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Статті в журналах з теми "Probabilistic fault evaluation":
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Kourd, Yahia, Dimitri Lefebvre, and Noureddine Guersi. "Neural Networks and Fault Probability Evaluation for Diagnosis Issues." Computational Intelligence and Neuroscience 2014 (2014): 1–15. http://dx.doi.org/10.1155/2014/370486.
This paper presents a new FDI technique for fault detection and isolation in unknown nonlinear systems. The objective of the research is to construct and analyze residuals by means of artificial intelligence and probabilistic methods. Artificial neural networks are first used for modeling issues. Neural networks models are designed for learning the fault-free and the faulty behaviors of the considered systems. Once the residuals generated, an evaluation using probabilistic criteria is applied to them to determine what is the most likely fault among a set of candidate faults. The study also includes a comparison between the contributions of these tools and their limitations, particularly through the establishment of quantitative indicators to assess their performance. According to the computation of a confidence factor, the proposed method is suitable to evaluate the reliability of the FDI decision. The approach is applied to detect and isolate 19 fault candidates in the DAMADICS benchmark. The results obtained with the proposed scheme are compared with the results obtained according to a usual thresholding method.
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Eslami, R., S. H. H. Sadeghi, and H. Askarian Abyaneh. "A Probabilistic Approach for the Evaluation of Fault Detection Schemes in Microgrids." Engineering, Technology & Applied Science Research 7, no. 5 (October 19, 2017): 1967–73. http://dx.doi.org/10.48084/etasr.1472.
An important challenge in protection of a microgrid is the process of fault detection, considering the uncertainties in its topologies. Equally important is the evaluation of proposed methods as their incorrect performances could result in unreasonable power outages. In this paper, a new fault detection and characterization method is introduced and evaluated subject to the uncertainties of network topologies. The features of three-phase components together with the positive, negative and zero sequences of current and voltage waveforms are derived to detect the occurrence of a fault, its location, type and the engaged phases. The proposed method is independent of the microgrid topology. To evaluate the performance of the proposed method in various network topologies, a Monte Carlo scheme is developed. This is done by computing the expected energy not-supplied reliability index and the percentage of successful performance of the fault detection. Simulation results show that the proposed method can detect faults in various microgrid topologies with a very high degree of accuracy.
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Gnanavel, S., M. Sreekrishna, Vinodhini Mani, G. Kumaran, R. S. Amshavalli, Sadeen Alharbi, Mashael Maashi, et al. "Analysis of Fault Classifiers to Detect the Faults and Node Failures in a Wireless Sensor Network." Electronics 11, no. 10 (May 18, 2022): 1609. http://dx.doi.org/10.3390/electronics11101609.
Technology evaluation in the electronics field leads to the great development of Wireless Sensor Networks (WSN) for a variety of applications. The sensor nodes are deployed in hazardous environments, and they are operated by isolated battery sources. Network connectivity is purely based on power availability, which impacts the network lifetime. Hence, power must be used wisely to prolong the network lifetime. The sensor nodes that fail due to power have to detect quickly to maintain the network. In a WSN, classifiers are used to detect the faults for checking the data generated by the sensor nodes. In this paper, six classifiers such as Support Vector Machine, Convolutional Neural Network, Multilayer Perceptron, Stochastic Gradient Descent, Random Forest and Probabilistic Neural Network have been taken for analysis. Six different faults (Offset fault, Gain fault, Stuck-at fault, Out of Bounds, Spike fault and Data loss) are injected in the data generated by the sensor nodes. The faulty data are checked by the classifiers. The simulation results show that the Random Forest detected more faults and it also outperformed all other classifiers in that category.
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Goda, Katsuichiro. "Potential Fault Displacement Hazard Assessment Using Stochastic Source Models: A Retrospective Evaluation for the 1999 Hector Mine Earthquake." GeoHazards 2, no. 4 (December 4, 2021): 398–414. http://dx.doi.org/10.3390/geohazards2040022.
Surface fault displacement due to an earthquake affects buildings and infrastructure in the near-fault area significantly. Although approaches for probabilistic fault displacement hazard analysis have been developed and applied in practice, there are several limitations that prevent fault displacement hazard assessments for multiple locations simultaneously in a physically consistent manner. This study proposes an alternative approach that is based on stochastic source modelling and fault displacement analysis using Okada equations. The proposed method evaluates the fault displacement hazard potential due to a fault rupture. The developed method is applied to the 1999 Hector Mine earthquake from a retrospective perspective. The stochastic-source-based fault displacement hazard analysis method successfully identifies multiple source models that predict fault displacements in close agreement with observed GPS displacement vectors and displacement offsets along the fault trace. The case study for the 1999 Hector Mine earthquake demonstrates that the proposed stochastic-source-based method is a viable option in conducting probabilistic fault displacement hazard analysis.
