Pub Date : 2023-08-28DOI: 10.1177/1748006x231193476
Gopalakrishnan Suresh, V. R. Renjith, Anchalassery Balakrishnan Bhasi
A very reliable control system for offering a safety layer in emergency scenarios is an emergency shutdown system, or ESD system. It aids in preventing crises from having disastrous effects on the economy, the environment or business operations. In any plant, emergency shutdown systems reduce the risk of harm to the working population, the environment or equipment damage by guarding against leaks, hydrocarbon escapes, fire outbreaks and explosions. The ESD system halts process activity in an emergency, ensures that the hazard is isolated and does not worsen. It is essential that there is a clearly defined emergency procedure in place when a facility needs to be shut down in an emergency. When preparing an emergency procedure for use in an emergency, it is crucial to prioritise the operations. The factory uses an Interval-valued Intuitionistic Fuzzy Analytical Hierarchy Process (IVIFAHP) to prioritise activities in emergency situations. It is possible to design emergency shutdown systems using the aforementioned order of operations. It is possible to properly set up safety measures using the multi-criteria decision-making approach (MCDM). The Analytical Hierarchy Process (AHP) compares variables without taking measurement scales or units into consideration, which is a distinct benefit. The AHP’s handling of ambiguity, uncertainty and imprecise data is improved by the IVIFAHP.
{"title":"Prioritisation of operations during emergency shutdown of a crude distillation unit by interval-valued intuitionistic fuzzy analytic hierarchy process","authors":"Gopalakrishnan Suresh, V. R. Renjith, Anchalassery Balakrishnan Bhasi","doi":"10.1177/1748006x231193476","DOIUrl":"https://doi.org/10.1177/1748006x231193476","url":null,"abstract":"A very reliable control system for offering a safety layer in emergency scenarios is an emergency shutdown system, or ESD system. It aids in preventing crises from having disastrous effects on the economy, the environment or business operations. In any plant, emergency shutdown systems reduce the risk of harm to the working population, the environment or equipment damage by guarding against leaks, hydrocarbon escapes, fire outbreaks and explosions. The ESD system halts process activity in an emergency, ensures that the hazard is isolated and does not worsen. It is essential that there is a clearly defined emergency procedure in place when a facility needs to be shut down in an emergency. When preparing an emergency procedure for use in an emergency, it is crucial to prioritise the operations. The factory uses an Interval-valued Intuitionistic Fuzzy Analytical Hierarchy Process (IVIFAHP) to prioritise activities in emergency situations. It is possible to design emergency shutdown systems using the aforementioned order of operations. It is possible to properly set up safety measures using the multi-criteria decision-making approach (MCDM). The Analytical Hierarchy Process (AHP) compares variables without taking measurement scales or units into consideration, which is a distinct benefit. The AHP’s handling of ambiguity, uncertainty and imprecise data is improved by the IVIFAHP.","PeriodicalId":51266,"journal":{"name":"Proceedings of the Institution of Mechanical Engineers Part O-Journal of Risk and Reliability","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2023-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76313382","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-04DOI: 10.1177/1748006x231188075
A. Kohansal, Carlos J. Pérez-González, Arturo J. Fernández
This paper studies the statistical estimation of the stress-strength reliability of multi-component systems under the progressive first failure censoring samples, where the lifetime distribution of each component follows the modified Kumaraswamy distribution. Both the point and interval estimations of the parameters in the reliability function are considered. To this aim, some estimations such as maximum likelihood estimation (MLE), asymptotic confidence intervals, uniformly minimum variance unbiased estimation (UMVUE), approximate Bayes estimation, and highest posterior density (HPD) intervals are obtained. By employing the Monte Carlo simulation, comparison of the performance between different estimates is provided. The paper then analyzes a case study for illustration of the proposed method.
