Pub Date : 2023-05-04DOI: 10.1177/1748006x231168957
J. Skorupski, A. Tubis, S. Werbińska-Wojciechowska, Adam Wróblewski
Recently, supply chain risk management has been attracting growing attention. Therefore, various methods, tools, and practices have been developed in this area. However, they usually are focused on the direct consequences of disruptions occurring in supply chains. Thus, the purpose of this article is to present an operational risk analysis method for supply chains, in which consequences of an adverse event occurrence are assessed based on two measures: (1) the direct consequences (disruption) of supply processes to customers, and (2) recovery time for a supply system. Based on research findings, we introduced a Petri nets model for mapping material flows along a supply chain. We implement the proposed analysis method in a selected company from the automotive industry. The performed final discussion confirmed the relevance of distinguishing the direct and indirect consequences of the risk assessment. It was also suggested that the results’ interpretation could be two-fold, which may be necessary for appropriate risk management tool selection.
{"title":"Petri nets-based method for operational risk analysis in supply chains based on timeliness and recovery time","authors":"J. Skorupski, A. Tubis, S. Werbińska-Wojciechowska, Adam Wróblewski","doi":"10.1177/1748006x231168957","DOIUrl":"https://doi.org/10.1177/1748006x231168957","url":null,"abstract":"Recently, supply chain risk management has been attracting growing attention. Therefore, various methods, tools, and practices have been developed in this area. However, they usually are focused on the direct consequences of disruptions occurring in supply chains. Thus, the purpose of this article is to present an operational risk analysis method for supply chains, in which consequences of an adverse event occurrence are assessed based on two measures: (1) the direct consequences (disruption) of supply processes to customers, and (2) recovery time for a supply system. Based on research findings, we introduced a Petri nets model for mapping material flows along a supply chain. We implement the proposed analysis method in a selected company from the automotive industry. The performed final discussion confirmed the relevance of distinguishing the direct and indirect consequences of the risk assessment. It was also suggested that the results’ interpretation could be two-fold, which may be necessary for appropriate risk management tool selection.","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":"2023-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86830306","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 : 2023-04-12DOI: 10.1177/1748006x231167457
Jing Wu, Xinxin Zhang
Safe operations and adequate maintenance are two main means to achieve reliable production and reduce downtime of a plant. While the tasks of operations and maintenance are carried out by two different groups of staff, as a result, the close relationship between the two tasks is split. In this paper, this challenge is handled by a proposed integrated functional modeling framework. In this framework, the Multilevel Flow Modeling (MFM) method with its cause-consequence reasoning rules is used. Condition-based monitoring is a well-accepted strategy for predictive maintenance and fault detection based on measurements is a well-developed technology for operation support. Information fusion including monitoring conditions of assets and process sensors information for both operation and maintenance in the same modeling framework is desired. The qualitative relationship distribution between operations and maintenance can be established based on the function states of the system. In addition, these relationships are visible for both groups of staff. As a result, the detected information in the early stage of the development of the unpleasant scenarios is used to improve their situation awareness, so that the undesired emergency shutdown from both perspectives of operation and maintenance is prevented. Consequently, it can reduce production loss. A case study of operations and maintenance of a seawater injection system is carried out and shows the industrial applicability of the proposed framework. The case study strongly reveals that there is a highly close relation between operation and maintenance for ensuring the system working properly. It demonstrates that the proposed integrated framework is not only able to support operational tasks but also for the maintenance tasks by including relevant maintenance information of the system. The results show that it can potentially help with decreasing downtime of the system.
