The Experts below are selected from a list of 52557 Experts worldwide ranked by ideXlab platform
Pei Liu - One of the best experts on this subject based on the ideXlab platform.
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quantitative evaluation of human reliability based on fuzzy clonal selection
IEEE Transactions on Reliability, 2011Co-Authors: Ansi Wang, Yi Luo, Pei LiuAbstract:Human reliability analysis (HRA) is necessary for System Safety Assessment as well as equipment reliability analysis. The cognitive reliability and error analysis method (CREAM) as a representative HRA method provides nine common performance conditions (CPCs) to represent the contextual conditions under which a given action is performed. With a scarcity of empirical data, a high uncertainty in analyzing results is produced by subjective judgment. To obtain more objective and effective HRA results, this paper presents an optimized quantification method to evaluate the human error probability (HEP) according to CREAM, and its linguistic variables. The starting point for the quantification is an introduced fuzzy version of CREAM. However, many fuzzy rules are redundant, and occupy extensive computing time. The clonal selection algorithm combined with the fuzzy model is proposed to optimize the rule set. The evaluation method using the fuzzy-clonal selection algorithm is aimed to support possible applications for prospective and retrospective studies in the domain of Safety Assessment of power Systems. The simulations are carried out on four presumed contexts, and a practical power System. The conclusions illuminate the feasibility of the proposed quantitative method.
Brendan P Williams - One of the best experts on this subject based on the ideXlab platform.
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adoption of a bayesian belief network for the System Safety Assessment of remotely piloted aircraft Systems
Safety Science, 2019Co-Authors: Achim Washington, Reece A Clothier, Natasha A Neogi, Jose Silva, Kelly J Hayhurst, Brendan P WilliamsAbstract:Abstract There can be significant uncertainty as to the Safety of novel or complex aviation Systems, such as Remotely Piloted Aircraft Systems (RPAS). Current aviation Safety Assessment and compliance processes do not adequately account for uncertainty. The aim of this research is to support more objective, transparent, Systematic and consistent regulatory outcomes in relation to the Safety Assessment of such Systems. The objective of this work is to provide a Systematic means of accounting for the various uncertainties inherent to any System Safety Assessment (SSA) process. The paper first defines the System Safety compliance process and its modification to account for uncertainty. The SSA process, its various outputs, and associated uncertainties are defined and then applied to a generic RPAS. A Bayesian Belief Network (BBN) is adopted that facilitates a more comprehensive treatment of the uncertainty in each of the outputs of a typical SSA process. A case study of a generic RPAS is used to illustrate the features of the new approach. The adoption of the Proposed SSA approach would allow for the high uncertainty associated with the Safety Assessment of novel or complex aviation Systems, such as RPAS, to be taken into consideration. Such an approach would enable the risk-based regulation of the sector.
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a bayesian approach to System Safety Assessment and compliance Assessment for unmanned aircraft Systems
Journal of Air Transport Management, 2017Co-Authors: Achim Washington, Reece A Clothier, Brendan P WilliamsAbstract:This paper presents a new approach to showing compliance to System Safety requirements for aviation Systems. The aim is to improve the objectivity, transparency, and rationality of compliance findings in those cases where there is uncertainty in the Assessments of the System. A Bayesian approach is adopted that facilitates a more comprehensive treatment of the uncertainties inherent to all System Safety Assessments. The Assessment and compliance framework is reformulated as a problem of decision making under uncertainty, and a normative decision approach is used to illustrate the approach. A case study System Safety Assessment of a civil unmanned aircraft System is used to exemplify the proposed approach. The proposed approach could be readily applied to any regulatory compliance process and would represent a significant change to, and advancement over, current aviation Safety regulatory practice. This paper is the first to describe the application of Bayesian techniques to the field of aviation System Safety analysis. The adoption of the proposed compliance approach would bring aviation System Safety practitioners in line with more contemporary (and well established) approaches adopted in the nuclear power and space launch industries.
Ansi Wang - One of the best experts on this subject based on the ideXlab platform.
