academic literature on the topic 'Stochastic Fault Tree'

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Published: 10 March 2023

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Journal articles on the topic "Stochastic Fault Tree"

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Chovanec, A., A. Breznická, and P. Mikuš. "The fault tree stochastic analysis." IOP Conference Series: Materials Science and Engineering 776 (April 2, 2020): 012024. http://dx.doi.org/10.1088/1757-899x/776/1/012024.

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Jenab, K., and B. S. Dhillon. "Stochastic Fault Tree Analysis With Self-Loop Basic Events." IEEE Transactions on Reliability 54, no. 1 (March 2005): 173–80. http://dx.doi.org/10.1109/tr.2004.842087.

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Chiacchio, Aizpurua, Compagno, Khodayee, and D’Urso. "Modelling and Resolution of Dynamic Reliability Problems by the Coupling of Simulink and the Stochastic Hybrid Fault Tree Object Oriented (SHyFTOO) Library." Information 10, no. 9 (September 11, 2019): 283. http://dx.doi.org/10.3390/info10090283.

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Dependability assessment is one of the most important activities for the analysis of complex systems. Classical analysis techniques of safety, risk, and dependability, like Fault Tree Analysis or Reliability Block Diagrams, are easy to implement, but they estimate inaccurate dependability results due to their simplified hypotheses that assume the components’ malfunctions to be independent from each other and from the system working conditions. Recent contributions within the umbrella of Dynamic Probabilistic Risk Assessment have shown the potential to improve the accuracy of classical dependability analysis methods. Among them, Stochastic Hybrid Fault Tree Automaton (SHyFTA) is a promising methodology because it can combine a Dynamic Fault Tree model with the physics-based deterministic model of a system process, and it can generate dependability metrics along with performance indicators of the physical variables. This paper presents the Stochastic Hybrid Fault Tree Object Oriented (SHyFTOO), a Matlab® software library for the modelling and the resolution of a SHyFTA model. One of the novel features discussed in this contribution is the ease of coupling with a Matlab® Simulink model that facilitates the design of complex system dynamics. To demonstrate the utilization of this software library and the augmented capability of generating further dependability indicators, three different case studies are discussed and solved with a thorough description for the implementation of the corresponding SHyFTA models.
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Bose, S. Subash Chandra, Badria Sulaiman Alfurhood, Gururaj H. L, Francesco Flammini, Rajesh Natarajan, and Sheela Shankarappa Jaya. "Decision Fault Tree Learning and Differential Lyapunov Optimal Control for Path Tracking." Entropy 25, no. 3 (March 2, 2023): 443. http://dx.doi.org/10.3390/e25030443.

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This paper considers the main challenges for all components engaged in the driving task suggested by the automation of road vehicles or autonomous cars. Numerous autonomous vehicle developers often invest an important amount of time and effort in fine-tuning and measuring the route tracking to obtain reliable tracking performance over a wide range of autonomous vehicle speed and road curvature diversities. However, a number of automated vehicles were not considered for fault-tolerant trajectory tracking methods. Motivated by this, the current research study of the Differential Lyapunov Stochastic and Decision Defect Tree Learning (DLS-DFTL) method is proposed to handle fault detection and course tracking for autonomous vehicle problems. Initially, Differential Lyapunov Stochastic Optimal Control (SOC) with customizable Z-matrices is to precisely design the path tracking for a particular target vehicle while successfully managing the noise and fault issues that arise from the localization and path planning. With the autonomous vehicle’s low ceilings, a recommendation trajectory generation model is created to support such a safety justification. Then, to detect an unexpected deviation caused by a fault, a fault detection technique known as Decision Fault Tree Learning (DFTL) is built. The DLS-DFTL method can be used to find and locate problems in expansive, intricate communication networks. We conducted various tests and showed the applicability of DFTL. By offering some analysis of the experimental outcomes, the suggested method produces significant accuracy. In addition to a thorough study that compares the results to state-of-the-art techniques, simulation was also used to quantify the rate and time of defect detection. The experimental result shows that the proposed DLS-DFTL enhances the fault detection rate (38%), reduces the loss rate (14%), and has a faster fault detection time (24%) than the state of art methods.
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Xu, Yi Xin, Yan Bai, and Ren Shu Wang. "GSPN-Based Reliability Model of Wireless Control System." Applied Mechanics and Materials 392 (September 2013): 374–78. http://dx.doi.org/10.4028/www.scientific.net/amm.392.374.

