Failure Mode and Effect Analysis (FMEA) is acknowledged as a beneficial instrument for identifying and mitigating system failures. However, the traditional FMEA method has its limitations. For instance, crisp numbers fail to adequately represent the intricate information and cognitive nuances of experts. Additionally, the conventional approach overlooks the significance of weights assigned to FMEA experts and risk factors (RFs). Furthermore, the simplistic ranking of failure modes in traditional FMEA does not accurately reflect priorities. In light of these drawbacks, this paper introduces an innovative, fully data‐driven FMEA method, leveraging a probabilistic uncertain linguistic term sets (PULTSs) environment and the Weighted Aggregates Sum Product Assessment (WASPAS) method. In the assessment process, PULTSs serve as linguistic tools that express probability distribution, allowing for a more reasonable and precise description of information. To address the issue of weights for RFs, the regret theory and Modified CRITIC method are employed. Subsequently, the WASPAS method is applied to determine the risk rankings of failure modes. To illustrate the feasibility and rationality of this novel FMEA model, the paper includes an example involving the production of Lithium‐ion batteries. To emphasize the excellence of the proposed FMEA model, sensitivity and comparative analyses are carried out.
{"title":"A probabilistic uncertain linguistic approach for FMEA‐based risk assessment","authors":"Yingwei Tang, Dequn Zhou, Shichao Zhu, Linhan Ouyang","doi":"10.1002/qre.3657","DOIUrl":"https://doi.org/10.1002/qre.3657","url":null,"abstract":"Failure Mode and Effect Analysis (FMEA) is acknowledged as a beneficial instrument for identifying and mitigating system failures. However, the traditional FMEA method has its limitations. For instance, crisp numbers fail to adequately represent the intricate information and cognitive nuances of experts. Additionally, the conventional approach overlooks the significance of weights assigned to FMEA experts and risk factors (RFs). Furthermore, the simplistic ranking of failure modes in traditional FMEA does not accurately reflect priorities. In light of these drawbacks, this paper introduces an innovative, fully data‐driven FMEA method, leveraging a probabilistic uncertain linguistic term sets (PULTSs) environment and the Weighted Aggregates Sum Product Assessment (WASPAS) method. In the assessment process, PULTSs serve as linguistic tools that express probability distribution, allowing for a more reasonable and precise description of information. To address the issue of weights for RFs, the regret theory and Modified CRITIC method are employed. Subsequently, the WASPAS method is applied to determine the risk rankings of failure modes. To illustrate the feasibility and rationality of this novel FMEA model, the paper includes an example involving the production of Lithium‐ion batteries. To emphasize the excellence of the proposed FMEA model, sensitivity and comparative analyses are carried out.","PeriodicalId":56088,"journal":{"name":"Quality and Reliability Engineering International","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142178788","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}
While researchers and practitioners are seamlessly trying to develop methods for minimizing the effect of outliers in control charts, detecting and screening these outliers continue to pose serious challenges. Keeping in view, the researchers rely on robust estimators to modify the detection limits structure so that the chart can be more sensitive against outliers. In this study, we propose a robust control chart based on , , , , and estimators, whilst the process parameter is estimated from Phase‐I. Through intensive Monte‐Carlo simulations, the study presents how the estimation of parameter(s) and presence of outliers affect the efficacy of the chart, and then how the proposed outlier detectors bring the chart back to normalcy by restoring its efficacy and sensitivity. Average properties are used as the performance measures. The properties establish the superiority of the proposed scheme over and Tukey's outlier detectors. The applicability of the study includes the effectiveness of the proposed detectors in industrial data set but is not limited to manufacturing industries.
