Jiantai Wang, Xiaobing Ma, Kaiye Gao, Yu Zhao, Li Yang
Inspections often perform imperfect outcomes during maintenance processes owing to human errors, management issues and other limitations. In particular, such imperfection affects the maintenance management of multistage deterioration significantly due to both false state identification and measurement errors, whose quantitative analysis, however, is seldom reported in the literature. To fill these gaps, this paper devises a condition‐based maintenance management strategy oriented to two‐stage continuous degradation under two‐dimensional inspection imperfection. Specifically, a threshold‐based replacement is executed under the normal‐working state if the detected degradation value exceeds the preset limit; additionally, preventive replacement is immediately performed once the defective state is identified. Notably, the detection outcome rather than the actual working condition decides how preventive maintenance operates. The long‐run cost rate is minimized via the optimization of the inspection cycle and replacement limit. Besides, numerical experiments conducted on train bogie bearing are provided, showing substantial superiorities over cost‐effectiveness promotion and performance improvement.
{"title":"Condition‐based maintenance management for two‐stage continuous deterioration with two‐dimensional inspection errors","authors":"Jiantai Wang, Xiaobing Ma, Kaiye Gao, Yu Zhao, Li Yang","doi":"10.1002/qre.3613","DOIUrl":"https://doi.org/10.1002/qre.3613","url":null,"abstract":"Inspections often perform imperfect outcomes during maintenance processes owing to human errors, management issues and other limitations. In particular, such imperfection affects the maintenance management of multistage deterioration significantly due to both false state identification and measurement errors, whose quantitative analysis, however, is seldom reported in the literature. To fill these gaps, this paper devises a condition‐based maintenance management strategy oriented to two‐stage continuous degradation under two‐dimensional inspection imperfection. Specifically, a threshold‐based replacement is executed under the normal‐working state if the detected degradation value exceeds the preset limit; additionally, preventive replacement is immediately performed once the defective state is identified. Notably, the detection outcome rather than the actual working condition decides how preventive maintenance operates. The long‐run cost rate is minimized via the optimization of the inspection cycle and replacement limit. Besides, numerical experiments conducted on train bogie bearing are provided, showing substantial superiorities over cost‐effectiveness promotion and performance improvement.","PeriodicalId":56088,"journal":{"name":"Quality and Reliability Engineering International","volume":"33 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141546361","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ammar M. Sarhan, Ehab M. Almetwally, Abdelfattah Mustafa, Ahlam H. Tolba
In this study, we define a series system with non‐independent and non‐identical components using a shock model with sources of fatal shocks. Here, it is assumed that the shocks happen randomly and independently, following a Weibull distribution with various scale and shape parameters. A dependability model with unknown parameters is produced by this process. Making statistical conclusions about the model parameters is the main objective of this research. We apply the maximum likelihood and Bayes approaches to determine the model parameters' point and interval estimates. We shall demonstrate that no analytical solutions to the likelihood equations must be solved to obtain the parameters' maximum likelihood estimates. As a result, we will use the R program to approximate the parameter point and interval estimates. Additionally, we will use the bootstrap‐t and bootstrap‐p methods to approximate the confidence intervals. About the Bayesian approach, we presume that each model parameter is independent and follows a gamma prior distribution with a range of attached hyperparameter values. The model parameters' posterior distribution does not take a practical form. We are unable to derive the Bayes estimates in closed forms as a result. To solve this issue, we use the Gibbs sampler from the Metropolis‐Hasting algorithm based on the Markov chain Monte Carlo method to condense the posterior distribution. To demonstrate the relevance of this research, a real data set application is detailed.
