Qian He, Weian Yan, Weidong Liu, David Bigaud, Xiaofan Xu, Zitong Lei
The step stress accelerated degradation test (SSADT) is an effective tool for assessing the reliability of highly reliable products. However, conducting an SSADT is expensive and time consuming, and the obtained SSADT data has an impact on the accuracy of the subsequent product reliability index estimations. Consequently, devising a cost‐constrained SSADT plan that yields high‐precision reliability estimates poses a significant challenge. This paper focuses on the optimal design of SSADT for the Tweedie exponential dispersion process with random effect (TEDR), a general degradation model capable of describing product heterogeneity. Under given budget and boundary constraints, the optimal sample size, observation frequency and observation times at each stress level are obtained by minimizing the asymptotic variance of the estimated quantile life at normal operating conditions. The sensitivity and stability of the SSADT plan are also studied, and the results indicate the robustness of the optimal plan against slight parameters fluctuations. We use the expectation maximization (EM) algorithm to estimate TEDR parameters and reliability indicators under SSADT, providing a systematic method for obtaining the optimal SSADT plan under budget constraints. The proposed framework is illustrated using the case of LED chips data, showcasing its potential for practical application.
{"title":"Optimal planning of step‐stress accelerated degradation test based on Tweedie exponential dispersion process with random effects","authors":"Qian He, Weian Yan, Weidong Liu, David Bigaud, Xiaofan Xu, Zitong Lei","doi":"10.1002/qre.3561","DOIUrl":"https://doi.org/10.1002/qre.3561","url":null,"abstract":"The step stress accelerated degradation test (SSADT) is an effective tool for assessing the reliability of highly reliable products. However, conducting an SSADT is expensive and time consuming, and the obtained SSADT data has an impact on the accuracy of the subsequent product reliability index estimations. Consequently, devising a cost‐constrained SSADT plan that yields high‐precision reliability estimates poses a significant challenge. This paper focuses on the optimal design of SSADT for the Tweedie exponential dispersion process with random effect (TEDR), a general degradation model capable of describing product heterogeneity. Under given budget and boundary constraints, the optimal sample size, observation frequency and observation times at each stress level are obtained by minimizing the asymptotic variance of the estimated quantile life at normal operating conditions. The sensitivity and stability of the SSADT plan are also studied, and the results indicate the robustness of the optimal plan against slight parameters fluctuations. We use the expectation maximization (EM) algorithm to estimate TEDR parameters and reliability indicators under SSADT, providing a systematic method for obtaining the optimal SSADT plan under budget constraints. The proposed framework is illustrated using the case of LED chips data, showcasing its potential for practical application.","PeriodicalId":56088,"journal":{"name":"Quality and Reliability Engineering International","volume":"20 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140627407","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}
Qian‐Dong Feng, Liang‐Jun Wen, Cai‐Xia Jiang, Xue‐Liang Wang
Hydroelastic effects are particularly pronounced in ultralarge container ships due to their substantial size and high velocity. The computation of combined torsional and bending moments presents a challenge, exacerbated by warping effects. This study focuses on the investigation of wave loads, encompassing vertical, horizontal, and torsional moments, through model tests on a 20,000 TEU container ship. A 1/77 scale model featuring an open U‐shaped backbone was crafted to simultaneously simulate and measure longitudinal, horizontal, and torsional stiffness, along with these loads. Wave loads on the hull, including those resulting from springing effects, were calculated in regular waves, employing hydroelastic theory and accounting for hydrodynamic forces. The study also delves into the characteristics and analysis of spring effects within the wave loads on the hull. The insights garnered from this research contribute to the fatigue analysis and safety assessments of ship structures.