{"title":"Inference on the stress-strength reliability of multi-component systems based on progressive first failure censored samples","authors":"A. Kohansal, Carlos J. Pérez-González, Arturo J. Fernández","doi":"10.1177/1748006x231188075","DOIUrl":"https://doi.org/10.1177/1748006x231188075","url":null,"abstract":"This paper studies the statistical estimation of the stress-strength reliability of multi-component systems under the progressive first failure censoring samples, where the lifetime distribution of each component follows the modified Kumaraswamy distribution. Both the point and interval estimations of the parameters in the reliability function are considered. To this aim, some estimations such as maximum likelihood estimation (MLE), asymptotic confidence intervals, uniformly minimum variance unbiased estimation (UMVUE), approximate Bayes estimation, and highest posterior density (HPD) intervals are obtained. By employing the Monte Carlo simulation, comparison of the performance between different estimates is provided. The paper then analyzes a case study for illustration of the proposed method.","PeriodicalId":51266,"journal":{"name":"Proceedings of the Institution of Mechanical Engineers Part O-Journal of Risk and Reliability","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2023-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79059223","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-03DOI: 10.1177/1748006x231187448
Vaibhav Gaur, O. Yadav, G. Soni, A. Rathore, E. Khan
Critical infrastructures form the backbone of any nation, and the failures of these infrastructures could lead to economic disruptions and fatal consequences. Assessing the vulnerability of such critical infrastructures has now evolved into a necessary and imperative task for researchers around the world. Water distribution networks form an essential class of critical infrastructures of modern society. The uninterrupted functioning of such networks is crucial for ensuring a nation’s economic development and welfare of society. The objective of this article is to evaluate the vulnerability of nodes for Water Distribution Networks considering cascading failures. The system dynamics of a water network are included in the simulation process to estimate the impacts of cascading failures. Vulnerability metrics based on loss of topological connectivity and supply capability are formulated in this article. The applicability and significance of the proposed methodology are demonstrated using a sample network case study. Identification of different vulnerability zones is done by employing k-means clustering to perform a methodical comparison between the proposed and existing metrics of vulnerability assessment.
{"title":"Vulnerability assessment of critical infrastructures for cascading failures: An application to water distribution networks","authors":"Vaibhav Gaur, O. Yadav, G. Soni, A. Rathore, E. Khan","doi":"10.1177/1748006x231187448","DOIUrl":"https://doi.org/10.1177/1748006x231187448","url":null,"abstract":"Critical infrastructures form the backbone of any nation, and the failures of these infrastructures could lead to economic disruptions and fatal consequences. Assessing the vulnerability of such critical infrastructures has now evolved into a necessary and imperative task for researchers around the world. Water distribution networks form an essential class of critical infrastructures of modern society. The uninterrupted functioning of such networks is crucial for ensuring a nation’s economic development and welfare of society. The objective of this article is to evaluate the vulnerability of nodes for Water Distribution Networks considering cascading failures. The system dynamics of a water network are included in the simulation process to estimate the impacts of cascading failures. Vulnerability metrics based on loss of topological connectivity and supply capability are formulated in this article. The applicability and significance of the proposed methodology are demonstrated using a sample network case study. Identification of different vulnerability zones is done by employing k-means clustering to perform a methodical comparison between the proposed and existing metrics of vulnerability assessment.","PeriodicalId":51266,"journal":{"name":"Proceedings of the Institution of Mechanical Engineers Part O-Journal of Risk and Reliability","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2023-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88892544","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-03DOI: 10.1177/1748006x231184517
L. Pang, Tingting Mao, Jiaojiao Cao, Zikun Chen, Siheng Sun
A risk assessment method of dust explosion applicable to wood product processing enterprises was studied to effectively control the occurrence of dust explosion accidents. First, considering the explosive attributes of dust and the technical attributes of wood product processing, a risk assessment index system for dust explosion was constructed from the human, machine, environment, and management perspectives. It comprises 3 first-level indices (namely: accident possibility, accident severity, and safety management system), 6 second-level indices, and 28 third-level indices. The weight of each index was calculated using the structural entropy weight method (SEWM). Second, combined with an association degree matrix, a theoretical model for dust explosion risk assessment was established using a three-dimensional matrix method. Finally, an empirical analysis was conducted on a wood product processing plant using the established model. It was concluded that the overall risk level of the plant was level IV, indicating a relatively high risk. This conclusion is consistent with the actual operation status of the plant, showing that the model has a certain degree of applicability and effectiveness.