{"title":"Statistical inference of a series reliability system using shock models with Weibull distribution","authors":"Ammar M. Sarhan, Ehab M. Almetwally, Abdelfattah Mustafa, Ahlam H. Tolba","doi":"10.1002/qre.3604","DOIUrl":"https://doi.org/10.1002/qre.3604","url":null,"abstract":"In this study, we define a series system with non‐independent and non‐identical components using a shock model with sources of fatal shocks. Here, it is assumed that the shocks happen randomly and independently, following a Weibull distribution with various scale and shape parameters. A dependability model with unknown parameters is produced by this process. Making statistical conclusions about the model parameters is the main objective of this research. We apply the maximum likelihood and Bayes approaches to determine the model parameters' point and interval estimates. We shall demonstrate that no analytical solutions to the likelihood equations must be solved to obtain the parameters' maximum likelihood estimates. As a result, we will use the R program to approximate the parameter point and interval estimates. Additionally, we will use the bootstrap‐t and bootstrap‐p methods to approximate the confidence intervals. About the Bayesian approach, we presume that each model parameter is independent and follows a gamma prior distribution with a range of attached hyperparameter values. The model parameters' posterior distribution does not take a practical form. We are unable to derive the Bayes estimates in closed forms as a result. To solve this issue, we use the Gibbs sampler from the Metropolis‐Hasting algorithm based on the Markov chain Monte Carlo method to condense the posterior distribution. To demonstrate the relevance of this research, a real data set application is detailed.","PeriodicalId":56088,"journal":{"name":"Quality and Reliability Engineering International","volume":"39 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141546363","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper presents a consecutive ‐out‐of‐: balance system in a shock environment, where the state transition of component is induced by external shocks. If a predetermined threshold number of effective shocks are applied to component in a critical state, the component will fail. The state of the system is defined by the number of consecutive failing component groups in the system, which leads to system failure when a critical number of consecutive failing components is reached. To minimize maintenance costs, we propose a preventive maintenance method with an optimization model. We use finite Markov chain imbedding and Phase‐type distribution to calculate component group failure rates and associated probability functions in discrete and continuous time, respectively. The validity and accuracy of the model are confirmed by numerical examples and Monte Carlo simulations.
{"title":"Reliability analysis and preventive maintenance policy for consecutive k$k$‐out‐of‐n:F$n: F$ balanced system under failure criterion operating in shock environment","authors":"Qinglai Dong, Mengmeng Bai","doi":"10.1002/qre.3612","DOIUrl":"https://doi.org/10.1002/qre.3612","url":null,"abstract":"This paper presents a consecutive ‐out‐of‐: balance system in a shock environment, where the state transition of component is induced by external shocks. If a predetermined threshold number of effective shocks are applied to component in a critical state, the component will fail. The state of the system is defined by the number of consecutive failing component groups in the system, which leads to system failure when a critical number of consecutive failing components is reached. To minimize maintenance costs, we propose a preventive maintenance method with an optimization model. We use finite Markov chain imbedding and Phase‐type distribution to calculate component group failure rates and associated probability functions in discrete and continuous time, respectively. The validity and accuracy of the model are confirmed by numerical examples and Monte Carlo simulations.","PeriodicalId":56088,"journal":{"name":"Quality and Reliability Engineering International","volume":"90 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141546362","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The reliability modeling and optimization of performance sharing systems (PSSs) are of vital importance due to their wide applications. Existing research mainly focuses on evaluating and maximizing the reliability of PSSs. However, in many practical systems, decision‐makers tend to prioritize the average cost of the system over its reliability. This paper studies the resource allocation optimization in common bus PSSs. In such systems, each unit has binary‐state random performance to satisfy the multi‐state random demand. The surplus performance can be shared via a common bus with transmission loss between the common bus and the unit. The performance allocation, performance transmission, and unsupplied demand incur costs. Resource allocation strategies are determined by optimization models considering different objective functions and constraints. Additionally, transmission loss between the common bus and the unit is considered. A genetic algorithm is employed to efficiently find the optimal allocation strategies. Numerical examples prove the effectiveness of the proposed models in improving system reliability and reducing system costs.