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quantitative evaluation of human reliability based on fuzzy clonal selection
IEEE Transactions on Reliability, 2011Co-Authors: Ansi Wang, Yi Luo, Pei LiuAbstract:Human reliability analysis (HRA) is necessary for System Safety Assessment as well as equipment reliability analysis. The cognitive reliability and error analysis method (CREAM) as a representative HRA method provides nine common performance conditions (CPCs) to represent the contextual conditions under which a given action is performed. With a scarcity of empirical data, a high uncertainty in analyzing results is produced by subjective judgment. To obtain more objective and effective HRA results, this paper presents an optimized quantification method to evaluate the human error probability (HEP) according to CREAM, and its linguistic variables. The starting point for the quantification is an introduced fuzzy version of CREAM. However, many fuzzy rules are redundant, and occupy extensive computing time. The clonal selection algorithm combined with the fuzzy model is proposed to optimize the rule set. The evaluation method using the fuzzy-clonal selection algorithm is aimed to support possible applications for prospective and retrospective studies in the domain of Safety Assessment of power Systems. The simulations are carried out on four presumed contexts, and a practical power System. The conclusions illuminate the feasibility of the proposed quantitative method.
Achim Washington - One of the best experts on this subject based on the ideXlab platform.
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adoption of a bayesian belief network for the System Safety Assessment of remotely piloted aircraft Systems
Safety Science, 2019Co-Authors: Achim Washington, Reece A Clothier, Natasha A Neogi, Jose Silva, Kelly J Hayhurst, Brendan P WilliamsAbstract:Abstract There can be significant uncertainty as to the Safety of novel or complex aviation Systems, such as Remotely Piloted Aircraft Systems (RPAS). Current aviation Safety Assessment and compliance processes do not adequately account for uncertainty. The aim of this research is to support more objective, transparent, Systematic and consistent regulatory outcomes in relation to the Safety Assessment of such Systems. The objective of this work is to provide a Systematic means of accounting for the various uncertainties inherent to any System Safety Assessment (SSA) process. The paper first defines the System Safety compliance process and its modification to account for uncertainty. The SSA process, its various outputs, and associated uncertainties are defined and then applied to a generic RPAS. A Bayesian Belief Network (BBN) is adopted that facilitates a more comprehensive treatment of the uncertainty in each of the outputs of a typical SSA process. A case study of a generic RPAS is used to illustrate the features of the new approach. The adoption of the Proposed SSA approach would allow for the high uncertainty associated with the Safety Assessment of novel or complex aviation Systems, such as RPAS, to be taken into consideration. Such an approach would enable the risk-based regulation of the sector.
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a bayesian approach to System Safety Assessment and compliance Assessment for unmanned aircraft Systems
Journal of Air Transport Management, 2017Co-Authors: Achim Washington, Reece A Clothier, Brendan P WilliamsAbstract:This paper presents a new approach to showing compliance to System Safety requirements for aviation Systems. The aim is to improve the objectivity, transparency, and rationality of compliance findings in those cases where there is uncertainty in the Assessments of the System. A Bayesian approach is adopted that facilitates a more comprehensive treatment of the uncertainties inherent to all System Safety Assessments. The Assessment and compliance framework is reformulated as a problem of decision making under uncertainty, and a normative decision approach is used to illustrate the approach. A case study System Safety Assessment of a civil unmanned aircraft System is used to exemplify the proposed approach. The proposed approach could be readily applied to any regulatory compliance process and would represent a significant change to, and advancement over, current aviation Safety regulatory practice. This paper is the first to describe the application of Bayesian techniques to the field of aviation System Safety analysis. The adoption of the proposed compliance approach would bring aviation System Safety practitioners in line with more contemporary (and well established) approaches adopted in the nuclear power and space launch industries.
Yi Luo - One of the best experts on this subject based on the ideXlab platform.
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quantitative evaluation of human reliability based on fuzzy clonal selection
IEEE Transactions on Reliability, 2011Co-Authors: Ansi Wang, Yi Luo, Pei LiuAbstract:Human reliability analysis (HRA) is necessary for System Safety Assessment as well as equipment reliability analysis. The cognitive reliability and error analysis method (CREAM) as a representative HRA method provides nine common performance conditions (CPCs) to represent the contextual conditions under which a given action is performed. With a scarcity of empirical data, a high uncertainty in analyzing results is produced by subjective judgment. To obtain more objective and effective HRA results, this paper presents an optimized quantification method to evaluate the human error probability (HEP) according to CREAM, and its linguistic variables. The starting point for the quantification is an introduced fuzzy version of CREAM. However, many fuzzy rules are redundant, and occupy extensive computing time. The clonal selection algorithm combined with the fuzzy model is proposed to optimize the rule set. The evaluation method using the fuzzy-clonal selection algorithm is aimed to support possible applications for prospective and retrospective studies in the domain of Safety Assessment of power Systems. The simulations are carried out on four presumed contexts, and a practical power System. The conclusions illuminate the feasibility of the proposed quantitative method.