超大型集装箱船由于体积大、速度快，水弹性效应尤为明显。扭转力矩和弯曲力矩的综合计算是一项挑战，而翘曲效应又加剧了这一挑战。本研究通过对一艘 20,000 TEU 集装箱船进行模型试验，重点研究波浪载荷，包括垂直力矩、水平力矩和扭转力矩。我们制作了一个 1/77 比例的模型，其特点是采用开放式 U 型骨架，可同时模拟和测量纵向、水平和扭转刚度以及这些载荷。采用流体弹性理论并考虑到流体动力，计算了在规则波浪中船体上的波浪载荷，包括弹簧效应产生的载荷。研究还深入探讨了波浪载荷对船体产生的弹簧效应的特征和分析。这项研究获得的见解有助于船舶结构的疲劳分析和安全评估。
{"title":"Springing loads analysis of large‐scale container ships in regular waves","authors":"Qian‐Dong Feng, Liang‐Jun Wen, Cai‐Xia Jiang, Xue‐Liang Wang","doi":"10.1002/qre.3558","DOIUrl":"https://doi.org/10.1002/qre.3558","url":null,"abstract":"Hydroelastic effects are particularly pronounced in ultralarge container ships due to their substantial size and high velocity. The computation of combined torsional and bending moments presents a challenge, exacerbated by warping effects. This study focuses on the investigation of wave loads, encompassing vertical, horizontal, and torsional moments, through model tests on a 20,000 TEU container ship. A 1/77 scale model featuring an open U‐shaped backbone was crafted to simultaneously simulate and measure longitudinal, horizontal, and torsional stiffness, along with these loads. Wave loads on the hull, including those resulting from springing effects, were calculated in regular waves, employing hydroelastic theory and accounting for hydrodynamic forces. The study also delves into the characteristics and analysis of spring effects within the wave loads on the hull. The insights garnered from this research contribute to the fatigue analysis and safety assessments of ship structures.","PeriodicalId":56088,"journal":{"name":"Quality and Reliability Engineering International","volume":"25 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140627139","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 turbine disc plays a crucial role in aerospace engine, significantly influencing both their fatigue life and overall reliability. Due to the substantial uncertainties present, devising a method for the probabilistic analysis of turbine disc fatigue life becomes crucial. This paper introduces a novel framework that combines adaptive Kriging Monte Carlo simulation (AK‐MCS) with a mean stress correction model to evaluate fatigue life distribution effectively. AK‐MCS, stands out as an algorithm that constructs a Kriging model, effectively reducing the need for an extensive finite element analysis. Furthermore, a mean stress correction model is proposed for the Masson‐Coffin equation tailored for fatigue life prediction of GH4133 disc material. This model has been empirically validated to be effective. The presented framework for probabilistic fatigue life analysis not only holds considerable engineering value but also offers innovative approaches for tackling analogous challenges in the field.
{"title":"An AK‐MCS‐based probabilistic fatigue life prediction framework for turbine disc with a mean stress correction model","authors":"Yun Wang, Yan‐Feng Li, Hong‐Zhong Huang, Song Bai","doi":"10.1002/qre.3556","DOIUrl":"https://doi.org/10.1002/qre.3556","url":null,"abstract":"The turbine disc plays a crucial role in aerospace engine, significantly influencing both their fatigue life and overall reliability. Due to the substantial uncertainties present, devising a method for the probabilistic analysis of turbine disc fatigue life becomes crucial. This paper introduces a novel framework that combines adaptive Kriging Monte Carlo simulation (AK‐MCS) with a mean stress correction model to evaluate fatigue life distribution effectively. AK‐MCS, stands out as an algorithm that constructs a Kriging model, effectively reducing the need for an extensive finite element analysis. Furthermore, a mean stress correction model is proposed for the Masson‐Coffin equation tailored for fatigue life prediction of GH4133 disc material. This model has been empirically validated to be effective. The presented framework for probabilistic fatigue life analysis not only holds considerable engineering value but also offers innovative approaches for tackling analogous challenges in the field.","PeriodicalId":56088,"journal":{"name":"Quality and Reliability Engineering International","volume":"239 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140565823","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}
A novel maintenance policy for a two‐component warm standby system with multiple standby states is presented in this paper. Two standby states, that is, cold and warm standby, for components in the system are considered. Components can realize the transition from the cold standby to the warm standby state by periodic switching, intended to shorten recovery time and save system operating costs. Preventive maintenance (PM) and preventive switching (PS) of components are considered. In the PS strategy, the standby component can switch before the operating component fails. In addition, additional standby failure modes based on idle time are studied. Derive the long‐term average cost of the system through the semi‐regenerative process. A numerical example eventually verifies the feasibility of this paper's proposed maintenance and switching strategy.