{"title":"Optimal resource allocation in common bus performance sharing systems with transmission loss","authors":"Liudong Gu, Guanjun Wang, Yifan Zhou","doi":"10.1002/qre.3610","DOIUrl":"https://doi.org/10.1002/qre.3610","url":null,"abstract":"The reliability modeling and optimization of performance sharing systems (PSSs) are of vital importance due to their wide applications. Existing research mainly focuses on evaluating and maximizing the reliability of PSSs. However, in many practical systems, decision‐makers tend to prioritize the average cost of the system over its reliability. This paper studies the resource allocation optimization in common bus PSSs. In such systems, each unit has binary‐state random performance to satisfy the multi‐state random demand. The surplus performance can be shared via a common bus with transmission loss between the common bus and the unit. The performance allocation, performance transmission, and unsupplied demand incur costs. Resource allocation strategies are determined by optimization models considering different objective functions and constraints. Additionally, transmission loss between the common bus and the unit is considered. A genetic algorithm is employed to efficiently find the optimal allocation strategies. Numerical examples prove the effectiveness of the proposed models in improving system reliability and reducing system costs.","PeriodicalId":56088,"journal":{"name":"Quality and Reliability Engineering International","volume":"31 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141503570","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Éder S. Brito, Vera L. D. Tomazella, Paulo H. Ferreira, Francisco Louzada Neto, Oilson A. Gonzatto Junior
Imperfect repairs (IRs) are widely applicable in reliability engineering since most equipment is not completely replaced after failure. In this sense, it is necessary to develop methodologies that can describe failure processes and predict the reliability of systems under this type of repair. One of the challenges in this context is to establish reliability models for multiple repairable systems considering unobserved heterogeneity associated with systems failure times and their failure intensity after performing IRs. Thus, in this work, frailty models are proposed to identify unobserved heterogeneity in these failure processes. In this context, we consider the arithmetic reduction of age (ARA) and arithmetic reduction of intensity (ARI) classes of IR models, with constant repair efficiency and a power‐law process distribution to model failure times and a univariate Gamma distributed frailty by all systems failure times. Classical inferential methods are used to estimate the parameters and reliability predictors of systems under IRs. An extensive simulation study is carried out under different scenarios to investigate the suitability of the models and the asymptotic consistency and efficiency properties of the maximum likelihood estimators. Finally, we illustrate the practical relevance of the proposed models on two real data sets.
不完全修复（IRs）广泛应用于可靠性工程，因为大多数设备在发生故障后不会被完全替换。从这个意义上说，有必要开发能够描述故障过程并预测系统在此类维修下的可靠性的方法。这方面的挑战之一是为多个可修复系统建立可靠性模型，同时考虑到与系统故障时间相关的未观察到的异质性以及执行 IR 后的故障强度。因此，在这项工作中，我们提出了虚弱模型来识别这些故障过程中未观察到的异质性。在此背景下，我们考虑了年龄算术缩减（ARA）和强度算术缩减（ARI）类 IR 模型，用恒定维修效率和幂律过程分布来模拟故障时间，并用单变量伽马分布虚弱来模拟所有系统的故障时间。经典推理方法用于估算 IR 条件下系统的参数和可靠性预测因子。我们在不同场景下进行了广泛的模拟研究，以考察模型的适用性以及最大似然估计值的渐近一致性和效率特性。最后，我们在两个真实数据集上说明了所提模型的实用性。
{"title":"Reliability analysis of multiple repairable systems under imperfect repair and unobserved heterogeneity","authors":"Éder S. Brito, Vera L. D. Tomazella, Paulo H. Ferreira, Francisco Louzada Neto, Oilson A. Gonzatto Junior","doi":"10.1002/qre.3607","DOIUrl":"https://doi.org/10.1002/qre.3607","url":null,"abstract":"Imperfect repairs (IRs) are widely applicable in reliability engineering since most equipment is not completely replaced after failure. In this sense, it is necessary to develop methodologies that can describe failure processes and predict the reliability of systems under this type of repair. One of the challenges in this context is to establish reliability models for multiple repairable systems considering unobserved heterogeneity associated with systems failure times and their failure intensity after performing IRs. Thus, in this work, frailty models are proposed to identify unobserved heterogeneity in these failure processes. In this context, we consider the arithmetic reduction of age (ARA) and arithmetic reduction of intensity (ARI) classes of IR models, with constant repair efficiency and a power‐law process distribution to model failure times