The Experts below are selected from a list of 72 Experts worldwide ranked by ideXlab platform
Wei Han - One of the best experts on this subject based on the ideXlab platform.
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analysis of 2 28 keeper chemical industries hazardous chemical explosion accident based on fta and hfacs
International Journal of Environmental Research and Public Health, 2018Co-Authors: Wei Jiang, Wei HanAbstract:On 28 February 2012, a guanidine nitrate explosion occurred at HEBEI KEEPER Chemical Industries Co., Ltd., China, resulting in 25 deaths, with 4 missing individuals and 46 injured. In order to explore the causal relationship hidden behind this accident, Fault Tree analysis (FTA) and the Human Factors Analysis and Classification System (HFACS) were used to systematically analyze the incident. Firstly, FTA was used to analyze the causes of the accident in depth, until all the basic causal events causing the guanidine nitrate explosion were identified, and a Fault Tree Diagram of the guanidine nitrate explosion was drawn. Secondly, for the unsafe acts in the basic causal events, the HFACS model was used to analyze the three levels of factors that lead to unsafe acts, including the preconditions for unsafe acts, unsafe supervision, and organizational influences. Finally, based on the analysis results of FTA and HFACS, a complete logic Diagram of the causes of the accident was obtained. The FTA and HFACS accident analysis methods allowed for the identification of human factors and the accident evolution process in the explosion accident and provide a reference for accident investigation.
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Analysis of “2·28” KEEPER Chemical Industries Hazardous Chemical Explosion Accident Based on FTA and HFACS
MDPI AG, 2018Co-Authors: Wei Jiang, Wei HanAbstract:On 28 February 2012, a guanidine nitrate explosion occurred at HEBEI KEEPER Chemical Industries Co., Ltd., China, resulting in 25 deaths, with 4 missing individuals and 46 injured. In order to explore the causal relationship hidden behind this accident, Fault Tree analysis (FTA) and the Human Factors Analysis and Classification System (HFACS) were used to systematically analyze the incident. Firstly, FTA was used to analyze the causes of the accident in depth, until all the basic causal events causing the guanidine nitrate explosion were identified, and a Fault Tree Diagram of the guanidine nitrate explosion was drawn. Secondly, for the unsafe acts in the basic causal events, the HFACS model was used to analyze the three levels of factors that lead to unsafe acts, including the preconditions for unsafe acts, unsafe supervision, and organizational influences. Finally, based on the analysis results of FTA and HFACS, a complete logic Diagram of the causes of the accident was obtained. The FTA and HFACS accident analysis methods allowed for the identification of human factors and the accident evolution process in the explosion accident and provide a reference for accident investigation
Wei Jiang - One of the best experts on this subject based on the ideXlab platform.
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analysis of 2 28 keeper chemical industries hazardous chemical explosion accident based on fta and hfacs
International Journal of Environmental Research and Public Health, 2018Co-Authors: Wei Jiang, Wei HanAbstract:On 28 February 2012, a guanidine nitrate explosion occurred at HEBEI KEEPER Chemical Industries Co., Ltd., China, resulting in 25 deaths, with 4 missing individuals and 46 injured. In order to explore the causal relationship hidden behind this accident, Fault Tree analysis (FTA) and the Human Factors Analysis and Classification System (HFACS) were used to systematically analyze the incident. Firstly, FTA was used to analyze the causes of the accident in depth, until all the basic causal events causing the guanidine nitrate explosion were identified, and a Fault Tree Diagram of the guanidine nitrate explosion was drawn. Secondly, for the unsafe acts in the basic causal events, the HFACS model was used to analyze the three levels of factors that lead to unsafe acts, including the preconditions for unsafe acts, unsafe supervision, and organizational influences. Finally, based on the analysis results of FTA and HFACS, a complete logic Diagram of the causes of the accident was obtained. The FTA and HFACS accident analysis methods allowed for the identification of human factors and the accident evolution process in the explosion accident and provide a reference for accident investigation.
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Analysis of “2·28” KEEPER Chemical Industries Hazardous Chemical Explosion Accident Based on FTA and HFACS
MDPI AG, 2018Co-Authors: Wei Jiang, Wei HanAbstract:On 28 February 2012, a guanidine nitrate explosion occurred at HEBEI KEEPER Chemical Industries Co., Ltd., China, resulting in 25 deaths, with 4 missing individuals and 46 injured. In order to explore the causal relationship hidden behind this accident, Fault Tree analysis (FTA) and the Human Factors Analysis and Classification System (HFACS) were used to systematically analyze the incident. Firstly, FTA was used to analyze the causes of the accident in depth, until all the basic causal events causing the guanidine nitrate explosion were identified, and a Fault Tree Diagram of the guanidine nitrate explosion was drawn. Secondly, for the unsafe acts in the basic causal events, the HFACS model was used to analyze the three levels of factors that lead to unsafe acts, including the preconditions for unsafe acts, unsafe supervision, and organizational influences. Finally, based on the analysis results of FTA and HFACS, a complete logic Diagram of the causes of the accident was obtained. The FTA and HFACS accident analysis methods allowed for the identification of human factors and the accident evolution process in the explosion accident and provide a reference for accident investigation
J D Andrews - One of the best experts on this subject based on the ideXlab platform.