{"title":"A resilient S2 monitoring chart with novel outlier detectors","authors":"Ayesha Awais, Nadia Saeed","doi":"10.1002/qre.3658","DOIUrl":"https://doi.org/10.1002/qre.3658","url":null,"abstract":"While researchers and practitioners are seamlessly trying to develop methods for minimizing the effect of outliers in control charts, detecting and screening these outliers continue to pose serious challenges. Keeping in view, the researchers rely on robust estimators to modify the detection limits structure so that the chart can be more sensitive against outliers. In this study, we propose a robust control chart based on , , , , and estimators, whilst the process parameter is estimated from Phase‐I. Through intensive Monte‐Carlo simulations, the study presents how the estimation of parameter(s) and presence of outliers affect the efficacy of the chart, and then how the proposed outlier detectors bring the chart back to normalcy by restoring its efficacy and sensitivity. Average properties are used as the performance measures. The properties establish the superiority of the proposed scheme over and Tukey's outlier detectors. The applicability of the study includes the effectiveness of the proposed detectors in industrial data set but is not limited to manufacturing industries.","PeriodicalId":56088,"journal":{"name":"Quality and Reliability Engineering International","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142178801","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}
In recent years, there has been growing interest in employing predictive methods to forecast the remaining useful life of industrial equipment. However, the challenge lies in how to take advantage of the dynamic predictive information to facilitate the maintenance of decision‐making. This problem becomes particularly challenging for complex industrial systems consisting of multiple components with economic dependencies. This paper aims at providing an effective maintenance strategy for multi‐component systems based on predictive information, while considering economic dependencies among different system components. To this end, a dynamic predictive maintenance (PdM) strategy that minimizes the mean maintenance cost over a decision period is proposed, where both long‐term and short‐term policies are integrated into the decision‐making framework. Specifically, the long‐term policy is formulated using predictions derived from historical degradation data through a Long Short‐Term Memory (LSTM) model. Concurrently, real‐time monitoring data is employed to forecast imminent degradation in components, serving as a basis for determining the necessity of short‐term adjustments. This paper embeds the consideration of economic dependencies among components within the maintenance strategy design and employs hierarchical clustering to establish an effective and efficient maintenance grouping policy. The experimental results demonstrate that our proposed strategy significantly outperforms conventional approaches, including block‐based and age‐based maintenance, resulting in substantial cost savings. The proposed strategy is also compared with a similar version without grouping, and the results verify the added value of the optimal maintenance grouping policy in cost reduction. Moreover, a comprehensive analysis of the proposed method is provided, including the impact of different inspection costs and inspection intervals on maintenance decision‐making, which can provide insightful guidance to various PdM scenarios in practice.
{"title":"Dynamic predictive maintenance strategy for multi‐component system based on LSTM and hierarchical clustering","authors":"Lv Yaqiong, Zheng Pan, Li Yifan, Wang Xian","doi":"10.1002/qre.3656","DOIUrl":"https://doi.org/10.1002/qre.3656","url":null,"abstract":"In recent years, there has been growing interest in employing predictive methods to forecast the remaining useful life of industrial equipment. However, the challenge lies in how to take advantage of the dynamic predictive information to facilitate the maintenance of decision‐making. This problem becomes particularly challenging for complex industrial systems consisting of multiple components with economic dependencies. This paper aims at providing an effective maintenance strategy for multi‐component systems based on predictive information, while considering economic dependencies among different system components. To this end, a dynamic predictive maintenance (PdM) strategy that minimizes the mean maintenance cost over a decision period is proposed, where both long‐term and short‐term policies are integrated into the decision‐making framework. Specifically, the long‐term policy is formulated using predictions derived from historical degradation data through a Long Short‐Term Memory (LSTM) model. Concurrently, real‐time monitoring data is employed to forecast imminent degradation in components, serving as a basis for determining the necessity of short‐term adjustments. This paper embeds the consideration of economic dependencies among components within the maintenance strategy design and employs hierarchical clustering to establish an effective and efficient maintenance grouping policy. The experimental results demonstrate that our proposed strategy significantly outperforms conventional approaches, including block‐based and age‐based maintenance, resulting in substantial cost savings. The proposed strategy is also compared with a similar version without grouping, and the results verify the added value of the optimal maintenance grouping policy in cost reduction. Moreover, a comprehensive analysis of the proposed method is provided, including the impact of different inspection costs and inspection intervals on maintenance decision‐making, which can provide insightful guidance to various PdM scenarios in practice.","PeriodicalId":56088,"journal":{"name":"Quality and Reliability Engineering International","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142227597","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}
Monitoring the number of defects in constant‐size units is a well‐defined problem in the industrial domain and usually, the control chart is used for monitoring the total number of defects in a product or a sample of products. The c‐chart tracks the total number of defects in each case by assuming that the underlying number of defects (single or several different types of defects) follows approximately the Poisson distribution. An interesting class of problems where the ‐chart is used is when the number of defects in a surface is of interest. Although the number of defects on the surface of products characterizes the quality of the products, it is especially important how concentrated the defects are in specific parts of the product. In this paper, we introduce a scan‐based monitoring procedure, which simultaneously combines control charts for monitoring the evolvement of the number of defects (in general, events) through time and scan statistics for exploring the spatial distribution of defects. The numerical illustration showed that the new procedure has excellent performance under different scenarios.