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Feng, Zhi Gang, Qi Wang, and K. Shida. "Validated Uncertainty Evaluation for Self-Validating Sensor." Key Engineering Materials 381-382 (June 2008): 419–22. http://dx.doi.org/10.4028/www.scientific.net/kem.381-382.419.
This paper evaluates the validated uncertainty in SEVA sensor by integrating fault detection, identification and reconstruction (FDIR) and reliability engineering. The impact of each fault mode on measurement quality is evaluated quantitatively by using a priori sensor reliability information to investigate the impact of incomplete fault coverage, FDIR and manual maintenance intervention. Bayesian probabilistic approach and uncertainty calculus are employed to model the impact of sensor validation on parameter uncertainty and to fuse the individual modes into a complete sensor model. A simulation of SEVA pressure sensor example illustrates the concept and conclusions.
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Khan, Faisal I., and Tahir Husain. "Risk Assessment and Safety Evaluation Using Probabilistic Fault Tree Analysis." Human and Ecological Risk Assessment: An International Journal 7, no. 7 (December 2001): 1909–27. http://dx.doi.org/10.1080/20018091095483.
Nair, V. S. S., Y. V. Hoskote, and J. A. Abraham. "Probabilistic evaluation of online checks in fault-tolerant multiprocessor systems." IEEE Transactions on Computers 41, no. 5 (May 1992): 532–41. http://dx.doi.org/10.1109/12.142679.
Katona, Tamás János, László Tóth, and Erzsébet Győri. "Fault Displacement Hazard Analysis Based on Probabilistic Seismic Hazard Analysis for Specific Nuclear Sites." Applied Sciences 11, no. 15 (August 3, 2021): 7162. http://dx.doi.org/10.3390/app11157162.
Permanent ground displacements/deformations caused by earthquakes can seriously challenge the safety of the nuclear power plants. The state-of-the-art hazard analysis methods provide a fault displacement hazard curve, i.e., the annual probability of given measure of displacement will be exceeded. The evaluation of ground displacement hazard requires great effort, empirical evidence, and sufficient data for the characterization of the fault activity and capability to cause permanent surface displacement. There are practical cases when the fault at the site area revealed to be active, and, despite this, there are no sufficient data for the evaluation of permanent ground displacements hazard and for judging on the safety significance of permanent ground displacement. For these cases, a methodology is proposed that is based on the seismotectonic modelling and results of the probabilistic seismic hazard analysis. The method provides conservative assessment of the annual probability of fault displacement that allows the decision whether permanent displacement hazard is relevant to nuclear power plant safety. The feasibility and applicability of the method is demonstrated for the Paks site, Hungary.
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FENG, Yunwen, Zhicen SONG, Cheng LU, and Xiaofei LIU. "Analysis of comprehensive sensitivity evaluation of aircraft system based on FTA-AHP." Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University 39, no. 5 (October 2021): 971–77. http://dx.doi.org/10.1051/jnwpu/20213950971.
Aiming at the lack of unified evaluation index for the sensitivity analysis of fault tree of the system, the Analytic Hierarchy Process(AHP) is introduced into Fault Tree Analysis(FTA), and an FTA-AHP method is proposed to generate the comprehensive sensitivity evaluation index of the system. Firstly, the reliability analysis model of the system fault tree is established, and three indexes including probabilistic sensitivity, relative sensitivity and structural sensitivity, are calculated by using the traditional sensitivity analysis method. Then, combined with the analytic hierarchy process, the weight coefficients were determined according to the sensitivity analysis results above and the type of the minimum cut set, and the comprehensive sensitivity evaluation and analysis of the system fault tree were carried out. Finally, the case verification shows that compared with the traditional method, the comprehensive sensitivity ranking results have less repeatability, and the factors are considered comprehensively, which is convenient for decision makers to make judgment. The synthetic sensitivity evaluation model developed provides support for system reliability analysis.
