F
Faisal Khan
Researcher at Memorial University of Newfoundland
Publications - 785
Citations - 28657
Faisal Khan is an academic researcher from Memorial University of Newfoundland. The author has contributed to research in topics: Risk assessment & Risk analysis. The author has an hindex of 70, co-authored 705 publications receiving 21281 citations. Previous affiliations of Faisal Khan include Royal Hobart Hospital & Australian Maritime College.
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Reliability assessment of marine floating structures using Bayesian network
TL;DR: A novel methodology to conduct reliability analysis of moored floating structures using Bayesian network (BN) is proposed and can be employed to mitigate associated risk with marine structures brought about by stochastic hydrodynamic loads.
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Understanding industrial safety: Comparing Fault tree, Bayesian network, and FRAM approaches
TL;DR: In this article, three approaches to safety are examined: fault trees (FT), Bayesian networks (BN), and the Functional Resonance Analysis Method (FRAM), and a case study of a propane feed control system is used to apply these methods.
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Review and analysis of supervised machine learning algorithms for hazardous events in drilling operations
TL;DR: The bibliometric analysis attempts to answer pertinent questions related to progress in the use of supervised machine learning for hazardous events due to drilling fluid density/mud weight and indicates artificial neural network as the most popular algorithm among researchers.
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Development of a Monograph for Human Error Likelihood Assessment in Marine Operations
TL;DR: In this article, the authors developed a monograph for assessing the likelihood of human error in marine operations that can be applied for instant decision making, which can serve as a helpful tool to reduce the potential of accident occurrence.
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A risk-based shutdown inspection and maintenance interval estimation considering human error
TL;DR: In this article, a risk-based methodology to estimate shutdown inspection and maintenance interval by integrating human errors with degradation modeling of a processing unit is presented, which is the extension of the previously published work to determine the shutdown interval considering the system's desired availability.