{"title":"Risk assessment-based optimal safety measure for dust explosion in wooden products processing enterprises","authors":"L. Pang, Tingting Mao, Jiaojiao Cao, Zikun Chen, Siheng Sun","doi":"10.1177/1748006x231184517","DOIUrl":"https://doi.org/10.1177/1748006x231184517","url":null,"abstract":"A risk assessment method of dust explosion applicable to wood product processing enterprises was studied to effectively control the occurrence of dust explosion accidents. First, considering the explosive attributes of dust and the technical attributes of wood product processing, a risk assessment index system for dust explosion was constructed from the human, machine, environment, and management perspectives. It comprises 3 first-level indices (namely: accident possibility, accident severity, and safety management system), 6 second-level indices, and 28 third-level indices. The weight of each index was calculated using the structural entropy weight method (SEWM). Second, combined with an association degree matrix, a theoretical model for dust explosion risk assessment was established using a three-dimensional matrix method. Finally, an empirical analysis was conducted on a wood product processing plant using the established model. It was concluded that the overall risk level of the plant was level IV, indicating a relatively high risk. This conclusion is consistent with the actual operation status of the plant, showing that the model has a certain degree of applicability and effectiveness.","PeriodicalId":51266,"journal":{"name":"Proceedings of the Institution of Mechanical Engineers Part O-Journal of Risk and Reliability","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2023-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76196220","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-01DOI: 10.1177/1748006x231184289
L. Zhuang, Zhong Lu, Haijing Song, Xihui Liang
Safety analysis is an important task in both the development and certification of civil aircraft. The traditional safety analysis is significantly dependent on the skills and experiences of analysts. A model-based safety analysis approach is proposed for airborne systems based on the model built with Simulink. This study builds Simulink models of typical failure modes as well as the fault injection methods. The responses of system performances are monitored by traversing all failure combinations based on a state space reduction method. The system will be in an unsafe condition when the responses exceed their thresholds. The minimal cut sets of the system are obtained automatically by recording the failure combinations leading to the unsafe condition. Finally, a lateral-directional flight control system is taken as a practical example to illustrate the application and effectiveness of our proposed method. The result shows that our method has higher accuracy and the causes of the unsafe conditions can be determined by the automatic generation of the minimal cut sets. Additionally, the cumbersome work of building a traditional safety analysis model such as the fault tree, the Markov model, or the dependence diagram can be avoided.
{"title":"A model-based safety analysis approach for airborne systems using state traversals","authors":"L. Zhuang, Zhong Lu, Haijing Song, Xihui Liang","doi":"10.1177/1748006x231184289","DOIUrl":"https://doi.org/10.1177/1748006x231184289","url":null,"abstract":"Safety analysis is an important task in both the development and certification of civil aircraft. The traditional safety analysis is significantly dependent on the skills and experiences of analysts. A model-based safety analysis approach is proposed for airborne systems based on the model built with Simulink. This study builds Simulink models of typical failure modes as well as the fault injection methods. The responses of system performances are monitored by traversing all failure combinations based on a state space reduction method. The system will be in an unsafe condition when the responses exceed their thresholds. The minimal cut sets of the system are obtained automatically by recording the failure combinations leading to the unsafe condition. Finally, a lateral-directional flight control system is taken as a practical example to illustrate the application and effectiveness of our proposed method. The result shows that our method has higher accuracy and the causes of the unsafe conditions can be determined by the automatic generation of the minimal cut sets. Additionally, the cumbersome work of building a traditional safety analysis model such as the fault tree, the Markov model, or the dependence diagram can be avoided.","PeriodicalId":51266,"journal":{"name":"Proceedings of the Institution of Mechanical Engineers Part O-Journal of Risk and Reliability","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75206278","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-01DOI: 10.1177/1748006x231184656
Tao Yang, Xin Zhang, Jiaxu Wang, Yu Jin, Zhiyuan Gong, Lei Wang
In this study, an adaptive kernel dictionary learning method for intelligent fault diagnosis of bearings is proposed. Kernel KSVD (KKSVD) is an excellent dictionary learning method with the capacity to handle nonlinear signals. However, the choice of kernel parameters and sparse level is a key issue, since these parameters respectively determine the form of the high-dimensional kernel space and the capability of KKSVD to learn appropriate atomic information for representing the samples. As a result, it is difficult to achieve the maximum performance of KKSVD by pre-specifying the values of the parameters. To address this issue, an advanced meta-heuristic algorithm – that is, the grey wolf optimizer (GWO) is introduced into the KKSVD. Specifically, an objective function is first designed, in which the parameters to be optimized are involved in the learning process of KKSVD for the bearing train set and then applied to the testing of the bearing validation set to get the classification results. The classification accuracy is fed back to the GWO algorithm which will update the parameters iteratively and output the optimal parameters. Two case studies respectively corresponding to two common situations in bearing fault diagnosis – that is, strong noisy samples and unbalanced samples, are carried out. The analysis results demonstrate the effectiveness of the proposed method for adaptively obtaining the optimal parameters and improving the performance of KKSVD. Furthermore, the proposed method outperforms several state-of-art dictionary methods in terms of diagnosis accuracy and robustness.