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It is difficult to model and solve the reliability of complicated dynamic system in the traditional reliability modeling method. A new reliability modeling method based on generalized stochastic petri net (GSPN) is proposed to analyze the wireless control system (WCS). After studying the common faults of the WCS, a dynamic fault tree is constructed to analyze the system, and it is transformed into formalization definition of GSPN. Finally, a certain type of WCS is selected to validate the proposed method. This method has important value to other WCS design and analysis.
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Tang, Li Zhi, Jun Du, Kun Peng Xu, and Xue Qing Qi. "Distribution System Restoration Based on Hybrid Particle Swarm." Advanced Materials Research 732-733 (August 2013): 662–68. http://dx.doi.org/10.4028/www.scientific.net/amr.732-733.662.

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By the heuristic algorithm and particle swarm optimization algorithm combining hybrid particle swarm algorithm proposed combination of heuristic search and stochastic optimization,stochastic optimization process using a spanning tree and the loop matrix operations combined to ensure the system topology constraints to improve the efficiency of solution. The analysis shows that the proposed method calculation speed,easy to converge to the global optimal solution. It can effectively solve the problem of distribution network fault recovery.
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Huang, Tao, Zining Cao, and Qing Li. "Verification and Fault Analysis based on Combination of AADL and Modelica." Journal of Physics: Conference Series 2261, no. 1 (June 1, 2022): 012014. http://dx.doi.org/10.1088/1742-6596/2261/1/012014.

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Abstract CPS is a multidimensional complex system that can realize the interaction between computing process and physical process. Aiming at the problems of fault occurrence and uncertain behavior, this paper proposes the fault analysis stochastic hybrid automata as a formal model, the attributes of randomness and fault analysis are added through AADL behavior attachment to expand the attributes of hybrid automata, and applies the extended automata to the embedded system for system description and fault analysis. The model is used to model the fire control system, and AADL, Modelica and fault tree are combined to form a new model. The behavior is analyzed, and the conversion algorithm and conversion example are given.
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Liu, Wuqiang, Xiaoqiang Yang, and Shen Jinxing. "An Integrated Fault Identification Approach for Rolling Bearings Based on Dual-Tree Complex Wavelet Packet Transform and Generalized Composite Multiscale Amplitude-Aware Permutation Entropy." Shock and Vibration 2020 (November 26, 2020): 1–18. http://dx.doi.org/10.1155/2020/8851310.

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The health condition of rolling bearings, as a widely used part in rotating machineries, directly influences the working efficiency of the equipment. Consequently, timely detection and judgment of the current working status of the bearing is the key to improving productivity. This paper proposes an integrated fault identification technology for rolling bearings, which contains two parts: the fault predetection and the fault recognition. In the part of fault predetection, the threshold based on amplitude-aware permutation entropy (AAPE) is defined to judge whether the bearing currently has a fault. If there is a fault in the bearing, the fault feature is adequately extracted using the feature extraction method combined with dual-tree complex wavelet packet transform (DTCWPT) and generalized composite multiscale amplitude-aware permutation entropy (GCMAAPE). Firstly, the method decomposes the fault vibration signal into a set of subband components through the DTCWPT with good time-frequency decomposing capability. Secondly, the GCMAAPE values of each subband component are computed to generate the initial candidate feature. Next, a low-dimensional feature sample is established using the t-distributed stochastic neighbor embedding (t-SNE) with good nonlinear dimensionality reduction performance to choose sensitive features from the initial high-dimensional features. Afterwards, the featured specimen representing fault information is fed into the deep belief network (DBN) model to judge the fault type. In the end, the superiority of the proposed solution is verified by analyzing the collected experimental data. Detection and classification experiments indicate that the proposed solution can not only accurately detect whether there is a fault but also effectively determine the fault type of the bearing. Besides, this solution can judge the different faults more accurately compared with other ordinary methods.
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Chiacchio, Ferdinando, Fabio Famoso, Diego D’Urso, Sebastian Brusca, Jose Aizpurua, and Luca Cedola. "Dynamic Performance Evaluation of Photovoltaic Power Plant by Stochastic Hybrid Fault Tree Automaton Model." Energies 11, no. 2 (January 31, 2018): 306. http://dx.doi.org/10.3390/en11020306.

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Wang, Bing, Guangdong Tian, Yanping Liang, and Tiangang Qiang. "Reliability Modeling and Evaluation of Electric Vehicle Motor by Using Fault Tree and Extended Stochastic Petri Nets." Journal of Applied Mathematics 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/638013.