{"title":"Monitoring defects on products' surface by incorporating scan statistics into process monitoring procedures","authors":"Sotirios Bersimis, Athanasios Sachlas, Polychronis Economou","doi":"10.1002/qre.3652","DOIUrl":"https://doi.org/10.1002/qre.3652","url":null,"abstract":"Monitoring the number of defects in constant‐size units is a well‐defined problem in the industrial domain and usually, the control chart is used for monitoring the total number of defects in a product or a sample of products. The <jats:italic>c</jats:italic>‐chart tracks the total number of defects in each case by assuming that the underlying number of defects (single or several different types of defects) follows approximately the Poisson distribution. An interesting class of problems where the ‐chart is used is when the number of defects in a surface is of interest. Although the number of defects on the surface of products characterizes the quality of the products, it is especially important how concentrated the defects are in specific parts of the product. In this paper, we introduce a scan‐based monitoring procedure, which simultaneously combines control charts for monitoring the evolvement of the number of defects (in general, events) through time and scan statistics for exploring the spatial distribution of defects. The numerical illustration showed that the new procedure has excellent performance under different scenarios.","PeriodicalId":56088,"journal":{"name":"Quality and Reliability Engineering International","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142178802","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}
Safety and reliability represent indispensable prerequisites for electric rudder systems (ERS), while health states recognition serves as a potent technology that fortifies and optimizes these essential aspects. To address this problem, we present a health‐state recognition muti‐class model BAFAO‐IPBT‐TWSVM for ERS considering several typical operating parameters obtained from intelligent electric rudder system test platform. The twin support vector machine (TWSVM) not only possesses the ability of traditional fault diagnosis methods based on SVM to handle unbalanced data, but also introduces two non‐parallel hyperplanes to replace single hyperplane of traditional SVM. Traditional TWSVM simplifies and streamlines the problem‐solving, but it is limited to binary classification problem. Therefore, the improved separability principle weighting intra‐class distance and inter‐class distance generates the best decision tree structure named improved partial binary tree (IPBT) is to effectively decompose multi‐classification problem into multiple binary classification problems. A novel intelligent algorithms called bat algorithm‐based fruit fly optimization algorithm (BAFOA) is utilized to self‐adaptively optimize the parameters of each sub‐classifier TWSVMi. This strategic integration makes the model more flexible in adapting to the characteristics of electric rudder system and enhances the accuracy and robustness of the model. The performance of the proposed model is validated under real‐world datasets by the results of health states recognition experiments. The Accuracy, Precision, TPR, TNR, F1‐score, G‐mean, and Kappa of the BAFOA‐IPBT‐TWSVM are 0.972, 0.987, 0.982, 0.959, 0.985, 0.970, and 0.954 respectively. The reserved BAFOA‐IPBT‐TWSVM is capable of recognizing the health status with preferable performance compared with other nine models, which could introduce a novel idea for future rudder maintenance approaches.
