Pub Date : 2023-06-01DOI: 10.1177/1748006X221100365
Y.R Melo, Cristiano AV Cavalcante, P. Scarf, R. Lopes
We model a maintenance policy with fixed periodic structure that is a hybrid of periodic inspection and opportunistic replacement. The policy is applicable to geographically remote systems such as offshore wind farms. The policy has three phases. Initially, there is an inspection phase to identify early defects. This is followed by a wear out phase during which corrective replacements are performed. Preventive replacement occurs at the end of this phase. The novelty of the model is an opportunistic phase, which overlaps with the latter part of the corrective phase, when preventive replacement is executed early if an opportunity arises. In this way, we model the reality in which remote systems with high logistics costs and restricted access may benefit from opportunistic visits for maintenance. Using a numerical example, we analyse the behaviour of the decision variables for a range of values of the parameters common to such systems. These parameters relate to: component heterogeneity; restricted access; default (failure to execute a planned action); arrival of opportunities and other standard parameters in a maintenance cost model. Specifically, our results indicate when opportunities can have a significant impact on the cost-rate of the optimum policy, but that leveraging opportunities cannot achieve a very high availability. Generally, we demonstrate that maintenance planning should be flexible when factors beyond the control of the maintainer impact maintenance effectiveness.
{"title":"A hybrid maintenance policy with fixed periodic structure and opportunistic replacement","authors":"Y.R Melo, Cristiano AV Cavalcante, P. Scarf, R. Lopes","doi":"10.1177/1748006X221100365","DOIUrl":"https://doi.org/10.1177/1748006X221100365","url":null,"abstract":"We model a maintenance policy with fixed periodic structure that is a hybrid of periodic inspection and opportunistic replacement. The policy is applicable to geographically remote systems such as offshore wind farms. The policy has three phases. Initially, there is an inspection phase to identify early defects. This is followed by a wear out phase during which corrective replacements are performed. Preventive replacement occurs at the end of this phase. The novelty of the model is an opportunistic phase, which overlaps with the latter part of the corrective phase, when preventive replacement is executed early if an opportunity arises. In this way, we model the reality in which remote systems with high logistics costs and restricted access may benefit from opportunistic visits for maintenance. Using a numerical example, we analyse the behaviour of the decision variables for a range of values of the parameters common to such systems. These parameters relate to: component heterogeneity; restricted access; default (failure to execute a planned action); arrival of opportunities and other standard parameters in a maintenance cost model. Specifically, our results indicate when opportunities can have a significant impact on the cost-rate of the optimum policy, but that leveraging opportunities cannot achieve a very high availability. Generally, we demonstrate that maintenance planning should be flexible when factors beyond the control of the maintainer impact maintenance 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":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75110073","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 : 2023-05-29DOI: 10.1177/1748006x231175708
Cong Peng, W. Shangguan, B. Cai, Bin Chen
Preventive maintenance procedures for systems are designed to improve long-term operating performance and reduce maintenance costs. This paper attempts to seek a trade-off between system reliability and maintenance costs and explores the optimal multi-objective maintenance strategy applicable to different field situations. We first provide an overview of the problem and analyze the impacts of varying different maintenance activities on reliability. Then, a mathematical model of reliability losses and maintenance costs is defined and derived, which will be used to construct a multi-objective maintenance strategy. In this research, the PSO-SA stochastic optimization algorithm is proposed to discover the Pareto frontier to find the optimal preventive maintenance threshold and inspection interval that minimizes reliability loss and maintenance costs. Finally, a case study is performed with railway VOBC as an illustration. The analysis results illustrate that the proposed multi-objective maintenance strategy can satisfy the preferences of various decision-makers. It is suitable for different maintenance needs and can support the development of on-site maintenance strategies.