Les événements singuliers proviennent de l'interaction d'une particule énergétique avec un circuit microélectronique. Ces perturbations peuvent modifier d'une manière imprévue le fonctionnement du circuit et introduire des fautes. La sensibilité des circuits augmentant à chaque nouvelle génération technologique, il devient nécessaire de disposer d'outils spécifiques pour la conception des circuits durcis face aux événements singuliers. Les travaux de cette thèse visent à étendre la compréhension de ces phénomènes et à proposer des outils CAO pour faciliter l'analyse de ces problèmes dans les circuits actuels. Nous avons développé des méthodologies pour l'analyse du comportement des cellules de la bibliothèque standard et des outils pour la simulation accélérée des fautes et pour l'évaluation probabiliste des effets singuliers. Les résultats fournis par ces outils vont permettre aux concepteurs d'évaluer et de choisir des méthodes adéquates pour améliorer la fiabilité des circuits intégrés Single Events (SE) are produced by the interaction of charged particles with the transistors of a microelectronic circuit. These perturbations may alter the functioning of the circuit and cause logic faults and errors. As the sensitivity of circuits increases for each technological evolution, specific tools are needed for the design of hardened circuits. This thesis aims at furthering the comprehension of the phenomena and proposes EDA tools to help the analysis of these problems in today's ICs. We have developed methodologies for the characterization of the cells from the standard library and tools for accelerated fault simulation and probabilistic analysis of single events. The results provided by these tools allow the designer to correctly evaluate the sensitivity of his design and select the most adequate methods to improve the reliability of ICs
Книги з теми "Probabilistic fault evaluation":
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Agency, International Atomic Energy. Introduction to Probabilistic Fault Displacement Hazard Analysis in Site Evaluation for Existing Nuclear Installations: IAEA TECDOC No 1987. International Atomic Energy Agency, 2022.
Частини книг з теми "Probabilistic fault evaluation":
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Suyama, Koichi. "Safety integrity evaluation framework for fault-tolerant control logic according to IEC 61508." In Probabilistic Safety Assessment and Management, 1357–62. London: Springer London, 2004. http://dx.doi.org/10.1007/978-0-85729-410-4_219.
Modi, Siddharth, Meka Srinivasa Rao, and T. C. S. M. Gupta. "Evaluating Probabilistic Chances of Overpressure-Induced Mechanical Explosion in Fixed Bed Reactor: A Case Study of Hydrogenation of Heavy Base Oils Using Fault Tree and Bayesian Network." In Advances in Sustainable Development, 41–63. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-4400-9_4.
Bastida, Juan Pablo Martínez, Olena Havrylenko, and Andrey Chukhray. "Information Technologies for Learning Principles of Fault-Tolerant Systems." In Automated Systems in the Aviation and Aerospace Industries, 331–57. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-7709-6.ch012.
In this chapter, the authors present a methodology for developing a model-tracing cognitive tutor. The methodology is based on Bayesian probabilistic networks for generating pedagogical interventions. The presented probabilistic model increases fidelity assessment due to its ability of independently diagnosing the degree of mastery for every knowledge component involved in students' actions; fidelity assessment in education is the ability to represent students' cognitive states as close as possible for analysis and evaluation. The cognitive tutor was developed to promote a self-regulated learning approach with an open learner model. The open learner model let students change the learning flow by changing the assigned tasks. The authors explain in detail the structural construction and employed algorithms for developing a model-tracing cognitive tutor in the domain of fault-tolerant systems. Preliminary results and future work are also discussed to assess effectiveness of the proposed approach and its implication in actual educational programs.
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Omerovic, Aida, Amela Karahasanovic, and Ketil Stølen. "Uncertainty Handling in Weighted Dependency Trees." In Dependability and Computer Engineering, 381–416. IGI Global, 2012. http://dx.doi.org/10.4018/978-1-60960-747-0.ch016.
Weighted dependency trees (WDTs) are used in a multitude of approaches to system analysis, such as fault tree analysis or event tree analysis. In fact, any acyclic graph can be transformed to a WDT. Important decisions are often based on WDT analysis. Common for all WDT-based approaches is the inherent uncertainty due to lack or inaccuracy of the input data. In order to indicate credibility of such WDT analysis, uncertainty handling is essential. There is however, to our knowledge, no comprehensive evaluation of the uncertainty handling approaches in the context of the WDTs. This chapter aims to rectify this. We concentrate on approaches applicable for epistemic uncertainty related to empirical input. The existing and the potentially useful approaches are identified through a systematic literature review. The approaches are then outlined and evaluated at a high-level, before a restricted set undergoes a more detailed evaluation based on a set of pre-defined evaluation criteria. We argue that the epistemic uncertainty is better suited for possibilistic uncertainty representations than the probabilistic ones. The results indicate that precision, expressiveness, predictive accuracy, scalability on real-life systems, and comprehensibility are among the properties which differentiate the approaches. The selection of a preferred approach should depend on the degree of need for certain properties relative to others, given the context. The right trade off is particularly important when the input is based on both expert judgments and measurements. The chapter may serve as a roadmap for examining the uncertainty handling approaches, or as a resource for identifying the adequate one.