and a univariate Gamma distributed frailty by all systems failure times. Classical inferential methods are used to estimate the parameters and reliability predictors of systems under IRs. An extensive simulation study is carried out under different scenarios to investigate the suitability of the models and the asymptotic consistency and efficiency properties of the maximum likelihood estimators. Finally, we illustrate the practical relevance of the proposed models on two real data sets.","PeriodicalId":56088,"journal":{"name":"Quality and Reliability Engineering International","volume":"45 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141528368","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Advanced reliability and safety methodologies and novel applications (Selected papers of the international conference of QR2MSE2023)","authors":"Hong‐Zhong Huang, He Li, Yanfeng Li","doi":"10.1002/qre.3611","DOIUrl":"https://doi.org/10.1002/qre.3611","url":null,"abstract":"","PeriodicalId":56088,"journal":{"name":"Quality and Reliability Engineering International","volume":"2 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141503571","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The step‐stress procedure is a popular accelerated test used to analyze the lifetime of highly reliable components. This paper considers a simple step‐stress accelerated test assuming a cumulative exposure model with uncensored lifetime data following a Weibull distribution. The maximum likelihood approach is often used to analyze accelerated stress test data. Another approach is to use the Bayesian inference, which is useful when there is limited data available. In this paper, the parameters of the model are estimated based on the objective Bayesian viewpoint using non‐informative priors. Our main aim is to propose the maximal data information prior (MDIP) presented by Zellner (1984) as an alternative prior to the conventional independent gamma priors for the unknown parameters, in situations where there is little or no a priori knowledge about the parameters. We also obtain the Bayes estimators based on both classes of priors, assuming three different loss functions: square error loss function (SELF), linear‐exponential loss function (LINEX), and generalized entropy loss function (GELF). The proposed MDIP prior is compared with the gamma priors via Monte Carlo simulations by examining their biases and mean square errors under the three loss functions, and coverage probability. Additionally, we employ the Markov Chain Monte Carlo (MCMC) algorithm to extract characteristics of marginal posterior distributions, such as the Bayes estimator and credible intervals. Finally, a real lifetime data is presented to illustrate the proposed methodology.
{"title":"Maximal entropy prior for the simple step‐stress accelerated test","authors":"Fernando Antonio Moala, Karlla Delalibera Chagas","doi":"10.1002/qre.3609","DOIUrl":"https://doi.org/10.1002/qre.3609","url":null,"abstract":"The step‐stress procedure is a popular accelerated test used to analyze the lifetime of highly reliable components. This paper considers a simple step‐stress accelerated test assuming a cumulative exposure model with uncensored lifetime data following a Weibull distribution. The maximum likelihood approach is often used to analyze accelerated stress test data. Another approach is to use the Bayesian inference, which is useful when there is limited data available. In this paper, the parameters of the model are estimated based on the objective Bayesian viewpoint using non‐informative priors. Our main aim is to propose the maximal data information prior (MDIP) presented by Zellner (1984) as an alternative prior to the conventional independent gamma priors for the unknown parameters, in situations where there is little or no a priori knowledge about the parameters. We also obtain the Bayes estimators based on both classes of priors, assuming three different loss functions: square error loss function (SELF), linear‐exponential loss function (LINEX), and generalized entropy loss function (GELF). The proposed MDIP prior is compared with the gamma priors via Monte Carlo simulations by examining their biases and mean square errors under the three loss functions, and coverage probability. Additionally, we employ the Markov Chain Monte Carlo (MCMC) algorithm to extract characteristics of marginal posterior distributions, such as the Bayes estimator and credible intervals. Finally, a real lifetime data is presented to illustrate the proposed methodology.","PeriodicalId":56088,"journal":{"name":"Quality and Reliability Engineering International","volume":"73 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141503572","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