{"title":"Integrating condition-based monitoring with process sensors information for operation and maintenance in a functional modeling framework","authors":"Jing Wu, Xinxin Zhang","doi":"10.1177/1748006x231167457","DOIUrl":"https://doi.org/10.1177/1748006x231167457","url":null,"abstract":"Safe operations and adequate maintenance are two main means to achieve reliable production and reduce downtime of a plant. While the tasks of operations and maintenance are carried out by two different groups of staff, as a result, the close relationship between the two tasks is split. In this paper, this challenge is handled by a proposed integrated functional modeling framework. In this framework, the Multilevel Flow Modeling (MFM) method with its cause-consequence reasoning rules is used. Condition-based monitoring is a well-accepted strategy for predictive maintenance and fault detection based on measurements is a well-developed technology for operation support. Information fusion including monitoring conditions of assets and process sensors information for both operation and maintenance in the same modeling framework is desired. The qualitative relationship distribution between operations and maintenance can be established based on the function states of the system. In addition, these relationships are visible for both groups of staff. As a result, the detected information in the early stage of the development of the unpleasant scenarios is used to improve their situation awareness, so that the undesired emergency shutdown from both perspectives of operation and maintenance is prevented. Consequently, it can reduce production loss. A case study of operations and maintenance of a seawater injection system is carried out and shows the industrial applicability of the proposed framework. The case study strongly reveals that there is a highly close relation between operation and maintenance for ensuring the system working properly. It demonstrates that the proposed integrated framework is not only able to support operational tasks but also for the maintenance tasks by including relevant maintenance information of the system. The results show that it can potentially help with decreasing downtime of the 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":"2023-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84533726","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 : 2023-04-07DOI: 10.1177/1748006x231164723
Ming Li, Yuan Luo, L. Xie, Cao Tong, Chuan Chen
As a core strategic technology industry, the wind power plays an important role in protecting national energy reserves. The large gear component is one of the core foundation parts in wind turbines, and its quality indexes greatly affect the service performance of the wind turbine drive chain and even the wind turbine as a whole. This paper calculates the fatigue load history of the wind power large gear system under the coupling mechanism of elastic behavior based on a multidimensional finite element method, and obtains the probabilistic fatigue strength of gear teeth through the gear low circumference fatigue test and life distribution transformation method, and deeply explores the inherent characteristics of the wind power gear system in functional implementation and then establishes a system fatigue reliability evaluation model. Finally, a mapping path from the global structural elements of the wind power gearbox to the reliability indexes of the gear system is constructed with significant simulation and test cost advantages. It can provide structural optimization guidance in the development and design of large wind power gear systems, and significantly reduce the cost of achieving reliability indexes in the design iterations of such large high-end equipment. At the same time, it can provide cost-effective training data for intelligent optimization algorithms such as the deep reinforcement learning, which will eventually achieve multi-objective optimal stiffness matching for wind power gearboxes under reliability index constraints.
{"title":"Fatigue reliability assessment method for wind power gear system based on multidimensional finite element method","authors":"Ming Li, Yuan Luo, L. Xie, Cao Tong, Chuan Chen","doi":"10.1177/1748006x231164723","DOIUrl":"https://doi.org/10.1177/1748006x231164723","url":null,"abstract":"As a core strategic technology industry, the wind power plays an important role in protecting national energy reserves. The large gear component is one of the core foundation parts in wind turbines, and its quality indexes greatly affect the service performance of the wind turbine drive chain and even the wind turbine as a whole. This paper calculates the fatigue load history of the wind power large gear system under the coupling mechanism of elastic behavior based on a multidimensional finite element method, and obtains the probabilistic fatigue strength of gear teeth through the gear low circumference fatigue test and life distribution transformation method, and deeply explores the inherent characteristics of the wind power gear system in functional implementation and then establishes a system fatigue reliability evaluation model. Finally, a mapping path from the global structural elements of the wind power gearbox to the reliability indexes of the gear system is constructed with significant simulation and test cost advantages. It can provide structural optimization guidance in the development and design of large wind power gear systems, and significantly reduce the cost of achieving reliability indexes in the design iterations of such large high-end equipment. At the same time, it can provide cost-effective training data for intelligent optimization algorithms such as the deep reinforcement learning, which will eventually achieve multi-objective optimal stiffness matching for wind power gearboxes under reliability index constraints.","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":"2023-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83453628","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 : 2023-04-03DOI: 10.1177/1748006x231162127
Li Lu, Yizhong Wu, Qi Zhang, Zhehao Xia, Ping Qiao
In this paper, a response band-based method for time-dependent reliability-based robust design optimization is proposed. The proposed method provides a novel alternative framework, consist of a two-step transformation stage and a solving stage, to solve the time-dependent reliability-based robust design optimization problem. The original time-dependent reliability-based robust design optimization problem is transformed into an equivalent deterministic robust design optimization problem in the transformation stage, and the equivalent problem is settled in the solving stage. In the transformation stage, the dynamic modal decomposition technique and the kriging technique are combined to overcome the problem that there is no standard for both time division and observation sampling in the commonly used transformation methods. In the solving stage, an approach for constructing the response band of the objective function is presented, which significantly reduces the computational consumption of the variation evaluation of the objective function. Five cases are employed to verify the effectiveness of the proposed method.
