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.
Papers
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Review of hydrogen safety during storage, transmission, and applications processes
TL;DR: This study comprehensively reviews and analyses safety challenges related to hydrogen, focusing on hydrogen storage, transmission, and application processes, and approaches to quantitative risk assessment are briefly discussed.
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Handling data uncertainties in event tree analysis
TL;DR: This paper explores two approaches to address data uncertainties, namely, fuzzy sets and evidence theory, and compares the results with Monte Carlo simulations, and demonstrates application of these approaches in ETA.
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Handling and updating uncertain information in bow-tie analysis
TL;DR: In this article, the authors proposed a methodology to characterize the uncertainties, aggregate knowledge and update prior knowledge or evidence, if new data become available for the bow-tie analysis, to minimize the overall uncertainty, fusing the knowledge of multiple experts and updating prior knowledge with new evidence.
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Root Cause Diagnosis of Process Fault Using KPCA and Bayesian Network
TL;DR: In this paper, the authors developed a methodology to combine diagnostic information from various fault detection and isolation tools to diagnose the true root cause of an abnormal event in industrial processes using Bayesian belief network.
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A Bibliometric Review and Analysis of Data-Driven Fault Detection and Diagnosis Methods for Process Systems
TL;DR: The focus of the current review is on the data-driven techniques as the authors are now in a digital era and data analytics is getting more emphasis in all areas including process industries.