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choosing a heuristic for the Fault Tree to binary decision Diagram conversion using neural networks
IEEE Transactions on Reliability, 2002Co-Authors: L M Bartlett, J D AndrewsAbstract:Fault-Tree analysis is commonly used for risk assessment of industrial systems. Several computer packages are available to carry out the analysis. Despite its common usage there are associated limitations of the technique in terms of accuracy and efficiency when dealing with large Fault-Tree structures. The most recent approach to aid the analysis of the Fault-Tree Diagram is the BDD (binary decision Diagram). To use the BDD, the Fault-Tree structure needs to be converted into the BDD format. Converting the Fault Tree is relatively straightforward but requires that the basic events of the Tree be ordered. This ordering is critical to the resulting size of the BDD, and ultimately affects the qualitative and quantitative performance and benefits of this technique. Several heuristic approaches were developed to produce an optimal ordering permutation for a specific Tree. These heuristic approaches do not always yield a minimal BDD structure for all Trees. There is no single heuristic that guarantees a minimal BDD for any Fault-Tree structure. This paper looks at a selection approach using a neural network to choose the best heuristic from a set of alternatives that will yield the smallest BDD and promote an efficient analysis. The set of possible selection choices are 6 alternative heuristics, and the prediction capacity produced was a 70% chance of the neural network choosing the best ordering heuristic from the set of 6 for the test set of given Fault Trees.
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an ordering heuristic to develop the binary decision Diagram based on structural importance
Reliability Engineering & System Safety, 2001Co-Authors: L M Bartlett, J D AndrewsAbstract:Abstract Fault Tree analysis is often used to assess risks within industrial systems. The technique is commonly used although there are associated limitations in terms of accuracy and efficiency when dealing with large Fault Tree structures. The most recent approach to aid the analysis of the Fault Tree Diagram is the Binary Decision Diagram (BDD) methodology. To utilise the technique the Fault Tree structure needs to be converted into the BDD format. Converting the Fault Tree requires the basic events of the Tree to be placed in an ordering. The ordering of the basic events is critical to the resulting size of the BDD, and ultimately affects the performance and benefits of this technique. A number of heuristic approaches have been developed to produce an optimal ordering permutation for a specific Tree. These heuristic approaches do not always yield a minimal BDD structure for all Trees. This paper looks at a heuristic that is based on the structural importance measure of each basic event. Comparing the resulting size of the BDD with the smallest generated from a set of six alternative ordering heuristics, this new structural heuristic produced a BDD of smaller or equal dimension on 77% of trials.
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improved efficiency in qualitative Fault Tree analysis
Quality and Reliability Engineering International, 1997Co-Authors: Roslyn M Sinnamon, J D AndrewsAbstract:The Fault Tree Diagram itself is an excellent way of deriving the failure logic for a system and representing it in a form which is ideal for communication to managers, designers, operators, etc. Since the method was first conceived, algorithms to derive the minimal cut sets have worked directly with the Fault Tree Diagram using either bottom-up or top-down approaches. These conventional techniques have several disadvantages when it comes to analysing the Fault Tree. For complex systems an analysis may produce hundreds of thousands of minimal cut sets, the determination of which can be a very time-consuming process. Also, for large Fault Trees it may not be possible to evaluate all minimal cut sets, so methods to identify those event combinations which provide the most significant contributions to the system failure are evoked. Such methods include probabilistic or order culling to reduce the problem to a practical size, but they can also create considerable inaccuracies when it comes to evaluating top event probability parameters. This paper describes how the binary decision Diagram method can be employed to evaluate the minimal cut sets of a Fault Tree efficiently and without the need to use approximations such as order culling.
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improved accuracy in quantitative Fault Tree analysis
Quality and Reliability Engineering International, 1997Co-Authors: Roslyn M Sinnamon, J D AndrewsAbstract:The Fault Tree Diagram defines the causes of the system failure mode or 'top event' in terms of the component failures and human errors, represented by basic events. By providing information which enables the basic event probability to be calculated, the Fault Tree can then be quantified to yield reliability parameters for the system. Fault Tree quantification enables the probability of the top event to be calculated and in addition its failure rate and expected number of occurrences. Importance measures which signify the contribution each basic event makes to system failure can also be determined. Owing to the large number of failure combinations (minimal cut sets) which generally result from a Fault Tree study, it is not possible using conventional techniques to calculate these parameters exactly and approximations are required. The approximations usually rely on the basic events having a small likelihood of occurrence. When this condition is not met, it can result in large inaccuracies. These problems can be overcome by employing the binary decision Diagram (BDD) approach. This method converts the Fault Tree Diagram into a format which encodes Shannon's decomposition and allows the exact failure probability to be determined in a very efficient calculation procedure. This paper describes how the BDD method can be employed in Fault Tree quantification.