{"title":"Optimal maintenance policy considering imperfect switching for a multi‐state warm standby system","authors":"Fa‐Qun Qi, Yun‐Ke Wang, Hong‐Zhong Huang","doi":"10.1002/qre.3550","DOIUrl":"https://doi.org/10.1002/qre.3550","url":null,"abstract":"A novel maintenance policy for a two‐component warm standby system with multiple standby states is presented in this paper. Two standby states, that is, cold and warm standby, for components in the system are considered. Components can realize the transition from the cold standby to the warm standby state by periodic switching, intended to shorten recovery time and save system operating costs. Preventive maintenance (PM) and preventive switching (PS) of components are considered. In the PS strategy, the standby component can switch before the operating component fails. In addition, additional standby failure modes based on idle time are studied. Derive the long‐term average cost of the system through the semi‐regenerative process. A numerical example eventually verifies the feasibility of this paper's proposed maintenance and switching strategy.","PeriodicalId":56088,"journal":{"name":"Quality and Reliability Engineering International","volume":"49 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140566038","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 complex systems with multiple variables monitored at high‐frequency, variables are not only temporally autocorrelated, but they may also be nonlinearly related or exhibit nonstationarity as the inputs or operation changes. One approach to handling such variables is to detrend them prior to monitoring and then apply control charts that assume independence and stationarity to the residuals. Monitoring controlled systems is even more challenging because the control strategy seeks to maintain variables at prespecified mean levels, and to compensate, correlations among variables may change, making monitoring the covariance essential. In this paper, a vector autoregressive model (VAR) is compared with a multivariate random forest (MRF) and a neural network (NN) for detrending multivariate time series prior to monitoring the covariance of the residuals using a multivariate exponentially weighted moving average (MEWMA) control chart. Machine learning models have an advantage when the data's structure is unknown or may change. We design a novel simulation study with nonlinear, nonstationary, and autocorrelated data to compare the different detrending models and subsequent covariance monitoring. The machine learning models have superior performance for nonlinear and strongly autocorrelated data and similar performance for linear data. An illustration with data from a reverse osmosis process is given.
{"title":"Monitoring covariance in multivariate time series: Comparing machine learning and statistical approaches","authors":"Derek Weix, Tzahi Y. Cath, Amanda S. Hering","doi":"10.1002/qre.3551","DOIUrl":"https://doi.org/10.1002/qre.3551","url":null,"abstract":"In complex systems with multiple variables monitored at high‐frequency, variables are not only temporally autocorrelated, but they may also be nonlinearly related or exhibit nonstationarity as the inputs or operation changes. One approach to handling such variables is to detrend them prior to monitoring and then apply control charts that assume independence and stationarity to the residuals. Monitoring controlled systems is even more challenging because the control strategy seeks to maintain variables at prespecified mean levels, and to compensate, correlations among variables may change, making monitoring the covariance essential. In this paper, a vector autoregressive model (VAR) is compared with a multivariate random forest (MRF) and a neural network (NN) for detrending multivariate time series prior to monitoring the covariance of the residuals using a multivariate exponentially weighted moving average (MEWMA) control chart. Machine learning models have an advantage when the data's structure is unknown or may change. We design a novel simulation study with nonlinear, nonstationary, and autocorrelated data to compare the different detrending models and subsequent covariance monitoring. The machine learning models have superior performance for nonlinear and strongly autocorrelated data and similar performance for linear data. An illustration with data from a reverse osmosis process is given.","PeriodicalId":56088,"journal":{"name":"Quality and Reliability Engineering International","volume":"197 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140565950","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}
Qian Qian Zhao, Jae Yoon Yoo, Tadashi Dohi, Won Young Yun
This paper deals with an optimization problem of determining the maintenance units and age‐based inspection schemes to minimize the expected maintenance cost rate and satisfy a target system availability for multi‐indenture systems that have a modular structure with redundant units. Failures of primary components directly lead to system failures and can therefore be identified and maintained immediately, while failures of redundant components need to be identified by inspections. And the designated maintenance units need to be replaced during maintenance. By constructing the dual structure of the fault tree, we present a failure path enumeration method, based on which the system availability and expected maintenance cost rate models considering maintenance time are derived. For redundant units, two types of inspection policies are considered in our models: one is the corrective maintenance (CM)‐based inspection policy, and the other one is the age‐based inspection (PM) policy, inspections are performed at failures or age h, depending on which occurred earlier. A heuristic algorithm is proposed to find the reasonable maintenance units and inspection intervals simultaneously, and its performance is also compared and analyzed. Several numerical examples are studied to investigate the effect of parameters on optimal solutions.