{"title":"A response band-based method for time-dependent reliability-based robust design optimization","authors":"Li Lu, Yizhong Wu, Qi Zhang, Zhehao Xia, Ping Qiao","doi":"10.1177/1748006x231162127","DOIUrl":"https://doi.org/10.1177/1748006x231162127","url":null,"abstract":"In this paper, a response band-based method for time-dependent reliability-based robust design optimization is proposed. The proposed method provides a novel alternative framework, consist of a two-step transformation stage and a solving stage, to solve the time-dependent reliability-based robust design optimization problem. The original time-dependent reliability-based robust design optimization problem is transformed into an equivalent deterministic robust design optimization problem in the transformation stage, and the equivalent problem is settled in the solving stage. In the transformation stage, the dynamic modal decomposition technique and the kriging technique are combined to overcome the problem that there is no standard for both time division and observation sampling in the commonly used transformation methods. In the solving stage, an approach for constructing the response band of the objective function is presented, which significantly reduces the computational consumption of the variation evaluation of the objective function. Five cases are employed to verify the effectiveness 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":"2023-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73243779","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 : 2023-04-01DOI: 10.1177/1748006X221104883
Christoph A. Thieme, M. Ramos, I. Utne, A. Mosleh
Assessing and demonstrating Safety of Autonomous Systems is not a trivial task. It poses several challenges and requires covering aspects related to hardware, software, humans, the environment, and their interactions, in addition to legislation, society, and ethics. The International Workshop on Autonomous System Safety (IWASS) aims at identifying research directions and solutions for addressing these challenges. IWASS’ first edition was held in 2019 in Trondheim, Norway, followed by a second edition in 2021 in an online format. IWASS 2019 and 2021 gathered each around 50 invited experts from various parts of the world on topics concerning autonomous systems safety from academia, industry, and governmental agencies. The two editions of IWASS focused on critical issues for autonomous systems safety, namely: (i) Demonstrating safety of autonomous systems – verification and validation (V&V), and risk acceptance, (ii) human in or on the loop – the role of humans in autonomous systems operations, (iii) modeling and simulation for understanding complexity and cascading failures, and (iv) artificial intelligence (AI) and data analytics for resilient autonomous systems. This special issue of the Journal of Risk and Reliability aims to provide a venue for researchers to extend and share scientific contributions to the topics addressed during IWASS. The papers in this special issue portray IWASS’ wide range of discussed issues, as well as the represented industries and application areas. They complement IWASS’ discussions, as described below.