{"title":"Multi-objective optimal maintenance strategy considering imperfect preventive maintenance: A case study on railway VOBC","authors":"Cong Peng, W. Shangguan, B. Cai, Bin Chen","doi":"10.1177/1748006x231175708","DOIUrl":"https://doi.org/10.1177/1748006x231175708","url":null,"abstract":"Preventive maintenance procedures for systems are designed to improve long-term operating performance and reduce maintenance costs. This paper attempts to seek a trade-off between system reliability and maintenance costs and explores the optimal multi-objective maintenance strategy applicable to different field situations. We first provide an overview of the problem and analyze the impacts of varying different maintenance activities on reliability. Then, a mathematical model of reliability losses and maintenance costs is defined and derived, which will be used to construct a multi-objective maintenance strategy. In this research, the PSO-SA stochastic optimization algorithm is proposed to discover the Pareto frontier to find the optimal preventive maintenance threshold and inspection interval that minimizes reliability loss and maintenance costs. Finally, a case study is performed with railway VOBC as an illustration. The analysis results illustrate that the proposed multi-objective maintenance strategy can satisfy the preferences of various decision-makers. It is suitable for different maintenance needs and can support the development of on-site maintenance strategies.","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":"2023-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80427629","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 : 2023-05-25DOI: 10.1177/1748006x231174960
H. Rasay, Seyed Mohammad Hadian, F. Naderkhani, Fariba Azizi
Condition-based maintenance (CBM) has been emerged as a relatively new trend in maintenance management. Instead of conducting preventive maintenance actions in specified time intervals, the CBM program collects information through condition monitoring, then recommends maintenance actions based on the observed data. On the other hand, Bayesian control charts use the posterior probability of being the system in an unhealthy state as the chart statistic. An attribute Bayesian control chart is employed in this study to monitor a deteriorating system and plan CBM actions based on a continuous-time homogeneous Markov chain. The system consists of three states: healthy, unhealthy, and failure states. A partially observable Markov decision process (POMDP) is developed, which optimally determines the sample size, sampling interval, and warning limit to minimize the long-term expected cost per time unit. Numerical examples and sensitivity analyses are conducted to clarify the performance of the proposed attribute control chart. To the best of the authors’ knowledge, this is the first study of the applications of attribute Bayesian control charts in condition-based maintenance.
{"title":"Optimal condition based maintenance using attribute Bayesian control chart","authors":"H. Rasay, Seyed Mohammad Hadian, F. Naderkhani, Fariba Azizi","doi":"10.1177/1748006x231174960","DOIUrl":"https://doi.org/10.1177/1748006x231174960","url":null,"abstract":"Condition-based maintenance (CBM) has been emerged as a relatively new trend in maintenance management. Instead of conducting preventive maintenance actions in specified time intervals, the CBM program collects information through condition monitoring, then recommends maintenance actions based on the observed data. On the other hand, Bayesian control charts use the posterior probability of being the system in an unhealthy state as the chart statistic. An attribute Bayesian control chart is employed in this study to monitor a deteriorating system and plan CBM actions based on a continuous-time homogeneous Markov chain. The system consists of three states: healthy, unhealthy, and failure states. A partially observable Markov decision process (POMDP) is developed, which optimally determines the sample size, sampling interval, and warning limit to minimize the long-term expected cost per time unit. Numerical examples and sensitivity analyses are conducted to clarify the performance of the proposed attribute control chart. To the best of the authors’ knowledge, this is the first study of the applications of attribute Bayesian control charts in condition-based maintenance.","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":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79815343","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 : 2023-05-25DOI: 10.1177/1748006x231175719
K. Kirytopoulos, Andreas Mourelatos, G. Chatzistelios, Panagiotis Ntzeremes, M. Konstantinidou
Studies reveal that drivers’ behavior is the most significant factor in road accidents worldwide. Regarding tunnels, which are the most critical element of road infrastructure, despite the significant efforts that have been conducted toward the enhancement of drivers’ education all these years, studies illustrate that there are still serious deficiencies need to be tackled. To address this issue, this research endeavor develops a virtual reality tool based on the serious game idea in order to inform and educate potential users about the specific rules and behavioral patterns that should govern their safe driving when passing through tunnels. To do so, the appropriate behavioral patterns are determined using applicable norms and guidelines while the specific educational requirements are identified. Following that, the novel tool for training users is developed. The tool consists of a virtual reality gaming environment based on the notion of serious games that simulates driving through a tunnel from a first-person perspective. Various scenarios are developed within this environment based on the knowledge gaps identified in the literature, with the aim of assessing users’ knowledge as well as educating them when required. The developed tool was tried by more than 50 drivers, professional and non-professional during tool’s launch activities. In particular, drivers who had recently obtained a driver’s license confirmed that such a tool would be especially useful in the context of their training. The ultimate goal of this study is to provide an efficient tool in order to support both practitioners and authorities to significantly improve the safety level of road tunnels by emphasizing on the driving behavior, since this is considered the most crucial component of each tunnel system.