{"title":"Enhanced health states recognition for electric rudder system using optimized twin support vector machine","authors":"Chenxia Guo, Hao Qin, Ruifeng Yang","doi":"10.1002/qre.3643","DOIUrl":"https://doi.org/10.1002/qre.3643","url":null,"abstract":"Safety and reliability represent indispensable prerequisites for electric rudder systems (ERS), while health states recognition serves as a potent technology that fortifies and optimizes these essential aspects. To address this problem, we present a health‐state recognition muti‐class model BAFAO‐IPBT‐TWSVM for ERS considering several typical operating parameters obtained from intelligent electric rudder system test platform. The twin support vector machine (TWSVM) not only possesses the ability of traditional fault diagnosis methods based on SVM to handle unbalanced data, but also introduces two non‐parallel hyperplanes to replace single hyperplane of traditional SVM. Traditional TWSVM simplifies and streamlines the problem‐solving, but it is limited to binary classification problem. Therefore, the improved separability principle weighting intra‐class distance and inter‐class distance generates the best decision tree structure named improved partial binary tree (IPBT) is to effectively decompose multi‐classification problem into multiple binary classification problems. A novel intelligent algorithms called bat algorithm‐based fruit fly optimization algorithm (BAFOA) is utilized to self‐adaptively optimize the parameters of each sub‐classifier TWSVM<jats:sub>i</jats:sub>. This strategic integration makes the model more flexible in adapting to the characteristics of electric rudder system and enhances the accuracy and robustness of the model. The performance of the proposed model is validated under real‐world datasets by the results of health states recognition experiments. The Accuracy, Precision, TPR, TNR, F<jats:sub>1</jats:sub>‐score, G‐mean, and Kappa of the BAFOA‐IPBT‐TWSVM are 0.972, 0.987, 0.982, 0.959, 0.985, 0.970, and 0.954 respectively. The reserved BAFOA‐IPBT‐TWSVM is capable of recognizing the health status with preferable performance compared with other nine models, which could introduce a novel idea for future rudder maintenance approaches.","PeriodicalId":56088,"journal":{"name":"Quality and Reliability Engineering International","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142178803","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}
Wenhan Zhang, Xiaojun Zhu, Mu He, Balakrishnan Narayanaswamy
In this article, we consider test for the two null hypotheses for and , two widely useful tests in reliability, based on ranked set sampling (RSS). We derive the likelihood ratio test as well as the associated exact and asymptotic results. Considering a fixed significance level and power of the test, we show that the proposed test statistic outperforms the existing test. In small sample cases, the proposed test leads to a much narrower confidence interval for the reliability function . Then, the test statistics obtained from simple random sampling and RSS schemes are compared through which, the efficiency of using RSS is demonstrated. For illustration, we apply the proposed test to a degradation data from the reliability literature. Upon using RSS, the cost of measurement gets reduced and efficiency gets improved, suggesting the importance and use of RSS data in reliability experiments and their design.
{"title":"Reliability test for degradation data based on ranked set sampling","authors":"Wenhan Zhang, Xiaojun Zhu, Mu He, Balakrishnan Narayanaswamy","doi":"10.1002/qre.3654","DOIUrl":"https://doi.org/10.1002/qre.3654","url":null,"abstract":"In this article, we consider test for the two null hypotheses for and , two widely useful tests in reliability, based on ranked set sampling (RSS). We derive the likelihood ratio test as well as the associated exact and asymptotic results. Considering a fixed significance level and power of the test, we show that the proposed test statistic outperforms the existing test. In small sample cases, the proposed test leads to a much narrower confidence interval for the reliability function . Then, the test statistics obtained from simple random sampling and RSS schemes are compared through which, the efficiency of using RSS is demonstrated. For illustration, we apply the proposed test to a degradation data from the reliability literature. Upon using RSS, the cost of measurement gets reduced and efficiency gets improved, suggesting the importance and use of RSS data in reliability experiments and their design.","PeriodicalId":56088,"journal":{"name":"Quality and Reliability Engineering International","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142178804","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}
Chibuzo Solomon Ezievuo, Abimibola Victoria Oladugba, Oluwagbenga Tobi Babatunde
Orthogonal‐array composite designs (OACDs) and orthogonal‐uniform composite designs (OUCDs) are orthogonal composite designs that combine two‐level full or fractional factorial and three‐level orthogonal‐array/uniform designs for estimation of the linear, bilinear, and quadratic effects in a second‐order response surface model. In this study, the effects of missing one observation in the various design portions (factorial (f) axial (a) and center (c)), on the precision of parameter estimates, prediction variance and design efficiency of OACDs and OUCDs for 5 ≤ k ≤ 9 factors at different values of α (the distance of a non‐zero co‐ordinate in an additional design point from the center) are evaluated. The results showed that missing a factorial and an axial point have adverse effect on the precision of parameter estimates of OACDs and OUCDs, while missing a center point has little effect. Missing an axial point caused the highest effect on the prediction variance and design efficiencies. The FDS plots showed OACDs to be better designs for k ≤ 7 and OUCDs for k = 8 and 9 factors.