{"title":"Editorial for the special issue on the International Workshop on Autonomous System Safety (IWASS)","authors":"Christoph A. Thieme, M. Ramos, I. Utne, A. Mosleh","doi":"10.1177/1748006X221104883","DOIUrl":"https://doi.org/10.1177/1748006X221104883","url":null,"abstract":"Assessing and demonstrating Safety of Autonomous Systems is not a trivial task. It poses several challenges and requires covering aspects related to hardware, software, humans, the environment, and their interactions, in addition to legislation, society, and ethics. The International Workshop on Autonomous System Safety (IWASS) aims at identifying research directions and solutions for addressing these challenges. IWASS’ first edition was held in 2019 in Trondheim, Norway, followed by a second edition in 2021 in an online format. IWASS 2019 and 2021 gathered each around 50 invited experts from various parts of the world on topics concerning autonomous systems safety from academia, industry, and governmental agencies. The two editions of IWASS focused on critical issues for autonomous systems safety, namely: (i) Demonstrating safety of autonomous systems – verification and validation (V&V), and risk acceptance, (ii) human in or on the loop – the role of humans in autonomous systems operations, (iii) modeling and simulation for understanding complexity and cascading failures, and (iv) artificial intelligence (AI) and data analytics for resilient autonomous systems. This special issue of the Journal of Risk and Reliability aims to provide a venue for researchers to extend and share scientific contributions to the topics addressed during IWASS. The papers in this special issue portray IWASS’ wide range of discussed issues, as well as the represented industries and application areas. They complement IWASS’ discussions, as described below.","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":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84640983","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 : 2023-03-30DOI: 10.1177/1748006x231164848
Farjam Kayedpour, M. Amiri, M. Rafizadeh, Arash Shahryari Nia, M. Sharifi
Many studies have been conducted on designing systems based on the redundancy allocation problem (RAP). However, considering repairable warm-standby components (which are subject to failure even in an idle state) is somewhat neglected by researchers due to the complex mathematical models. One of the crucial aspects of these systems is considering the probability of failure when switching to a standby component or subsystem. This study tries to highlight these imperfect switching and switch selection strategies in the redundancy allocation designs. In this regard, this article is dedicated to developing two RAP models (a single objective and a bi-objective) with warm standby repairable components by proposing a solving approach based on the genetic algorithm (GA) and Markov chains. Since the model’s objective functions minimize the system’s mean time to failure (MTTF) and cost, we discussed how imperfect switches affect the total system’s cost and mean time to failure for the proposed RAPs. Finally, we adopted a GA and a non-dominated sorting genetic algorithm (NSGA-II) to solve the proposed models due to the models’ complexity. Solving these models clearly indicates the critical role of selecting an appropriate switching strategy on the system’s costs and reliability.
{"title":"A Markov chain-based genetic algorithm for solving a redundancy allocation problem for a system with repairable warm standby components","authors":"Farjam Kayedpour, M. Amiri, M. Rafizadeh, Arash Shahryari Nia, M. Sharifi","doi":"10.1177/1748006x231164848","DOIUrl":"https://doi.org/10.1177/1748006x231164848","url":null,"abstract":"Many studies have been conducted on designing systems based on the redundancy allocation problem (RAP). However, considering repairable warm-standby components (which are subject to failure even in an idle state) is somewhat neglected by researchers due to the complex mathematical models. One of the crucial aspects of these systems is considering the probability of failure when switching to a standby component or subsystem. This study tries to highlight these imperfect switching and switch selection strategies in the redundancy allocation designs. In this regard, this article is dedicated to developing two RAP models (a single objective and a bi-objective) with warm standby repairable components by proposing a solving approach based on the genetic algorithm (GA) and Markov chains. Since the model’s objective functions minimize the system’s mean time to failure (MTTF) and cost, we discussed how imperfect switches affect the total system’s cost and mean time to failure for the proposed RAPs. Finally, we adopted a GA and a non-dominated sorting genetic algorithm (NSGA-II) to solve the proposed models due to the models’ complexity. Solving these models clearly indicates the critical role of selecting an appropriate switching strategy on the system’s costs and reliability.","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":"2023-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77822898","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 : 2023-03-20DOI: 10.1177/1748006x231157827
W. Shi, Wenhao Gui
This paper investigates the performance of empirical Bayesian and E-Bayesian estimation of the bathtub-shaped distribution based on the progressive Type-II censored samples. These two estimations overcome the selection of hyperparameters in the Bayesian method. The empirical Bayesian analysis employs the classical approach to determine the hyperparameters. The E-Bayesian estimation uses the prior distribution of hyperparameters to derive the expectation of Bayesian estimates. The estimates of parameters are derived under the squared error loss and LINEX loss functions. The extensive simulations show the results of the empirical Bayesian estimates and the E-Bayesain estimates. Further, the empirical Bayesian estimation is also presented to analyze the parameters of bathtub-shaped distribution based on the general progressive Type-II censored samples. The two datasets from the electromechanical field and medical survival analysis are analyzed using the empirical Bayesian and E-Bayesian methods.