{"title":"A virtual reality instrument to raise drivers’ awareness on safer driving through road tunnels","authors":"K. Kirytopoulos, Andreas Mourelatos, G. Chatzistelios, Panagiotis Ntzeremes, M. Konstantinidou","doi":"10.1177/1748006x231175719","DOIUrl":"https://doi.org/10.1177/1748006x231175719","url":null,"abstract":"Studies reveal that drivers’ behavior is the most significant factor in road accidents worldwide. Regarding tunnels, which are the most critical element of road infrastructure, despite the significant efforts that have been conducted toward the enhancement of drivers’ education all these years, studies illustrate that there are still serious deficiencies need to be tackled. To address this issue, this research endeavor develops a virtual reality tool based on the serious game idea in order to inform and educate potential users about the specific rules and behavioral patterns that should govern their safe driving when passing through tunnels. To do so, the appropriate behavioral patterns are determined using applicable norms and guidelines while the specific educational requirements are identified. Following that, the novel tool for training users is developed. The tool consists of a virtual reality gaming environment based on the notion of serious games that simulates driving through a tunnel from a first-person perspective. Various scenarios are developed within this environment based on the knowledge gaps identified in the literature, with the aim of assessing users’ knowledge as well as educating them when required. The developed tool was tried by more than 50 drivers, professional and non-professional during tool’s launch activities. In particular, drivers who had recently obtained a driver’s license confirmed that such a tool would be especially useful in the context of their training. The ultimate goal of this study is to provide an efficient tool in order to support both practitioners and authorities to significantly improve the safety level of road tunnels by emphasizing on the driving behavior, since this is considered the most crucial component of each tunnel system.","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":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80060156","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 : 2023-05-12DOI: 10.1177/1748006x231173595
Alfian Tan, R. Remenyte-Prescott, M. Valstar, D. Sharkey
Variations in clinical practice are common. However, some variations may cause undesired consequences. Careful consideration of their causes and effects is necessary to assure the quality of healthcare delivery. A modelling approach that could capture these aspects would help to achieve this goal. In this paper, a Newborn Life Support procedure is modelled. This activity is considered prone to error with reduced outcomes for the patient. Hence, it is necessary to understand the nature of the activity and its variations. A Coloured Petri Net (CPN) approach and a simulation technique are used for this purpose. The CPN colours are used to represent the characteristics of babies and to control the flow of tokens representing the resuscitation procedure. Probabilistic modelling aspects include the duration of individual tasks, the choice of treatment and the condition of the baby. The model outputs consist of the percentage of babies with an unsatisfactory outcome, the percentage of babies who need full resuscitation, and the duration of the procedure until a satisfactory condition is achieved. The modelling approach is demonstrated using a number of scenarios on some common NLS variations, relating to the maximum number of ventilation and the probability of errors in the inflation procedure.
{"title":"A modelling approach to studying variations in newborn life support procedure","authors":"Alfian Tan, R. Remenyte-Prescott, M. Valstar, D. Sharkey","doi":"10.1177/1748006x231173595","DOIUrl":"https://doi.org/10.1177/1748006x231173595","url":null,"abstract":"Variations in clinical practice are common. However, some variations may cause undesired consequences. Careful consideration of their causes and effects is necessary to assure the quality of healthcare delivery. A modelling approach that could capture these aspects would help to achieve this goal. In this paper, a Newborn Life Support procedure is modelled. This activity is considered prone to error with reduced outcomes for the patient. Hence, it is necessary to understand the nature of the activity and its variations. A Coloured Petri Net (CPN) approach and a simulation technique are used for this purpose. The CPN colours are used to represent the characteristics of babies and to control the flow of tokens representing the resuscitation procedure. Probabilistic modelling aspects include the duration of individual tasks, the choice of treatment and the condition of the baby. The model outputs consist of the percentage of babies with an unsatisfactory outcome, the percentage of babies who need full resuscitation, and the duration of the procedure until a satisfactory condition is achieved. The modelling approach is demonstrated using a number of scenarios on some common NLS variations, relating to the maximum number of ventilation and the probability of errors in the inflation procedure.","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":"2023-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88601433","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}
In the complex multi-state system (MSS), reliability analysis is an important research content, both for equipment design, manufacturing, operation and maintenance. Universal Generating Function (UGF) is an essential method in reliability analysis, which efficiently obtains system reliability by a fast algebraic procedure. However, when structural relationships between subsystems or components are unclear or without explicit expressions, the UGF method is difficult to use or not applicable at all. Bayesian Network (BN) has a natural advantage in terms of reliability inference for the relationship without explicit expressions. When the number of components is extremely large, though, it has the defects of low efficiency. To overcome the respective shortcomings of UGF and BN, a novel reliability analysis method called UGF-BN is proposed for the complex MSS. In the UGF-BN framework, the UGF method is first used to analyze the bottom components with a large number. Then probability distributions obtained are taken as the input of BN. Finally, the reliability of the complex MSS is modeled by the BN method. This proposed method improves the computational efficiency, especially for the MSS with a large number of bottom components. Besides, the aircraft reliability-based design optimization based on the UGF-BN method is further studied with budget constraints on mass, power, and cost. Finally, two cases are used to demonstrate and verify the proposed method.