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Performing reliability analysis of electric vehicle motor has an important impact on its safety. To do so, this paper proposes its reliability modeling and evaluation issues of electric vehicle motor by using fault tree (FT) and extended stochastic Petri nets (ESPN). Based on the concepts of FT and ESPN, an FT based ESPN model for reliability analysis is obtained. In addition, the reliability calculation method is introduced and this work designs a hybrid intelligent algorithm integrating stochastic simulation and NN, namely, NN based simulation algorithm, to solve it. Finally, taking an electric vehicle motor as an example, its reliability modeling and evaluation issues are analyzed. The results illustrate the proposed models and the effectiveness of proposed algorithms. Moreover, the results reported in this work could be useful for the designers of electric vehicle motor, particularly, in the process of redesigning the electric vehicle motor and scheduling its reliability growth plan.
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Dissertations / Theses on the topic "Stochastic Fault Tree"

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Manno, Gabriele Antonino. "Reliability modelling of complez systems: an adaptive transition system approach to match accuracy and efficiency." Doctoral thesis, Università di Catania, 2012. http://hdl.handle.net/10761/1039.

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In questa tesi è introdotto un linguaggio di modellizzazione per la valutazione di misure di affidabilità di sistemi complessi. Adaptive Transition Systems (ATS) è un linguaggio di modellizzazione basato su macchine di stato interdipendenti dotate di variabili che sono inputs e outputs di un set di funzoni di comunicazione che permettono l'adattamento delle prprietà legate alle transizioni delle macchine di stato rispetto all'evoluzione del sistema. Le funzioni di comunicazione sono il mezzo per modellizzare le dipendenze tra le parti del sistema. Gli algoritmi per la risoluzione simulativa ed analitica del modello ATS sono definiti. Un applicazione per la risoluzione di alberi di guasto dinamici con componenti riparabili è anche riportata.
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Sampietro, Samuele. "Timed Failure Logic Analysis in a Model-Driven Engineering approach." Doctoral thesis, 2021. http://hdl.handle.net/2158/1238685.

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A complex System of Systems, integrating several hardware and software components in the holistic perspective of providing an emergent behaviour and operating within business-critical contexts, aims at affording contrasting requirements of reliability and complexity in delivered functions and quality of services by supporting system evolution and adaptation over time. This dissertation contributes to the area of Model-Driven Engineering (MDE), proposing a model-driven approach supporting timed failure logic analysis of complex Cyber-Physical Systems (CPS) in business-critical scenarios. The research defines a meta-model joining structural information about system architectures with their failure logic, decoupling representations of communication interfaces from those of failure propagation. The meta-model also supports runtime evolution (which can be very fast in the case of complex CPS) of concrete systems by enabling the configuration of product lines, capable of representing multiple variation points of a component, supporting continuous adaptation of offered products and services to business or customer needs. The meta-model enables a round-trip engineering process through the definition of a set of transformation rules, supporting the automated and correct-by-construction initialisation of meta-model instances starting from SysML Block Definition Diagrams for system specification and stochastic Fault Trees for timed failure logic, thus activating co-evolution mechanisms propagating external manual modifications, applied on meta-model instances, directly to the adopted structural and reliability artefacts. At the same time, a set of transformation rules has been defined so as to enable the automated generation of Stochastic Time Petri Nets (STPN) from meta-model instances, thus supporting quantitative evaluation of the imed failure logic. The MDE approach is demonstrated on the case study of a CPS operating in a Smart City environment, evaluating at design time different configurations of the system with respect to the reliability of its cyber-side. The research also addresses the design and the prototypical implementation of a tool offered both as-a-service and as a Java API.
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Book chapters on the topic "Stochastic Fault Tree"

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Famoso, F., F. Chiacchio, S. Brusca, and D. D’Urso. "Stochastic hybrid fault tree automaton for the production forecast of PV power plant." In Risk, Reliability and Safety: Innovating Theory and Practice, 824–30. Taylor & Francis Group, 6000 Broken Sound Parkway NW, Suite 300, Boca Raton, FL 33487-2742: CRC Press, 2016. http://dx.doi.org/10.1201/9781315374987-123.

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Fourneau, J. M., and Nihal Pekergin. "A Numerical Analysis of Dynamic Fault Trees Based on Stochastic Bounds." In Quantitative Evaluation of Systems, 176–91. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-22264-6_12.