{"title":"Reliability analysis of bathtub-shaped distribution using empirical Bayesian and E-Bayesian estimations under progressive Type-II censoring","authors":"W. Shi, Wenhao Gui","doi":"10.1177/1748006x231157827","DOIUrl":"https://doi.org/10.1177/1748006x231157827","url":null,"abstract":"This paper investigates the performance of empirical Bayesian and E-Bayesian estimation of the bathtub-shaped distribution based on the progressive Type-II censored samples. These two estimations overcome the selection of hyperparameters in the Bayesian method. The empirical Bayesian analysis employs the classical approach to determine the hyperparameters. The E-Bayesian estimation uses the prior distribution of hyperparameters to derive the expectation of Bayesian estimates. The estimates of parameters are derived under the squared error loss and LINEX loss functions. The extensive simulations show the results of the empirical Bayesian estimates and the E-Bayesain estimates. Further, the empirical Bayesian estimation is also presented to analyze the parameters of bathtub-shaped distribution based on the general progressive Type-II censored samples. The two datasets from the electromechanical field and medical survival analysis are analyzed using the empirical Bayesian and E-Bayesian 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":"2023-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88550916","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 : 2023-03-20DOI: 10.1177/1748006x231159823
Gu Dongwei, Zhong Yuhong, Hu Yanjuan, Chen Guang, Wang Zhixin, Li Nianhuan
As an important tool to evaluate the key components of the multi-state system, the importance degree is essential in the system reliability design stage, to provide the basis for the system reliability improvement and maintenance. To accurately improve the reliability of the system, this paper provides an importance measure analysis method that comprehensively considers the state and maintenance effects. To measure the impact of components on the system more comprehensively, this paper proposes an Integrated Availability Importance Measure (IAIM) to evaluate the relative importance of components by combining component state probability, state transition rate, repair rate, and state repair transition rate and considering the impact of component reliability and maintainability on the performance of multi-state systems. Considering the randomness of system operation, a Monte Carlo simulation based IAIM analysis method for a multi-state system was developed. Taking the series system and the hybrid system as examples, the IAIM of the component is simulated and analyzed. Comparing IAIM with Integrated Importance Measure (IIM) and performance Utility Importance measure (UI), among them, UI considers the impact of the state on performance, while IIM considers state transition on the basis of UI, but does not consider the impact of maintenance. IAIM is more comprehensive than UI and IAIM. It can be seen that IAIM is different from importance measures based on reliability. This is because the IAIM fully examines the impact of component reliability and maintainability on multi-state systems. The IAIM improves the traditional shortcomings of only considering component reliability, and provides a more comprehensive way to evaluate the system.