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Balakrishna, Poornima, Sherry Smith Borener, Ian Crook, Alan Durston, and Mindy J. Robinson. "Applying Probabilistic Risk Assessment to Safety Risk Analysis in Aviation." In Analyzing Risk through Probabilistic Modeling in Operations Research, 321–43. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-4666-9458-3.ch013.
When making policy, procedural, or technological changes to a complex system that has safety implications, a key question decision makers must answer is: What are the risks to the users of the system that will result from making these changes to the system? This chapter illustrates a method to explore different facets of this question using mathematical modeling and probabilistic risk assessment techniques, with the objective of assessing the safety impact of changes to the National Airspace System that follow from the Federal Aviation Administration's next generation air traffic modernization program. The authors describe the development of an Integrated Safety Assessment Model as a structured approach to evaluating current and emerging risks in National Airspace System operations. This process addresses the previously stated risk question by combining fault tree and event sequence diagram modeling techniques, hazard identification and analysis methods, opinions from subject matter experts, and concepts from business intelligence.
Тези доповідей конференцій з теми "Probabilistic fault evaluation":
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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.
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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Wagner, Marcus, and Hans-Joachim Wunderlich. "Probabilistic sensitization analysis for variation-aware path delay fault test evaluation." In 2017 22nd IEEE European Test Symposium (ETS). IEEE, 2017. http://dx.doi.org/10.1109/ets.2017.7968226.
Khludova, M. "Probabilistic Evaluation of Checkpoint-Based Fault Tolerance in Real-Time Systems." In 2019 International Multi-Conference on Industrial Engineering and Modern Technologies (FarEastCon). IEEE, 2019. http://dx.doi.org/10.1109/fareastcon.2019.8934147.
Morozov, Andrey, Mihai A. Diaconeasa, and Mikael Steurer. "A Hybrid Methodology for Model-Based Probabilistic Resilience Evaluation of Dynamic Systems." In ASME 2020 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/imece2020-23789.
Abstract Advanced classical Probabilistic Risk Assessment (PRA) effectively combines various methods for quantitative risk evaluation, such as event trees, fault trees, and Bayesian networks. PRA methods and tools provide the means for the qualitative reliability evaluation (e.g., cut sets) and the computation of quantitative reliability metrics (e.g., end states probabilities). Modern safety-critical systems from various industrial domains tend toward a high level of autonomy and demand not only reliability but also resilience, the ability to recover from degraded or failed states. The numerical resilience analysis of such dynamic systems requires more flexible methods. These methods shall enable the analysis of the systems with sophisticated software parts and dynamic feedback loops. A suitable candidate is the Dual-graph Error Propagation Model (DEPM) that can capture nontrivial failure scenarios and dynamic fault-tolerance mechanisms. The DEPM exploits the method for the automatic generation of Markov chain models and the application of probabilistic model checking techniques. Moreover, the DEPM enables the analysis of highly-customizable system resilience metrics, e.g., “the probability of system recovery to a particular state after a specified system failure during a defined time interval.” In this paper, we show how DEPM-based resilience analysis can be integrated with the general PRA methodology for resilience evaluations. The proposed methodology is demonstrated on a safety-critical autonomous UAV system.
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Svard, Carl, Mattias Nyberg, Erik Frisk, and Mattias Krysander. "A data-driven and probabilistic approach to residual evaluation for fault diagnosis." In 2011 50th IEEE Conference on Decision and Control and European Control Conference (CDC-ECC 2011). IEEE, 2011. http://dx.doi.org/10.1109/cdc.2011.6160714.
Elhaffar, Abdelsalam M., Nagy I. Elkalashy, Naser G. Tarhuni, Mohamed F. Abdel-Fattah, and Matti Lehtonen. "Evaluation of probabilistic-based selectivity technique for earth fault protection in MV networks." In 2009 IEEE Bucharest PowerTech (POWERTECH). IEEE, 2009. http://dx.doi.org/10.1109/ptc.2009.5282189.