本文讨论的是一个优化问题，即如何确定维护单元和基于年龄的检查方案，以最大限度地降低预期维护成本率，并满足具有冗余单元模块结构的多诱饵系统的目标系统可用性。主要组件的故障会直接导致系统故障，因此可以立即识别和维护，而冗余组件的故障则需要通过检查来识别。而指定的维护单元则需要在维护过程中进行更换。通过构建故障树的二元结构，我们提出了一种故障路径枚举法，并在此基础上推导出考虑维护时间的系统可用性和预期维护成本率模型。对于冗余单元，我们的模型考虑了两种检查策略：一种是基于纠正性维护（CM）的检查策略，另一种是基于年龄的检查（PM）策略，在故障或年龄 h 时进行检查，具体取决于哪个发生得更早。本文提出了一种启发式算法来同时找到合理的维护单位和检查间隔，并对其性能进行了比较和分析。研究了几个数值示例，以探讨参数对最优解的影响。
{"title":"Optimum maintenance units in multi‐indenture systems","authors":"Qian Qian Zhao, Jae Yoon Yoo, Tadashi Dohi, Won Young Yun","doi":"10.1002/qre.3549","DOIUrl":"https://doi.org/10.1002/qre.3549","url":null,"abstract":"This paper deals with an optimization problem of determining the maintenance units and age‐based inspection schemes to minimize the expected maintenance cost rate and satisfy a target system availability for multi‐indenture systems that have a modular structure with redundant units. Failures of primary components directly lead to system failures and can therefore be identified and maintained immediately, while failures of redundant components need to be identified by inspections. And the designated maintenance units need to be replaced during maintenance. By constructing the dual structure of the fault tree, we present a failure path enumeration method, based on which the system availability and expected maintenance cost rate models considering maintenance time are derived. For redundant units, two types of inspection policies are considered in our models: one is the corrective maintenance (CM)‐based inspection policy, and the other one is the age‐based inspection (PM) policy, inspections are performed at failures or age <jats:italic>h</jats:italic>, depending on which occurred earlier. A heuristic algorithm is proposed to find the reasonable maintenance units and inspection intervals simultaneously, and its performance is also compared and analyzed. Several numerical examples are studied to investigate the effect of parameters on optimal solutions.","PeriodicalId":56088,"journal":{"name":"Quality and Reliability Engineering International","volume":"27 1 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140565953","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}
Tudi Huang, Ying Zeng, Yan‐Feng Li, Jun Liu, Hong‐Zhong Huang
Dynamic fault tree (DFT) is a reliable method for modeling complex systems with dynamic characteristics. However, evaluating the reliability of such systems can be complex. The sequential binary decision diagram (SBDD) method simplifies this process by reducing computational complexity. This study investigates factors influencing SBDD size and proposes a new method, the complete priority ordering method, for ordering basic events in SBDDs. Results demonstrate the method's effectiveness in comparison to existing approaches.
{"title":"A new ordering method of basic events in dynamic fault tree for sequential binary decision diagram","authors":"Tudi Huang, Ying Zeng, Yan‐Feng Li, Jun Liu, Hong‐Zhong Huang","doi":"10.1002/qre.3555","DOIUrl":"https://doi.org/10.1002/qre.3555","url":null,"abstract":"Dynamic fault tree (DFT) is a reliable method for modeling complex systems with dynamic characteristics. However, evaluating the reliability of such systems can be complex. The sequential binary decision diagram (SBDD) method simplifies this process by reducing computational complexity. This study investigates factors influencing SBDD size and proposes a new method, the complete priority ordering method, for ordering basic events in SBDDs. Results demonstrate the method's effectiveness in comparison to existing approaches.","PeriodicalId":56088,"journal":{"name":"Quality and Reliability Engineering International","volume":"20 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140566019","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 this paper, we have introduced the concept of a new multicomponent reliability function Estimation procedures are developed for under progressive type II censoring scheme, which is subsequently used to develop the estimation procedures for the multicomponent stress‐strength reliability function . Herein, we consider a system having k statistically independent and identically distributed strength components, , say, which are exposed to a common random stress Y. Both strength and stress variables are independent and follow different scaled proportional hazard rate family of distributions with common scale parameter. The reliability functions and are estimated by using maximum likelihood and Bayesian methods of estimation. The performances of these estimators are studied on the basis of their mean squared errors through Monte Carlo simulation technique. Finally, the utility of the proposed estimators is examined and explained through two different real data sets.