David Han, James D. Brownlow, Jesse Thompson, Ralph G. Brooks
Ensuring an acceptable level of reliability stands as a primary imperative for any mission‐focused operation since it serves as a critical determinant of success. Inadequate reliability can lead to severe repercussions, including substantial expenses for repairs and replacements, missed opportunities, service disruptions, and in the worst cases, safety violations and human casualties. Within national defense organizations such as the USAF, the precise assessment and maintenance of system reliability play a pivotal role in ensuring the success of mission‐critical operations. In this research, our primary objective is to model the reliability of repairable subsystems within the framework of competing and complementary risks. Subsequently, we construct the overall reliability of the entire repairable system, utilizing day‐to‐day group‐censored maintenance data from two identical aircraft systems. Assuming that the lifetimes of subsystems follow non‐identical exponential distributions, it is theoretically justified that the system reliability can be modeled by homogeneous Poisson processes even though the number of subsystems of any particular type is unknown and the temporal order of multiple subsystem failures within a given time interval is uncertain due to interval censoring. Using the proposed model, we formulate the likelihood function for the mean time between failures of subsystems with different causes, and subsequently establish an inferential procedure for the model parameters. Given a considerable number of parameters to estimate, we explore the efficacy of a Bayesian approach, treating the contractor‐supplied estimates as the hyperparameters of prior distributions. This approach mitigates potential model uncertainty as well as the practical limitation of a frequentist‐based approach. It also facilitates continuous updates of the estimates as new maintenance data become available. Finally, the entire inferential procedures were implemented in Microsoft Excel so that it is easy for any reliability practitioner to use without the need to learn sophisticated programming languages. Thus, this research supports an ongoing, real‐time assessment of the overall mission reliability and helps early detection of any subsystem whose reliability is below the threshold level.
确保可接受的可靠性水平是任何以任务为中心的运行的首要任务，因为它是决定成败的关键因素。可靠性不足会导致严重后果，包括维修和更换的巨额费用、错失良机、服务中断，最严重的情况下还会导致违反安全规定和人员伤亡。在美国空军等国防组织中，系统可靠性的精确评估和维护对确保关键任务的成功执行起着举足轻重的作用。在这项研究中，我们的主要目标是在相互竞争和互补的风险框架内建立可修复子系统的可靠性模型。随后，我们利用来自两个相同飞机系统的每日组删减维护数据，构建了整个可修复系统的总体可靠性。假定子系统的寿命遵循非同指数分布，那么即使任何特定类型的子系统数量未知，并且由于时间间隔删失，给定时间间隔内多个子系统故障的时间顺序不确定，系统可靠性仍可由同质泊松过程建模，这在理论上是合理的。利用所提出的模型，我们提出了不同原因子系统故障平均间隔时间的似然函数，并随后建立了模型参数的推断程序。鉴于需要估计的参数数量相当多，我们探讨了贝叶斯方法的有效性，将承包商提供的估计值视为先验分布的超参数。这种方法缓解了潜在的模型不确定性以及基于频数法的实际局限性。它还有利于在获得新的维护数据时不断更新估计值。最后，整个推论程序都是在 Microsoft Excel 中实现的，因此任何可靠性从业人员都可以轻松使用，无需学习复杂的编程语言。因此，这项研究支持对整个任务的可靠性进行持续、实时的评估，并有助于及早发现可靠性低于临界值的任何子系统。
{"title":"Bayesian estimation of the mean time between failures of subsystems with different causes using interval‐censored system maintenance data","authors":"David Han, James D. Brownlow, Jesse Thompson, Ralph G. Brooks","doi":"10.1002/qre.3606","DOIUrl":"https://doi.org/10.1002/qre.3606","url":null,"abstract":"Ensuring an acceptable level of reliability stands as a primary imperative for any mission‐focused operation since it serves as a critical determinant of success. Inadequate reliability can lead to severe repercussions, including substantial expenses for repairs and replacements, missed opportunities, service disruptions, and in the worst cases, safety violations and human casualties. Within national defense organizations such as the USAF, the precise assessment and maintenance of system reliability play a pivotal role in ensuring the success of mission‐critical operations. In this research, our primary objective is to model the reliability of repairable subsystems within the framework of competing and complementary risks. Subsequently, we construct the overall reliability of the entire repairable system, utilizing day‐to‐day group‐censored maintenance data from two identical aircraft systems. Assuming that the lifetimes of subsystems follow non‐identical exponential distributions, it is theoretically justified that the system reliability can be modeled by homogeneous Poisson processes even though the number of subsystems of any particular type is unknown and the temporal