{"title":"An adaptive kernel dictionary learning method based on grey wolf optimizer for bearing intelligent fault diagnosis","authors":"Tao Yang, Xin Zhang, Jiaxu Wang, Yu Jin, Zhiyuan Gong, Lei Wang","doi":"10.1177/1748006x231184656","DOIUrl":"https://doi.org/10.1177/1748006x231184656","url":null,"abstract":"In this study, an adaptive kernel dictionary learning method for intelligent fault diagnosis of bearings is proposed. Kernel KSVD (KKSVD) is an excellent dictionary learning method with the capacity to handle nonlinear signals. However, the choice of kernel parameters and sparse level is a key issue, since these parameters respectively determine the form of the high-dimensional kernel space and the capability of KKSVD to learn appropriate atomic information for representing the samples. As a result, it is difficult to achieve the maximum performance of KKSVD by pre-specifying the values of the parameters. To address this issue, an advanced meta-heuristic algorithm – that is, the grey wolf optimizer (GWO) is introduced into the KKSVD. Specifically, an objective function is first designed, in which the parameters to be optimized are involved in the learning process of KKSVD for the bearing train set and then applied to the testing of the bearing validation set to get the classification results. The classification accuracy is fed back to the GWO algorithm which will update the parameters iteratively and output the optimal parameters. Two case studies respectively corresponding to two common situations in bearing fault diagnosis – that is, strong noisy samples and unbalanced samples, are carried out. The analysis results demonstrate the effectiveness of the proposed method for adaptively obtaining the optimal parameters and improving the performance of KKSVD. Furthermore, the proposed method outperforms several state-of-art dictionary methods in terms of diagnosis accuracy and robustness.","PeriodicalId":51266,"journal":{"name":"Proceedings of the Institution of Mechanical Engineers Part O-Journal of Risk and Reliability","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75095397","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-01DOI: 10.1177/1748006x231183196
Liu Tianyu, Pan Zhengqiang, Song Guopeng
Reliability block diagram (RBD) is an effective tool for modeling and evaluating system reliability. During operation, a system’s reliability may decrease significantly due to the failure of certain critical nodes and thus should be reconfigured. This paper presents a framework for system reliability evaluation and dynamic optimization based on RBD, designed from the perspective of system users. First, we improve the classic RBD model with a new encoding scheme and develop an accurate RBD computation algorithm that is easily recognized by computers and highly efficient. Second, we create an optimization algorithm based on Tabu Search to reconfigure the system after node failure, striking a balance between system reliability recovery and RBD variation amplitude. Finally, we provide some numerical examples and a computational experiment based on a practical instance from a navy fleet to demonstrate the correctness and effectiveness of our proposed methods.
{"title":"System reliability evaluation and dynamic optimization based on an improved reliability block diagram","authors":"Liu Tianyu, Pan Zhengqiang, Song Guopeng","doi":"10.1177/1748006x231183196","DOIUrl":"https://doi.org/10.1177/1748006x231183196","url":null,"abstract":"Reliability block diagram (RBD) is an effective tool for modeling and evaluating system reliability. During operation, a system’s reliability may decrease significantly due to the failure of certain critical nodes and thus should be reconfigured. This paper presents a framework for system reliability evaluation and dynamic optimization based on RBD, designed from the perspective of system users. First, we improve the classic RBD model with a new encoding scheme and develop an accurate RBD computation algorithm that is easily recognized by computers and highly efficient. Second, we create an optimization algorithm based on Tabu Search to reconfigure the system after node failure, striking a balance between system reliability recovery and RBD variation amplitude. Finally, we provide some numerical examples and a computational experiment based on a practical instance from a navy fleet to demonstrate the correctness and effectiveness of our proposed methods.","PeriodicalId":51266,"journal":{"name":"Proceedings of the Institution of Mechanical Engineers Part O-Journal of Risk and Reliability","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87865324","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-30DOI: 10.1177/1748006x231182455
E. Zio
This work is positioned within the new field of quantum probability theory and its application to the reliability analysis of wireless telecommunication networks. Specifically, we present the development of a Quantum Bayesian Network (QBN) for calculating the reliability of a 5G wireless telecommunication network. The qualitative comparison with a classical Bayesian Network model allows highlighting the role of interferences in the calculation of the reliability of a complex system such as a wireless telecommunication network.