{"title":"Integrated availability importance measure for multi-state complex systems analysis","authors":"Gu Dongwei, Zhong Yuhong, Hu Yanjuan, Chen Guang, Wang Zhixin, Li Nianhuan","doi":"10.1177/1748006x231159823","DOIUrl":"https://doi.org/10.1177/1748006x231159823","url":null,"abstract":"As an important tool to evaluate the key components of the multi-state system, the importance degree is essential in the system reliability design stage, to provide the basis for the system reliability improvement and maintenance. To accurately improve the reliability of the system, this paper provides an importance measure analysis method that comprehensively considers the state and maintenance effects. To measure the impact of components on the system more comprehensively, this paper proposes an Integrated Availability Importance Measure (IAIM) to evaluate the relative importance of components by combining component state probability, state transition rate, repair rate, and state repair transition rate and considering the impact of component reliability and maintainability on the performance of multi-state systems. Considering the randomness of system operation, a Monte Carlo simulation based IAIM analysis method for a multi-state system was developed. Taking the series system and the hybrid system as examples, the IAIM of the component is simulated and analyzed. Comparing IAIM with Integrated Importance Measure (IIM) and performance Utility Importance measure (UI), among them, UI considers the impact of the state on performance, while IIM considers state transition on the basis of UI, but does not consider the impact of maintenance. IAIM is more comprehensive than UI and IAIM. It can be seen that IAIM is different from importance measures based on reliability. This is because the IAIM fully examines the impact of component reliability and maintainability on multi-state systems. The IAIM improves the traditional shortcomings of only considering component reliability, and provides a more comprehensive way to evaluate the 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":"2023-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83007075","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 : 2023-03-15DOI: 10.1177/1748006x231159703
Somayyeh Shahraki Dehsoukhteh, M. Razmkhah, B. Castanier
Applying the theory of uncertainty is a good approach to study the reliability of a system when there is a little frequency of suitable data. Based on this theory, the policy of block replacement with minimal repair is considered assuming the system lifetime follows a power law process. The unknown parameters are estimated according to evidence theory that affects the epistemic uncertainty. The Dempster-Shafer as well as Yager rules are applied to aggregate the judgements and mental estimates of two or more experts. After determining the unknown parameters, a judgement interval is derived for the optimal replacement time using the long-run cost criterion.
{"title":"Optimal block replacement based on expert judgement method","authors":"Somayyeh Shahraki Dehsoukhteh, M. Razmkhah, B. Castanier","doi":"10.1177/1748006x231159703","DOIUrl":"https://doi.org/10.1177/1748006x231159703","url":null,"abstract":"Applying the theory of uncertainty is a good approach to study the reliability of a system when there is a little frequency of suitable data. Based on this theory, the policy of block replacement with minimal repair is considered assuming the system lifetime follows a power law process. The unknown parameters are estimated according to evidence theory that affects the epistemic uncertainty. The Dempster-Shafer as well as Yager rules are applied to aggregate the judgements and mental estimates of two or more experts. After determining the unknown parameters, a judgement interval is derived for the optimal replacement time using the long-run cost criterion.","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":"2023-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89261044","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 : 2023-03-07DOI: 10.1177/1748006x231156841
Kundan Singh, Amulya Kumar Mahto, Yogesh Mani Tripathi, Liang Wang
We consider estimation of multicomponent stress-strength reliability under classical as well as Bayesian approaches when stress-strength components follow lognormal distributions and data are observed under progressive censoring. Various estimates are obtained by sequentially considering one parameter common and unknown. Different expressions for the reliability are evaluated under these cases. For both the cases, we obtain different estimates of considered parametric function using likelihood, Lindley and repeated sampling methods. Asymptotic and credible intervals are also obtained. Extensive simulations are conducted to examine the behavior of all estimates under various censoring schemes. We finally present analysis of a real life example from application purposes.
{"title":"Estimation in a multicomponent stress-strength model for progressive censored lognormal distribution","authors":"Kundan Singh, Amulya Kumar Mahto, Yogesh Mani Tripathi, Liang Wang","doi":"10.1177/1748006x231156841","DOIUrl":"https://doi.org/10.1177/1748006x231156841","url":null,"abstract":"We consider estimation of multicomponent stress-strength reliability under classical as well as Bayesian approaches when stress-strength components follow lognormal distributions and data are observed under progressive censoring. Various estimates are obtained by sequentially considering one parameter common and unknown. Different expressions for the reliability are evaluated under these cases. For both the cases, we obtain different estimates of considered parametric function using likelihood, Lindley and repeated sampling methods. Asymptotic and credible intervals are also obtained. Extensive simulations are conducted to examine the behavior of all estimates under various censoring schemes. We finally present analysis of a real life example from application purposes.","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":"2023-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88635862","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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