order of multiple subsystem failures within a given time interval is uncertain due to interval censoring. Using the proposed model, we formulate the likelihood function for the mean time between failures of subsystems with different causes, and subsequently establish an inferential procedure for the model parameters. Given a considerable number of parameters to estimate, we explore the efficacy of a Bayesian approach, treating the contractor‐supplied estimates as the hyperparameters of prior distributions. This approach mitigates potential model uncertainty as well as the practical limitation of a frequentist‐based approach. It also facilitates continuous updates of the estimates as new maintenance data become available. Finally, the entire inferential procedures were implemented in Microsoft Excel so that it is easy for any reliability practitioner to use without the need to learn sophisticated programming languages. Thus, this research supports an ongoing, real‐time assessment of the overall mission reliability and helps early detection of any subsystem whose reliability is below the threshold level.","PeriodicalId":56088,"journal":{"name":"Quality and Reliability Engineering International","volume":"72 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141503575","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Reliability engineering faces many of the same challenges today that it did at its inception in the 1950s. The fundamental issue remains uncertainty in system representation, specifically related to performance model structure and parameterization. Details of a design are unavailable early in the development process and therefore performance models must either account for the range of possibilities or be wrong. Increasing system complexity has compounded this uncertainty. In this work, we seek to understand how the reliability engineering literature has shifted over time. We exe cute a systematic literature review of 30,543 reliability engineering papers (covering roughly a third of the reliability papers indexed by Elsevier's Engineering Village. Topic modeling was performed on the abstracts of those papers to identify 279 topics. The hierarchical topic reduction resulted in the identification of eight top‐level method topics (prognostics, statistics, maintenance, quality control, management, physics of failure, modeling, and risk assessment) as well as three domain‐specific topics (nuclear, infrastructure, and software). We found that topics more associated with later phases in the development process (such as prognostics, maintenance, and quality control) have increased in popularity over time relative to other topics. We propose that this is a response to the challenges posed by model uncertainty and increasing complexity.
今天，可靠性工程面临着许多与二十世纪五十年代初创时相同的挑战。最根本的问题仍然是系统表示的不确定性，特别是与性能模型结构和参数化有关的不确定性。设计的细节在开发过程的早期无法获得，因此性能模型必须考虑到各种可能性，否则就是错误的。系统复杂性的增加加剧了这种不确定性。在这项工作中，我们试图了解可靠性工程文献是如何随着时间的推移而变化的。我们对 30,543 篇可靠性工程论文（约占 Elsevier's Engineering Village 索引的可靠性论文的三分之一）进行了系统的文献综述。我们对这些论文的摘要进行了主题建模，以确定 279 个主题。通过对主题进行分级，确定了八个顶级方法主题（预后、统计、维护、质量控制、管理、故障物理、建模和风险评估）以及三个特定领域主题（核、基础设施和软件）。我们发现，随着时间的推移，与开发过程后期阶段更相关的主题（如预测、维护和质量控制）相对于其他主题更受欢迎。我们认为，这是为了应对模型不确定性和复杂性增加所带来的挑战。
{"title":"Assessing changes in reliability methods over time: An unsupervised text mining approach","authors":"Charles K. Brown, Bruce G. Cameron","doi":"10.1002/qre.3596","DOIUrl":"https://doi.org/10.1002/qre.3596","url":null,"abstract":"Reliability engineering faces many of the same challenges today that it did at its inception in the 1950s. The fundamental issue remains uncertainty in system representation, specifically related to performance model structure and parameterization. Details of a design are unavailable early in the development process and therefore performance models must either account for the range of possibilities or be wrong. Increasing system complexity has compounded this uncertainty. In this work, we seek to understand how the reliability engineering literature has shifted over time. We exe cute a systematic literature review of 30,543 reliability engineering papers (covering roughly a third of the reliability papers indexed by Elsevier's Engineering Village. Topic modeling was performed on the abstracts of those papers to identify 279 topics. The hierarchical topic reduction resulted in the identification of eight top‐level method topics (prognostics, statistics, maintenance, quality control, management, physics of failure, modeling, and risk assessment) as well as three domain‐specific topics (nuclear, infrastructure, and software). We found that topics more associated