{"title":"Reliability analysis of complex multi-state system based on universal generating function and Bayesian Network","authors":"Xu Liu, Wen Yao, Xiaohu Zheng, Yingchun Xu, Xiaoqian Chen","doi":"10.1177/1748006x231173301","DOIUrl":"https://doi.org/10.1177/1748006x231173301","url":null,"abstract":"In the complex multi-state system (MSS), reliability analysis is an important research content, both for equipment design, manufacturing, operation and maintenance. Universal Generating Function (UGF) is an essential method in reliability analysis, which efficiently obtains system reliability by a fast algebraic procedure. However, when structural relationships between subsystems or components are unclear or without explicit expressions, the UGF method is difficult to use or not applicable at all. Bayesian Network (BN) has a natural advantage in terms of reliability inference for the relationship without explicit expressions. When the number of components is extremely large, though, it has the defects of low efficiency. To overcome the respective shortcomings of UGF and BN, a novel reliability analysis method called UGF-BN is proposed for the complex MSS. In the UGF-BN framework, the UGF method is first used to analyze the bottom components with a large number. Then probability distributions obtained are taken as the input of BN. Finally, the reliability of the complex MSS is modeled by the BN method. This proposed method improves the computational efficiency, especially for the MSS with a large number of bottom components. Besides, the aircraft reliability-based design optimization based on the UGF-BN method is further studied with budget constraints on mass, power, and cost. Finally, two cases are used to demonstrate and verify 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":0.0,"publicationDate":"2023-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135338170","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 : 2023-05-11DOI: 10.1177/1748006X221149608
R. Remenyte-Prescott, P. Do, J. Andrews
Maintenance has an important role in modern economies and industries. Effective maintenance can improve system safety and reliability and reduce whole-life cycle costs of complex engineered systems. With the emergence of new technology and opportunities to collect system performance and condition data, it has become necessary to develop advanced methods for modelling maintenance of complex systems. Maintenance modelling allows balancing the cost of performing maintenance for a system against the losses incurred due to its performance loss. It provides decision makers with the knowledge and tools to enable them to reduce costs or/and keep system performance at a desired and safe level. Over the years, research in maintenance modelling has attracted considerable attention from both academy and industry and it is the main focus of this special issue, which contains a number of articles that are focussed on recent advancements in modelling maintenance for complex and industrial systems. The first paper by Tamssaouet et al. reviews literature in the areas of system-level prognostics and RUL estimation for multicomponent systems. Prognostics and Health Management approaches integrate fault detection, failure diagnostics, prognostics and maintenance decision support processes, and their effective usage can make large savings in asset management costs. Many studies focus on component-level prognostics, but their practical use can be enhanced only if system operators and maintenance managers can base their decisions on system-level parameters of complex system performance. Future challenges in this relatively recent research area conclude the paper. The second paper by Corset et al. proposes a stochastic model for imperfect condition-based maintenance. The degradation is modelled by a gamma process, and the condition-based maintenance policy with perfect corrective and imperfect preventive actions is proposed. The statistical inference of the model parameters is carried out, considering degradation data with imperfect maintenance. Finally, a sensitivity analysis shows how the whole lifecycle maintenance cost depends on the degradation and maintenance model parameters, and such information can support asset management decision-making processes. Ulansky and Raza in the third paper focus on imperfect inspections while modelling condition-based and preventive maintenance. The authors develop probabilistic indicators of imperfect inspections that can be used to describe correct and incorrect decisions. The effectiveness indicators of such maintenance are expressed in terms of operating costs, total error probability and a posterior probability of failure-free operation. The paper concludes with emphasising the importance of including time-dependency in the obtained probabilities of correct and incorrect decisions. A mathematical model of a hybrid maintenance policy is proposed in the fourth paper by Melo et al. Such a policy consists of combining periodic insp