L M Bartlett - One of the best experts on this subject based on the ideXlab platform.
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choosing a heuristic for the Fault Tree to binary decision Diagram conversion using neural networks
IEEE Transactions on Reliability, 2002Co-Authors: L M Bartlett, J D AndrewsAbstract:Fault-Tree analysis is commonly used for risk assessment of industrial systems. Several computer packages are available to carry out the analysis. Despite its common usage there are associated limitations of the technique in terms of accuracy and efficiency when dealing with large Fault-Tree structures. The most recent approach to aid the analysis of the Fault-Tree Diagram is the BDD (binary decision Diagram). To use the BDD, the Fault-Tree structure needs to be converted into the BDD format. Converting the Fault Tree is relatively straightforward but requires that the basic events of the Tree be ordered. This ordering is critical to the resulting size of the BDD, and ultimately affects the qualitative and quantitative performance and benefits of this technique. Several heuristic approaches were developed to produce an optimal ordering permutation for a specific Tree. These heuristic approaches do not always yield a minimal BDD structure for all Trees. There is no single heuristic that guarantees a minimal BDD for any Fault-Tree structure. This paper looks at a selection approach using a neural network to choose the best heuristic from a set of alternatives that will yield the smallest BDD and promote an efficient analysis. The set of possible selection choices are 6 alternative heuristics, and the prediction capacity produced was a 70% chance of the neural network choosing the best ordering heuristic from the set of 6 for the test set of given Fault Trees.
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an ordering heuristic to develop the binary decision Diagram based on structural importance
Reliability Engineering & System Safety, 2001Co-Authors: L M Bartlett, J D AndrewsAbstract:Abstract Fault Tree analysis is often used to assess risks within industrial systems. The technique is commonly used although there are associated limitations in terms of accuracy and efficiency when dealing with large Fault Tree structures. The most recent approach to aid the analysis of the Fault Tree Diagram is the Binary Decision Diagram (BDD) methodology. To utilise the technique the Fault Tree structure needs to be converted into the BDD format. Converting the Fault Tree requires the basic events of the Tree to be placed in an ordering. The ordering of the basic events is critical to the resulting size of the BDD, and ultimately affects the performance and benefits of this technique. A number of heuristic approaches have been developed to produce an optimal ordering permutation for a specific Tree. These heuristic approaches do not always yield a minimal BDD structure for all Trees. This paper looks at a heuristic that is based on the structural importance measure of each basic event. Comparing the resulting size of the BDD with the smallest generated from a set of six alternative ordering heuristics, this new structural heuristic produced a BDD of smaller or equal dimension on 77% of trials.
Hiroyuki Sawada - One of the best experts on this subject based on the ideXlab platform.
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method of computer aided Fault Tree analysis for high reliable and safety design
IEEE Transactions on Reliability, 2016Co-Authors: Youji Hiraoka, Tamotsu Murakami, Katsunari Yamamoto, Yoshiyuki Furukawa, Hiroyuki SawadaAbstract:Fault Tree analysis (FTA) is a method of analyzing and visualizing the causes of a Fault using a Fault Tree Diagram (FT Diagram), which has a Tree structure with logical steps. Design engineers developing a new product generally use FTA to analyze many Fault events, calculate their probability, and include redundancy systems in the design process. Furthermore, FTA has been used to analyze problems with products and to prevent the occurrence of problems in the design phase. In particular, it is necessary for design engineers to analyze the events after a failure to determine the root causes of the failure of the redundancy systems. However, it is not easy for design engineers to produce an accurate FT Diagram in the actual design process. We have developed a computer-aided knowledge management system for creating FT Diagrams (FTAid) as part of a collaborative group (The University of Tokyo, National Institute of Advanced Industrial Science and Technology (AIST), and Jatco Ltd.). This system has been verified by the design engineers of Jatco Ltd. in actual product development. We report its effectiveness for predicting mechanical, electrical, and heat transfer failure, the verification of the system, and its validation in an actual design process. We conclude that the system can help design engineers to effectively and efficiently create FT Diagrams in reliability engineering, although some existing ability in FTA and engineering is required. We also describe some outstanding issues regarding the improvement of FTAid, engineering education, and ensuring reliability.