{"title":"Evaluation of orthogonal composite designs for second‐order model in presence of missing observation","authors":"Chibuzo Solomon Ezievuo, Abimibola Victoria Oladugba, Oluwagbenga Tobi Babatunde","doi":"10.1002/qre.3653","DOIUrl":"https://doi.org/10.1002/qre.3653","url":null,"abstract":"Orthogonal‐array composite designs (OACDs) and orthogonal‐uniform composite designs (OUCDs) are orthogonal composite designs that combine two‐level full or fractional factorial and three‐level orthogonal‐array/uniform designs for estimation of the linear, bilinear, and quadratic effects in a second‐order response surface model. In this study, the effects of missing one observation in the various design portions (factorial (<jats:italic>f</jats:italic>) axial (<jats:italic>a</jats:italic>) and center (<jats:italic>c</jats:italic>)), on the precision of parameter estimates, prediction variance and design efficiency of OACDs and OUCDs for 5 ≤ <jats:italic>k</jats:italic> ≤ 9 factors at different values of <jats:italic>α</jats:italic> (the distance of a non‐zero co‐ordinate in an additional design point from the center) are evaluated. The results showed that missing a factorial and an axial point have adverse effect on the precision of parameter estimates of OACDs and OUCDs, while missing a center point has little effect. Missing an axial point caused the highest effect on the prediction variance and design efficiencies. The FDS plots showed OACDs to be better designs for <jats:italic>k</jats:italic> ≤ 7 and OUCDs for <jats:italic>k </jats:italic>= 8 and 9 factors.","PeriodicalId":56088,"journal":{"name":"Quality and Reliability Engineering International","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142179104","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}
Carly E. Metcalfe, Bradley Jones, Douglas C. Montgomery
Nonregular fractional factorial designs are a preferable alternative to regular resolution IV designs because they avoid confounding 2‐factor interactions. As a result, nonregular designs can estimate and identify a few active 2‐factor interactions. However, due to the sometimes complex alias structure of nonregular designs, standard factor screening strategies can fail to identify all active effects. We report on a screening technique that takes advantage of the alias structure of these nonregular designs. This alias‐informed‐model‐selection (AIMS) technique has been used previously for a specific 6‐factor nonregular design. We show how the AIMS technique can be applied to 7‐ and 8‐factor nonregular designs, completing the exposition of this method for all 16‐run 2‐level designs that are viable alternatives to standard Resolution IV fractional factorial designs. We compare AIMS to three other standard analysis methods for nonregular designs, stepwise regression, the lasso, and the Dantzig selector. AIMS consistently outperforms these methods in identifying the set of active factors.
{"title":"Alias‐informed model selection (AIMS) for 7 and 8 factor no‐confounding 16‐run fractional factorial designs","authors":"Carly E. Metcalfe, Bradley Jones, Douglas C. Montgomery","doi":"10.1002/qre.3650","DOIUrl":"https://doi.org/10.1002/qre.3650","url":null,"abstract":"Nonregular fractional factorial designs are a preferable alternative to regular resolution IV designs because they avoid confounding 2‐factor interactions. As a result, nonregular designs can estimate and identify a few active 2‐factor interactions. However, due to the sometimes complex alias structure of nonregular designs, standard factor screening strategies can fail to identify all active effects. We report on a screening technique that takes advantage of the alias structure of these nonregular designs. This alias‐informed‐model‐selection (AIMS) technique has been used previously for a specific 6‐factor nonregular design. We show how the AIMS technique can be applied to 7‐ and 8‐factor nonregular designs, completing the exposition of this method for all 16‐run 2‐level designs that are viable alternatives to standard Resolution IV fractional factorial designs. We compare AIMS to three other standard analysis methods for nonregular designs, stepwise regression, the lasso, and the Dantzig selector. AIMS consistently outperforms these methods in identifying the set of active factors.","PeriodicalId":56088,"journal":{"name":"Quality and Reliability Engineering International","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142178805","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}
In the present age of industrial revolution where the smart manufacturing and smart production are being used in the developed countries, the production systems are still vulnerable to disruptions and outages that affect the reliability and availability of the systems. Certain scientific and probabilistic approaches are required to study the inter‐failure times and repair times and find the possible solutions for maximum production and minimum losses. In this article, we propose a bivariate Power Pareto distribution to model the inter‐failure times and repair times of a system along with some of its characteristics. The model parameters are estimated by employing the maximum likelihood estimation, Bayesian estimation and ant colony optimization algorithm. A simulation study is conducted for different sample sizes to assess the stability of the model parameters. Moreover, we derive the distribution of convolution from our proposed bivariate stochastic distribution and compute its quantiles that help to determine the total time of availability and recovery of a certain system. To demonstrate the efficacy of our model, we use real data set of inter‐failure times and repair times of a certain system.