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Pinheiro, Thiago, Danilo Oliveira, Rubens Matos, Bruno Silva, Paulo Pereira, Carlos Melo, Felipe Oliveira, Eduardo Tavares, Jamilson Dantas, and Paulo Maciel. "The Mercury Environment: A Modeling Tool for Performance and Dependability Evaluation." In Intelligent Environments 2021. IOS Press, 2021. http://dx.doi.org/10.3233/aise210075.

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It is important to be able to judge the performance or dependability metrics of a system and often we do so by using abstract models even when the system is in the conceptual phase. Evaluating a system by performing measurements can have a high temporal and/or financial cost, which may not be feasible. Mathematical models can provide estimates about system behavior and we need tools supporting different types of formalisms in order to compute desired metrics. The Mercury tool enables a range of models to be created and evaluated for supporting performance and dependability evaluations, such as reliability block diagrams (RBDs), dynamic RBDs (DRBDs), fault trees (FTs), stochastic Petri nets (SPNs), continuous and discrete-time Markov chains (CTMCs and DTMCs), as well as energy flow models (EFMs). In this paper, we introduce recent enhancements to Mercury, namely new SPN simulators, support to prioritized timed transitions, sensitivity analysis evaluation, several improvements to the usability of the tool, and support to DTMC and FT formalisms.
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Conference papers on the topic "Stochastic Fault Tree"

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Herscheid, Lena, and Peter Troger. "Specification of Dynamic Fault Tree Concepts with Stochastic Petri Nets." In 2014 8th IEEE International Conference on Software Security and Reliability (SERE). IEEE, 2014. http://dx.doi.org/10.1109/sere.2014.31.

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Cheshmikhani, Elham, and Hamid R. Zarandi. "Accelerating Dynamic Fault Tree Analysis Based on Stochastic Logic Utilizing GPGPUs." In 2016 24th Euromicro International Conference on Parallel, Distributed, and Network-Based Processing (PDP). IEEE, 2016. http://dx.doi.org/10.1109/pdp.2016.130.

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Aliee, Hananeh, and Hamid R. Zarandi. "Fault tree analysis using stochastic logic: A reliable and high speed computing." In Integrity (RAMS). IEEE, 2011. http://dx.doi.org/10.1109/rams.2011.5754466.

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Onyebueke, Landon C., Chinyere Onwubiko, and Feng C. Chen. "Probabilistic Design Methodology and the Application of Probabilistic Fault Tree Analysis to Machine Design." In ASME 1995 Design Engineering Technical Conferences collocated with the ASME 1995 15th International Computers in Engineering Conference and the ASME 1995 9th Annual Engineering Database Symposium. American Society of Mechanical Engineers, 1995. http://dx.doi.org/10.1115/detc1995-0144.

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Abstract This paper gives an overview of Probabilistic Design Methodology (PDM) with emphasis on quantification of the effects of uncertainties for structural variables and the evaluation of failure probability. The application of Probabilistic Fault Tree Analysis (PFTA) to the design of a shaft carrying a spur gear is also presented. The PFTA includes the development of a fault tree to represent the system, construction of an approximation function for bottom events, computation of sensitivity factors of design variables, and the calculation of the system reliability. The computer code employed for the analyses is known as “Numerical Evaluation of Stochastic Structures Under Stress” (NESSUS). NESSUS is developed under NASA probabilistic structural analysis program.
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Khosravi Babadi, Parham, and Lixuan Lu. "Reliability and Safety Assessment of Passive Safety Systems Through Coupling of Fault Tree Analysis and Artificial Neural Network." In ASME 2022 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/imece2022-95897.