{"title":"Quantum reliability analysis of a wireless telecommunication network","authors":"E. Zio","doi":"10.1177/1748006x231182455","DOIUrl":"https://doi.org/10.1177/1748006x231182455","url":null,"abstract":"This work is positioned within the new field of quantum probability theory and its application to the reliability analysis of wireless telecommunication networks. Specifically, we present the development of a Quantum Bayesian Network (QBN) for calculating the reliability of a 5G wireless telecommunication network. The qualitative comparison with a classical Bayesian Network model allows highlighting the role of interferences in the calculation of the reliability of a complex system such as a wireless telecommunication network.","PeriodicalId":51266,"journal":{"name":"Proceedings of the Institution of Mechanical Engineers Part O-Journal of Risk and Reliability","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2023-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72965273","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-23DOI: 10.1177/1748006x231182223
Hongseok Kim, Dooyoul Lee
Using a dynamic Bayesian network (DBN) to estimate the failure risk of a component or system that deteriorates with time has several advantages. A DBN discretizes the probability distribution of variables and thereby increases the efficiency of computing resources and reduces computation time. However, it is important to devise an optimal discretization scheme because the size of the model grows exponentially as the number of discretized intervals increases. In this paper, we propose an optimal discretization scheme for a DBN used to model the time-varying deterioration of a turbine blade component. The results of estimating the reliability indices with the DBN were verified by comparing them with the results of a Monte Carlo simulation. In addition, compared with a log-transformed discretization method, our DBN discretization method shows a significantly increased computation speed.
{"title":"An efficient discretization scheme for a dynamic Bayesian network in structural reliability analysis","authors":"Hongseok Kim, Dooyoul Lee","doi":"10.1177/1748006x231182223","DOIUrl":"https://doi.org/10.1177/1748006x231182223","url":null,"abstract":"Using a dynamic Bayesian network (DBN) to estimate the failure risk of a component or system that deteriorates with time has several advantages. A DBN discretizes the probability distribution of variables and thereby increases the efficiency of computing resources and reduces computation time. However, it is important to devise an optimal discretization scheme because the size of the model grows exponentially as the number of discretized intervals increases. In this paper, we propose an optimal discretization scheme for a DBN used to model the time-varying deterioration of a turbine blade component. The results of estimating the reliability indices with the DBN were verified by comparing them with the results of a Monte Carlo simulation. In addition, compared with a log-transformed discretization method, our DBN discretization method shows a significantly increased computation speed.","PeriodicalId":51266,"journal":{"name":"Proceedings of the Institution of Mechanical Engineers Part O-Journal of Risk and Reliability","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2023-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77724078","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-02DOI: 10.1177/1748006x231174666
Lingjie Wang, Yuqi Chen
The reliability assessment of structures with multiple failure modes and small failure probability is challenging due to the time-consuming simulations required. Active learning Kriging methods for structural reliability with multiple failure modes have shown high computational efficiency and accuracy. However, selecting the appropriate sample and its failure mode to update the Kriging models remains a key problem. In this paper, we propose a new learning function and stopping criterion to further improve the efficiency of structural system reliability analysis. Firstly, we propose a new learning function that combines the expected improvement function and the U learning function. This function selects the most suitable samples, balancing the degree of expected improvement of samples to the limit state surface and the degree of misclassification probability of samples. Secondly, we propose a new stopping criterion that considers both the accurate construction of limit state surfaces and the probability of accurately predicting the signs of samples. This criterion avoids premature or late termination of the active learning process. Thirdly, the sequential MCS simulation method is employed in the active learning process to efficiently evaluate small failure probability problems. By analyzing four examples, we verify the accuracy and efficiency of the proposed structural reliability analysis method.
{"title":"An improved active Kriging method for reliability analysis combining expected improvement and U learning functions","authors":"Lingjie Wang, Yuqi Chen","doi":"10.1177/1748006x231174666","DOIUrl":"https://doi.org/10.1177/1748006x231174666","url":null,"abstract":"The reliability assessment of structures with multiple failure modes and small failure probability is challenging due to the time-consuming simulations required. Active learning Kriging methods for structural reliability with multiple failure modes have shown high computational efficiency and accuracy. However, selecting the appropriate sample and its failure mode to update the Kriging models remains a key problem. In this paper, we propose a new learning function and stopping criterion to further improve the efficiency of structural system reliability analysis. Firstly, we propose a new learning function that combines the expected improvement function and the U learning function. This function selects the most suitable samples, balancing the degree of expected improvement of samples to the limit state surface and the degree of misclassification probability of samples. Secondly, we propose a new stopping criterion that considers both the accurate construction of limit state surfaces and the probability of accurately predicting the signs of samples. This criterion avoids premature or late termination of the active learning process. Thirdly, the sequential MCS simulation method is employed in the active learning process to efficiently evaluate small failure probability problems. By analyzing four examples, we verify the accuracy and efficiency of the proposed structural reliability analysis method.","PeriodicalId":51266,"journal":{"name":"Proceedings of the Institution of Mechanical Engineers Part O-Journal of Risk and Reliability","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2023-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87527919","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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