Melissianos, Vasileios E., Dimitrios Vamvatsikos, and Charis J. Gantes. "Probabilistic Assessment of Innovative Mitigating Measures for Buried Steel Pipeline–Fault Crossing." In ASME 2015 Pressure Vessels and Piping Conference. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/pvp2015-45345.
A methodology is presented on assessing the effectiveness of flexible joints in mitigating the consequences of faulting on buried steel pipelines through a comprehensive analysis that incorporates the uncertainty of fault displacement magnitude and the response of the pipeline itself. The proposed methodology is a two-step process. In the first step the probabilistic nature of the fault displacement magnitude is evaluated by applying the Probabilistic Fault Displacement Hazard Analysis, considering also all pertinent uncertainties. The second step is the “transition” from seismological data to the pipeline structural response through the fault displacement components as the adopted vector intensity measure. To mitigate the consequences of faulting on pipelines, flexible joints between pipeline parts are proposed as innovative measure for reducing the deformation of pipeline walls. Thus, the mechanical behavior of continuous pipelines and pipelines with flexible joints is numerically assessed and strains are extracted in order to develop the corresponding strain hazard curves. The latter are a useful engineering tool for pipeline – fault crossing risk assessment and for the effectiveness evaluation of flexible joints as innovative mitigating measures against the consequences of faulting on pipelines.
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Nair, V. S. S., and Jacob A. Abraham. "Probabilistic model for the evaluation of fault-tolerant multiprocessor systems using concurrent error detection." In San Diego '90, 8-13 July, edited by Franklin T. Luk. SPIE, 1990. http://dx.doi.org/10.1117/12.23509.
Demjancukova, Katerina, and Dana Prochazkova. "Probabilistic Seismic Hazard Assessment in Countries With Low Seismicity." In ASME 2014 Pressure Vessels and Piping Conference. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/pvp2014-28937.
The region of the Czech Republic is mostly composed of the Bohemian Massif which is considered as a geological unit with low seismic activity. Nevertheless, all critical objects as the nuclear power plants, big dams etc. are built as aseismic structures. The nuclear installations have to satisfy the IAEA safety standards and requirements. One of important phenomena that have to be involved in the PSHA process is the diffuse seismicity. In 2010 International Atomic Energy Agency issued a specific safety guide SSG-9 Seismic Hazards in Site Evaluation for Nuclear Installations. The key chapters are focused on general recommendations, necessary information and investigations (database), construction of a regional seismotectonic model, evaluation of the ground motion hazard, probabilistic seismic hazards analysis (PSHA), deterministic seismic hazards analysis, potential for fault displacement at the site, design basis ground motion, fault displacement and other hazards, evaluation of seismic hazards for nuclear installations other than NPPs. In the paper a numerical example of seismic hazard assessment will be presented with emphasis on problems and particularities related to PSHA in countries with low seismic activity.
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Oh, Kwangseok, and Kyongsu Yi. "A Longitudinal Model Based Probabilistic Fault Diagnosis Algorithm of Autonomous Vehicles Using Sliding Mode Observer." In ASME 2017 Conference on Information Storage and Processing Systems collocated with the ASME 2017 International Technical Conference and Exhibition on Packaging and Integration of Electronic and Photonic Microsystems. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/isps2017-5467.
This paper describes a longitudinal model based probabilistic fault diagnosis algorithm of autonomous vehicles using sliding mode observer. Autonomous vehicles use various sensors such as radar, lidar, and camera to obtain environment information. And internal sensors such as wheel speed, acceleration, and steering angle sensors have been used in vehicle to measure vehicle dynamic states. Based on the measured environment and vehicle states information, autonomous vehicle decides how to drive and control steering, throttle, and brake. Therefore, fault diagnosis of sensors used in autonomous vehicles is the most important for safe driving. In order to diagnosis longitudinal acceleration sensor fault of autonomous vehicle, longitudinal kinematic model has been used. The relative acceleration has been reconstructed using sliding mode observer based on environment information such as relative displacement and velocity between preceding vehicle and subject vehicle. The reconstructed relative acceleration has been used to compute longitudinal acceleration probabilistically based on analyzed longitudinal vehicle’s acceleration. The computed acceleration has been compared with measured acceleration for fault diagnosis of the acceleration sensor. The probabilistic fault diagnosis algorithm has been proposed and evaluated using actual data with arbitrary fault signal. The evaluation results of the proposed fault diagnosis algorithm show the reasonable fault diagnosis performance.