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Abstract Small Modular Reactors (SMRs) have attracted much attention in recent years, and they could play a significant role in the future of energy supply and the nuclear industry. Many factors have contributed to the advancement of SMRs, including their affordability and zero greenhouse gas emissions. However, the most significant advantage associated with SMRs is their increased safety level, which has been achieved by introducing a wide range of new design features. Despite the diversity of design techniques, a similar set of design principles, such as Passive Safety Systems (PSSs), has been adopted to improve plant safety and robustness, eliminate design vulnerabilities, minimize accident likelihood, and mitigate accident effects. Reliability and safety evaluation of PSSs are crucial from the design phase to achieve these objectives. Probabilistic Safety Assessment (PSA) is a well-known methodology for analyzing risk levels associated with safety-critical systems in many industries, such as the aerospace, oil and gas, and nuclear industries. Probabilistic safety assessment utilizes the combination of Event Tree (ET) and Fault Tree (FT) techniques to estimate risks associated with certain undesired top events, such as core meltdown in the nuclear industry. Although PSA offers a range of advantages for safety assessment compared with traditional deterministic risk analysis technology, it also has some limitations. There are still many challenges associated with dynamic PSA analysis due to the demand for computational power for oversized FTs and ETs. Moreover, the final assessment result is prone to a significant uncertainty level due to human-related errors. Some of the challenges associated with PSA might be alleviated by Artificial Neural Networks (ANNs), as ANNs address the limitations of PSA, such as adaptive capacity, learning ability, and real-time calculation, which are challenging for dynamic process systems. Apart from ANNs, Bayesian Networks (BNs) are used to establish the collection of stochastic processes and their conditional dependencies through graphical connections. Bayesian Network is a graph layout that models accident scenarios and various real-world problems. This paper investigates the application of artificial intelligence (Deep Learning (DL)) to enhance FT analysis through the conversion of FT and ANN models. The potentiality of extending this technique to analyze the reliability and safety of PSSs in SMRs is examined. In SMRs, natural circulation has a low driving force, and PSSs are easily manipulated by system variables such as heat loss, flow friction, and oxidation, leading to system instability and jeopardizing the system’s safety. As a result, FT analysis is inadequate to capture these effects in real-time to analyze the reliability and safety of PSSs. This paper demonstrates that the introduction of ANN could help address some of these limitations.
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Yin, Jiubo, Jianyuan Cao, and Xu Wang. "An Implementation of DFM for Reliability Modeling and Analyzing of AP1000 FWCS." In 2013 21st International Conference on Nuclear Engineering. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/icone21-15828.

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Both in the process of designing instrumentation and control (I&C) systems of nuclear power plants and replacement of aging and obsolete ones, digital technology are more and more popular. However, traditional static methodology for probabilistic risk assessment (PRA) may not yield satisfactory results when applied to digital systems. As a result, some dynamic methodologies are employed in the analysis for I&C systems of nuclear power plants. The dynamic flowgraph methodology (DFM) is one of the favorable methodologies which brings out most positive features and least negative features. The I&C system of AP1000 is a distributed digital I&C system, but its PRAs are based on the event tree (ET)/fault tree (FT) approach. The results are admitted to be enlarged to some extent. Also, under some circumstances, ET/FT approach cannot account for the coupling between triggered or stochastic logical events. Regarding these disadvantages, DFM is introduced in modeling of Feed-water Control System (FWCS) of AP1000. A basic system of FWCS is constructed, which contains the main components and control logic of FWCS. Furthermore, failure mode and effect analysis (FMEA) of the FWCS system is carried out in the way of analyzing each component individually. After these efforts, a DFM model of the basic system is built utilizing the DFM language. After the DFM model has been produced, the application of deductive analysis shows different initial conditions or events that can cause the FWCS system to fail in different modes.
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Yu, Yu, Shengfei Wang, Xuefeng Lv, and Fenglei Niu. "Reliability Analysis for Passive Containment Cooling System in Seismic Hazard." In 2012 20th International Conference on Nuclear Engineering and the ASME 2012 Power Conference. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/icone20-power2012-55040.

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Passive containment cooling system is an important safety-related system in AP1000 nuclear power plant, by which heat produced in reactor is transferred to the heat sink–atmosphere based on natural circulation. So it is important to the plant safety whether the system can work well or not in the seismic hazard. Since the system operation is independent of human interfere or the operation of outside equipments, the reliability of the system is improved, however, physical process failure become one of the important contributors to the system operation failure because natural circulation may not keep when the system configuration is different from the design. So it is necessary to analyze the system reliability in seismic situation. The equipment failure probability under earthquake is a function of the peak ground acceleration which is stochastic, and the fault tree method used in traditional probability safety assessment (PSA) for system reliability analysis is not power enough to deal with conditional probability. In this paper, a new analysis method for system reliability evaluate at seismic situation based on Monte Carlo (MC) simulation is put forward, and annual failure probability of passive containment cooling system in AP1000 in seismic hazard is calculated, the result is according with the AP1000 seismic margin evaluation.
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Bao, Shiyi, Zhibin Li, Lijia Luo, and Zengliang Gao. "Reliability Analysis of Spring Operated Pressure Relief Valve." In ASME 2013 Pressure Vessels and Piping Conference. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/pvp2013-97303.