在当今工业革命的时代，发达国家正在使用智能制造和智能生产，但生产系统仍然很容易受到干扰和中断的影响，从而影响系统的可靠性和可用性。需要采用一定的科学和概率方法来研究故障间隔时间和修复时间，并找到可能的解决方案，以实现最大产量和最小损失。在本文中，我们提出了一种双变量 Power Pareto 分布来模拟系统的故障间隔时间和修复时间以及系统的一些特征。模型参数通过最大似然估计、贝叶斯估计和蚁群优化算法进行估计。针对不同的样本量进行了模拟研究，以评估模型参数的稳定性。此外，我们从提出的双变量随机分布中推导出卷积分布，并计算出其量化值，这有助于确定某个系统的可用和恢复总时间。为了证明我们模型的有效性，我们使用了某个系统的故障间隔时间和修复时间的真实数据集。
{"title":"Assessment of reliability and availability of a system by using a bivariate stochastic model","authors":"Muhammad Mohsin, Aisha Bilal, Zulfiqar Ali","doi":"10.1002/qre.3655","DOIUrl":"https://doi.org/10.1002/qre.3655","url":null,"abstract":"In the present age of industrial revolution where the smart manufacturing and smart production are being used in the developed countries, the production systems are still vulnerable to disruptions and outages that affect the reliability and availability of the systems. Certain scientific and probabilistic approaches are required to study the inter‐failure times and repair times and find the possible solutions for maximum production and minimum losses. In this article, we propose a bivariate Power Pareto distribution to model the inter‐failure times and repair times of a system along with some of its characteristics. The model parameters are estimated by employing the maximum likelihood estimation, Bayesian estimation and ant colony optimization algorithm. A simulation study is conducted for different sample sizes to assess the stability of the model parameters. Moreover, we derive the distribution of convolution from our proposed bivariate stochastic distribution and compute its quantiles that help to determine the total time of availability and recovery of a certain system. To demonstrate the efficacy of our model, we use real data set of inter‐failure times and repair times of a certain system.","PeriodicalId":56088,"journal":{"name":"Quality and Reliability Engineering International","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142178806","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}
Condition‐based maintenance (CBM) has gradually gained more attention, and the degradation process has been increasingly applied to maintenance optimization models. The insufficient data and the complex degradation process of the equipment conditions will contribute to epistemic uncertainty. Besides, the implementation of maintenance introduces oscillatory features into the equipment degradation process, deviating from a monotonically decreasing trend, complicating the optimization of CBM. In this article, to simultaneously address the problem of epistemic uncertainty and consider the influence of inspection and maintenance, we establish a new type of degradation model based on uncertainty theory to deal with epistemic uncertainty. Then an uncertain maintenance optimization model is proposed to give an optimal CBM strategy. Finally, a case study is provided to illustrate the proposed CBM optimization method.
{"title":"A condition‐based maintenance optimization method with oscillating uncertain degradation process","authors":"Shuyu Li, Meilin Wen, Tianpei Zu, Rui Kang","doi":"10.1002/qre.3648","DOIUrl":"https://doi.org/10.1002/qre.3648","url":null,"abstract":"Condition‐based maintenance (CBM) has gradually gained more attention, and the degradation process has been increasingly applied to maintenance optimization models. The insufficient data and the complex degradation process of the equipment conditions will contribute to epistemic uncertainty. Besides, the implementation of maintenance introduces oscillatory features into the equipment degradation process, deviating from a monotonically decreasing trend, complicating the optimization of CBM. In this article, to simultaneously address the problem of epistemic uncertainty and consider the influence of inspection and maintenance, we establish a new type of degradation model based on uncertainty theory to deal with epistemic uncertainty. Then an uncertain maintenance optimization model is proposed to give an optimal CBM strategy. Finally, a case study is provided to illustrate the proposed CBM optimization method.","PeriodicalId":56088,"journal":{"name":"Quality and Reliability Engineering International","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142178807","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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