{"title":"Advanced Maintenance Modelling for Industrial Systems","authors":"R. Remenyte-Prescott, P. Do, J. Andrews","doi":"10.1177/1748006X221149608","DOIUrl":"https://doi.org/10.1177/1748006X221149608","url":null,"abstract":"Maintenance has an important role in modern economies and industries. Effective maintenance can improve system safety and reliability and reduce whole-life cycle costs of complex engineered systems. With the emergence of new technology and opportunities to collect system performance and condition data, it has become necessary to develop advanced methods for modelling maintenance of complex systems. Maintenance modelling allows balancing the cost of performing maintenance for a system against the losses incurred due to its performance loss. It provides decision makers with the knowledge and tools to enable them to reduce costs or/and keep system performance at a desired and safe level. Over the years, research in maintenance modelling has attracted considerable attention from both academy and industry and it is the main focus of this special issue, which contains a number of articles that are focussed on recent advancements in modelling maintenance for complex and industrial systems. The first paper by Tamssaouet et al. reviews literature in the areas of system-level prognostics and RUL estimation for multicomponent systems. Prognostics and Health Management approaches integrate fault detection, failure diagnostics, prognostics and maintenance decision support processes, and their effective usage can make large savings in asset management costs. Many studies focus on component-level prognostics, but their practical use can be enhanced only if system operators and maintenance managers can base their decisions on system-level parameters of complex system performance. Future challenges in this relatively recent research area conclude the paper. The second paper by Corset et al. proposes a stochastic model for imperfect condition-based maintenance. The degradation is modelled by a gamma process, and the condition-based maintenance policy with perfect corrective and imperfect preventive actions is proposed. The statistical inference of the model parameters is carried out, considering degradation data with imperfect maintenance. Finally, a sensitivity analysis shows how the whole lifecycle maintenance cost depends on the degradation and maintenance model parameters, and such information can support asset management decision-making processes. Ulansky and Raza in the third paper focus on imperfect inspections while modelling condition-based and preventive maintenance. The authors develop probabilistic indicators of imperfect inspections that can be used to describe correct and incorrect decisions. The effectiveness indicators of such maintenance are expressed in terms of operating costs, total error probability and a posterior probability of failure-free operation. The paper concludes with emphasising the importance of including time-dependency in the obtained probabilities of correct and incorrect decisions. A mathematical model of a hybrid maintenance policy is proposed in the fourth paper by Melo et al. Such a policy consists of combining periodic insp","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":"2023-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75715023","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 : 2023-05-08DOI: 10.1177/1748006x231171449
Xiaofang Luo, Yushan Li, Xutao Bai, Rongkeng Tang, Hui Jin
Due to the complex structure of multi-state complex systems and the lack of data, information, and knowledge, the uncertainty of the logical relationship between the failure states of systems and components and the uncertainty of related failure data become the key issues in the reliability analysis of multi-state complex systems. In this paper, combined with multi-state fault tree (MSFT), a multi-state reliability assessment framework for complex systems considering uncertainty based on multi-source information fusion and multi-state Bayesian network (MSBN) is proposed. The multi-source information fusion method combines historical data and experts’ opinions to solve the uncertainty problem of multi-state failure data in complex equipment systems effectively. Based on the multi-source information fusion method, the calculation method of multi-state prior probability and the construction method of conditional probability are given. By constructing the conditional probability table (CPT), the uncertain logic relationship between the multi-state nodes is effectively expressed, which effectively improves the efficiency of CPT acquisition for MSBN and reduces the workload of experts scoring. Finally, a mud circulating system is taken as an example to prove the proposed method, and the specific calculation process, evaluation results, and some discussions are given. The results show that the proposed method is an effective multi-state reliability analysis method for complex uncertain multi-state systems.