with later phases in the development process (such as prognostics, maintenance, and quality control) have increased in popularity over time relative to other topics. We propose that this is a response to the challenges posed by model uncertainty and increasing complexity.","PeriodicalId":56088,"journal":{"name":"Quality and Reliability Engineering International","volume":"33 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141197308","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Estimating the reliability and maintainability (R & M) parameters is crucial in various industrial applications. It serves purposes such as evaluating system performance and safety, minimising the risk and cost of potential failures, and designing efficient maintenance strategies. This task becomes challenging for complex repairable systems, where failures can occur due to different causes and performance may be affected by various covariates (such as material, environment, and labour). Another challenge in R & M studies arises from the presence of censorship in failure times. Existing methodologies often fail to account for all the aforementioned aspects of system‐related data in R & M analysis. By incorporating valuable information from covariates and utilising data from censored failure times alongside complete failure data, the accuracy of R & M parameter estimation can be significantly improved. This paper develops reliability models for repairable systems with multiple failure causes in the presence of covariates. The system can also be subject to imperfect maintenance. The R & M parameters are then estimated by applying the Kijima Type I and II model's virtual age concept. The proposed technique is illustrated using two case studies on gas pipelines and aero‐engine systems. Through these case studies, we show that the proposed method not only provides more efficient estimates of the R & M parameters compared to the alternative approach, but it is also easier to apply and yields more straightforward interpretations.
在各种工业应用中，估算可靠性和可维护性（R & M）参数至关重要。它的作用包括评估系统性能和安全性，最大限度地降低潜在故障的风险和成本，以及设计高效的维护策略。对于复杂的可维护性系统来说，这项任务具有挑战性，因为发生故障的原因可能各不相同，系统性能也可能受到各种协变量（如材料、环境和劳动力）的影响。R & M 研究中的另一个挑战来自于故障时间的普查。现有的方法往往无法在 Ramp &; M 分析中考虑到系统相关数据的所有上述方面。通过将有价值的协变量信息纳入其中，并在利用完整故障数据的同时利用经过删减的故障时间数据，可以显著提高 R & M 参数估计的准确性。本文为存在协变量、具有多种故障原因的可修复系统建立了可靠性模型。系统还可能受到不完善维护的影响。然后应用 Kijima I 型和 II 型模型的虚拟年龄概念估算 R & M 参数。我们通过对天然气管道和航空发动机系统的两个案例研究来说明所提出的技术。通过这些案例研究，我们表明，与其他方法相比，拟议的方法不仅能更有效地估算 R & M 参数，而且更易于应用，并能产生更直接的解释。
{"title":"Reliability and maintainability estimation of a multi‐failure‐cause system under imperfect maintenance","authors":"Fatemeh Safaei, Sharareh Taghipour","doi":"10.1002/qre.3595","DOIUrl":"https://doi.org/10.1002/qre.3595","url":null,"abstract":"Estimating the reliability and maintainability (R & M) parameters is crucial in various industrial applications. It serves purposes such as evaluating system performance and safety, minimising the risk and cost of potential failures, and designing efficient maintenance strategies. This task becomes challenging for complex repairable systems, where failures can occur due to different causes and performance may be affected by various covariates (such as material, environment, and labour). Another challenge in R & M studies arises from the presence of censorship in failure times. Existing methodologies often fail to account for all the aforementioned aspects of system‐related data in R & M analysis. By incorporating valuable information from covariates and utilising data from censored failure times alongside complete failure data, the accuracy of R & M parameter estimation can be significantly improved. This paper develops reliability models for repairable systems with multiple failure causes in the presence of covariates. The system can also be subject to imperfect maintenance. The R & M parameters are then estimated by applying the Kijima Type I and II model's virtual age concept. The proposed technique is illustrated using two case studies on gas pipelines and aero‐engine systems. Through these case studies, we show that the proposed method not only provides more efficient estimates of the R & M parameters compared to the alternative approach, but it is also easier to apply and yields more straightforward interpretations.","PeriodicalId":56088,"journal":{"name":"Quality and Reliability Engineering International","volume":"50 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141189028","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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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