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Pressure relief valve (PRV) is an important automatic overpressure protection system in the process industry. Because of the operating characteristics, the performance of PRV is supposed to be proved by the proof test. However, it’s difficult to determine the proof test intervals and the availability of the PRV between two proof tests. Based on stochastic Petri nets (SPN), the reliability modeling and analysis procedure of spring operated full lift pressure relief valve which is the most widely used PRV is depicted in this paper. Firstly, the FMECA method is used to analyze the causes and effects of the typical six failure modes of the PRV, such as vibration, leakage, frequency hopping, unable to open, open before the settings and the low back seat pressure. Second, the corresponding fault tree (FT) models of the PRV are built through the multi-component failure analysis. Third, the SPN models of the PRV are established by employing the logical relations in the FT models. Based on the collected failure data of the PRVs, the steady state and transient reliability index are calculated by Monte Carlo simulation based on the SPN software SPN@. Last, the idea about PRV reliability data collection in domestic process industries is proposed. The result of the reliability analysis can provide the basis for determination the proof test intervals of the PRV, and the proposed procedure also bears significance in its application in the reliability analysis of general system in process industry.
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Carnevali, L., L. Ridi, and E. Vicario. "Stochastic Fault Trees for cross-layer power management of WSN monitoring systems." In Factory Automation (ETFA 2009). IEEE, 2009. http://dx.doi.org/10.1109/etfa.2009.5347071.

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Hu, Jialiang, Pradeep Menon, Amna Al Yaqoubi, Mohamed Al Shehhi, Mahmoud Basioni, Fabio Roncarolo, and Natela Belova. "Fracture Characterization in Deep Gas Reservoirs to Identify Fracture Enhanced Flow Units, Offshore Abu Dhabi." In Abu Dhabi International Petroleum Exhibition & Conference. SPE, 2021. http://dx.doi.org/10.2118/207646-ms.

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Abstract High gas flow rates in deep-buried dolomitized reservoir from an offshore field Abu Dhabi cannot be explained by the low matrix permeability. Previous permeability multiplier based on distance to major faults is not a solid geological solution due to over-simplifying reservoir geomechanics, overlooking folding-related fractures, and lack of detailed fault interpretation from poor seismic. Alternatively, to characterize the heterogeneous flow related with natural fractures in this undeveloped reservoir, fracture network is modelled based on core, bore hole imager (BHI), conventional logs, seismic data and test information. Limited by investigation scale, vertical wells record apparent BHI, and raw fracture interpretation cannot represent true 3D percolation reflected on PLT. To overcome this shortfall, correction based on geomechanics and mechanical layer (ML) analysis is performed. Young's modulus (E), Poisson ratio (ν), and brittleness index are calculated from logs, describing reservoir tendency of fracturing. Other than defining MLs, bedding plane intensity from BHI is also used as an indicator of fracture occurrence, since stress tends to release at strata discontinuity and forms bed-bounded fractures observed from cores. Subsequently, a new fracture intensity is generated from combined geomechanics properties and statistics average of BHI-derived fracture occurrence within the ML frame, which improves match with PLT and distinguishes fracture enhance flow intervals consistently in all wells. Seismic discontinuity attributes are used as static fracture footprints to distribute fractures from wells to 3D. The final hybrid DFN comprises large-scale deterministic zone-crossing fractures and small-scale stochastic bed-bounded fractures. Sub-vertical open fractures are dominated by NE-SW wrenching fractures related with Zagros compression and reactive salt upward movement. There is no angle rotation of fractures in different fault blocks. Open fractures in other strikes are supported by partial cements and mismatching fracture walls on computerized tomography (CT) images. ML correlation shows vertical consistence across stratigraphic framework and its intensity indicates fracture potential of vertical zones reflected by tests. Fracture-enhanced flow units are further constrained by a threshold in both combined geomechanics properties and statistics average of raw BHI fracture intensity in ML frame. As a result, final fracture network maps reservoir brittleness and flow potential both vertically and laterally, identifying fracture regions along folding axis not just major faults, evidenced by wells and seismic. According to the upscaling results, the case study reveals a type-III fractured reservoir, where fractures contribute to flow not to volume. Fracture network enhances bed-wise horizontal communication but also opens vertical feeding channels. Fracture permeability is mainly influenced by aperture and intensity, while aspect ratio, fracture length, and proportion of strikes and dips mainly influence permeability distribution rather than absolute values. This study provides a production-oriented characterization workflow of natural fracture heterogeneity based on correction of raw BHI in undeveloped fields.
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