{"title":"A novel approach based on fault tree analysis and Bayesian network for multi-state reliability analysis of complex equipment systems","authors":"Xiaofang Luo, Yushan Li, Xutao Bai, Rongkeng Tang, Hui Jin","doi":"10.1177/1748006x231171449","DOIUrl":"https://doi.org/10.1177/1748006x231171449","url":null,"abstract":"Due to the complex structure of multi-state complex systems and the lack of data, information, and knowledge, the uncertainty of the logical relationship between the failure states of systems and components and the uncertainty of related failure data become the key issues in the reliability analysis of multi-state complex systems. In this paper, combined with multi-state fault tree (MSFT), a multi-state reliability assessment framework for complex systems considering uncertainty based on multi-source information fusion and multi-state Bayesian network (MSBN) is proposed. The multi-source information fusion method combines historical data and experts’ opinions to solve the uncertainty problem of multi-state failure data in complex equipment systems effectively. Based on the multi-source information fusion method, the calculation method of multi-state prior probability and the construction method of conditional probability are given. By constructing the conditional probability table (CPT), the uncertain logic relationship between the multi-state nodes is effectively expressed, which effectively improves the efficiency of CPT acquisition for MSBN and reduces the workload of experts scoring. Finally, a mud circulating system is taken as an example to prove the proposed method, and the specific calculation process, evaluation results, and some discussions are given. The results show that the proposed method is an effective multi-state reliability analysis method for complex uncertain multi-state systems.","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":"2023-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80196564","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 : 2023-05-08DOI: 10.1177/1748006x231170909
Shuai Cao, Xiaoyue Wang
For safety-critical systems such as submarines and solar lighting system, mission abort is an effective way to enhance system survivability when a certain malfunction condition is met. This paper contributes by presenting a bivariate mission abort policy for generalized k-out-of- n: F systems that fail if there are at least m non-overlapping kc consecutive failed components or at least kt failed components. When the number of non-overlapping kc consecutive failed components reaches a preset level or the total number of failed components exceeds a predetermined value, the mission is aborted, and then a rescue procedure is initiated. Mission reliability and system survivability are derived by employing a two-step finite Markov chain imbedding approach. The optimization models are formulated with the purpose of maximizing the mission reliability, and minimizing the expected total cost of mission failure and system failure, respectively. A numerical example based on a solar lighting system is presented to illustrate the applicability of the proposed policies.
{"title":"Mission abort strategy for generalized k-out-of-n: F systems considering competing failure criteria","authors":"Shuai Cao, Xiaoyue Wang","doi":"10.1177/1748006x231170909","DOIUrl":"https://doi.org/10.1177/1748006x231170909","url":null,"abstract":"For safety-critical systems such as submarines and solar lighting system, mission abort is an effective way to enhance system survivability when a certain malfunction condition is met. This paper contributes by presenting a bivariate mission abort policy for generalized k-out-of- n: F systems that fail if there are at least m non-overlapping kc consecutive failed components or at least kt failed components. When the number of non-overlapping kc consecutive failed components reaches a preset level or the total number of failed components exceeds a predetermined value, the mission is aborted, and then a rescue procedure is initiated. Mission reliability and system survivability are derived by employing a two-step finite Markov chain imbedding approach. The optimization models are formulated with the purpose of maximizing the mission reliability, and minimizing the expected total cost of mission failure and system failure, respectively. A numerical example based on a solar lighting system is presented to illustrate the applicability of the proposed policies.","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":"2023-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72412960","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 : 2023-05-06DOI: 10.1177/1748006x231167201
K. Kubo, Yoichi Tanaka, J. Ishikawa
Nuclear power plants are critical infrastructures that produce electricity. However, accidents in nuclear power plants can cause considerable consequences such as the release of radioactive materials. Therefore, appropriately managing their risk is necessary. Various nuclear regulatory agencies employ probabilistic risk assessment (PRA) to effectively evaluate risks in nuclear power plants. Dynamic PRA has gained popularity because it allows for more realistic assessment by reducing the assumptions and engineering judgments related to time-dependent failure probability and/or human-action reliability in the conventional PRA methodology. However, removing all assumptions and engineering judgments is difficult; thus, the risk analyst, for example, the regulator, must understand their effects on the assessment results. This study focuses on “risk dilution,” which emerges from the assumptions about uncertainty. Dynamic PRA of a station blackout sequence in a boiling-water reactor was performed using the dynamic PRA tool, namely, Risk Assessment with Plant Interactive Dynamics (RAPID) and the severe-accident code Thermal–Hydraulic Analysis of Loss of Coolant, Emergency Core Cooling, and Severe Core Damage version 2 (THALES2), which altered the correlation parameters among the uncertainties of the events that occurred in sequence. The results demonstrated that the conditional core-damage probability and mean value of the core-damage time varied from 0.27 to 0.47 and from 7.1 to 8.7 h, respectively. When the dynamic PRA results are used for risk-informed decision making, the decision maker should adequately consider the effect of risk dilution.
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