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Your search keyword '"Kabir, Sohag"' showing total 4 results
Start Over Author "Kabir, Sohag" Topic bayesian network
4 results on '"Kabir, Sohag"'

1. Reliability Assessment of IoT-Enabled Systems Using Fault Trees and Bayesian Networks

Author

Abdulhamid, Alhassan, Kabir, Sohag, Ghafir, Ibrahim, Lei, Ci, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Nanda, Umakanta, editor, Tripathy, Asis Kumar, editor, Sahoo, Jyoti Prakash, editor, Sarkar, Mahasweta, editor, and Li, Kuan-Ching, editor

Published
2024
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3. Dynamic reliability assessment of flare systems by combining fault tree analysis and Bayesian networks.

Author

Kabir, Sohag, Taleb-Berrouane, Mohammed, and Papadopoulos, Yiannis

Subjects
*FAULT trees (Reliability engineering), *BAYESIAN analysis, *SYSTEM failures, *WASTE gases, *FLAMMABLE gases, *RELIABILITY in engineering
Abstract

Flaring is a combustion process commonly used in the oil and gas industry to dispose flammable waste gases. Flare flameout occurs when these gases escape unburnt from the flare tip causing the discharge of flammable and/or toxic vapor clouds. The toxic gases released during this process have the potential to initiate safety hazards and cause serious harm to the ecosystem and human health. Flare flameout could be caused by environmental conditions, equipment failure, and human error. However, to better understand the causes of flare flameout, a rigorous analysis of the behavior of flare systems under failure conditions is required. In this article, we used fault tree analysis (FTA) and the dynamic Bayesian network (DBN) to assess the reliability of flare systems. In this study, we analyzed 40 different combinations of basic events that can cause flare flameout to determine the event with the highest impact on system failure. In the quantitative analysis, we use both constant and time-dependent failure rates of system components. The results show that combining these two approaches allows for robust probabilistic reasoning on flare system reliability, which can help improving the safety and asset integrity of process facilities. The proposed DBN model constitutes a significant step to improve the safety and reliability of flare systems in the oil and gas industry. [ABSTRACT FROM AUTHOR]

Published
2023
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4. A review of applications of fuzzy sets to safety and reliability engineering.

Author

Kabir, Sohag and Papadopoulos, Yiannis

Subjects
*FUZZY sets, *STATISTICAL reliability, *PROBABILITY theory, *BAYESIAN analysis, *PETRI nets
Abstract

Safety and reliability are rigorously assessed during the design of dependable systems. Probabilistic risk assessment (PRA) processes are comprehensive, structured and logical methods widely used for this purpose. PRA approaches include, but not limited to Fault Tree Analysis (FTA), Failure Mode and Effects Analysis (FMEA), and Event Tree Analysis (ETA). In conventional PRA, failure data about components is required for the purposes of quantitative analysis. In practice, it is not always possible to fully obtain this data due to unavailability of primary observations and consequent scarcity of statistical data about the failure of components. To handle such situations, fuzzy set theory has been successfully used in novel PRA approaches for safety and reliability evaluation under conditions of uncertainty. This paper presents a review of fuzzy set theory based methodologies applied to safety and reliability engineering, which include fuzzy FTA, fuzzy FMEA, fuzzy ETA, fuzzy Bayesian networks, fuzzy Markov chains, and fuzzy Petri nets. Firstly, we describe relevant fundamentals of fuzzy set theory and then we review applications of fuzzy set theory to system safety and reliability analysis. The review shows the context in which each technique may be more appropriate and highlights the overall potential usefulness of fuzzy set theory in addressing uncertainty in safety and reliability engineering. [ABSTRACT FROM AUTHOR]

Published
2018
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