本文引入了新的多分量可靠性函数的概念，并开发了渐进式 II 型删减方案下的估算程序，随后用于开发多分量应力-强度可靠性函数的估算程序。在这里，我们考虑一个系统，该系统有 k 个在统计上独立且同分布的强度分量，例如 ，这些分量暴露在一个共同的随机应力 Y 下。采用最大似然法和贝叶斯估计法对可靠性函数和进行估计。通过蒙特卡罗模拟技术，根据其均值平方误差研究了这些估计方法的性能。最后，通过两个不同的真实数据集对所提出的估计方法的实用性进行了检验和解释。
{"title":"Efficient estimation of the reliability functions in a multicomponent stress‐strength set‐up for a generalized family of distributions under progressive type II censoring","authors":"Taruna Kumari, Anupam Pathak","doi":"10.1002/qre.3547","DOIUrl":"https://doi.org/10.1002/qre.3547","url":null,"abstract":"In this paper, we have introduced the concept of a new multicomponent reliability function Estimation procedures are developed for under progressive type II censoring scheme, which is subsequently used to develop the estimation procedures for the multicomponent stress‐strength reliability function . Herein, we consider a system having <jats:italic>k</jats:italic> statistically independent and identically distributed strength components, , say, which are exposed to a common random stress <jats:italic>Y</jats:italic>. Both strength and stress variables are independent and follow different scaled proportional hazard rate family of distributions with common scale parameter. The reliability functions and are estimated by using maximum likelihood and Bayesian methods of estimation. The performances of these estimators are studied on the basis of their mean squared errors through Monte Carlo simulation technique. Finally, the utility of the proposed estimators is examined and explained through two different real data sets.","PeriodicalId":56088,"journal":{"name":"Quality and Reliability Engineering International","volume":"34 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140565941","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 zero‐inflated Conway‐Maxwell Poisson (ZICMP) distribution models count data with many zero observations. ZICMP model has been developed assuming that zero observations exist with probability and the number of non‐conformities in a product unit follows the Conway‐Maxwell Poisson (COM‐Poisson) distribution with location parameter and dispersion parameter . This article presents four kinds of cumulative sum (CUSUM) charts for monitoring upward shifts in a ZICMP process. Three CUSUM schemes, namely ‐CUSUM, ‐CUSUM, and ‐CUSUM, have been designed to detect shift only in one parameter assuming that the other two are fixed and one CUSUM scheme, namely ‐CUSUM, has been designed to detect shifts in all the parameters. The performance of the proposed charts has been evaluated in terms of the average run‐length (ARL). Finally, a numerical example is given to demonstrate the application of the proposed charts.
{"title":"Cumulative sum control charts for monitoring zero‐inflated COM‐Poisson processes","authors":"Konstantinos A. Tasias, Vasileios Alevizakos","doi":"10.1002/qre.3554","DOIUrl":"https://doi.org/10.1002/qre.3554","url":null,"abstract":"The zero‐inflated Conway‐Maxwell Poisson (ZICMP) distribution models count data with many zero observations. ZICMP model has been developed assuming that zero observations exist with probability and the number of non‐conformities in a product unit follows the Conway‐Maxwell Poisson (COM‐Poisson) distribution with location parameter and dispersion parameter . This article presents four kinds of cumulative sum (CUSUM) charts for monitoring upward shifts in a ZICMP process. Three CUSUM schemes, namely ‐CUSUM, ‐CUSUM, and ‐CUSUM, have been designed to detect shift only in one parameter assuming that the other two are fixed and one CUSUM scheme, namely ‐CUSUM, has been designed to detect shifts in all the parameters. The performance of the proposed charts has been evaluated in terms of the average run‐length (ARL). Finally, a numerical example is given to demonstrate the application of the proposed charts.","PeriodicalId":56088,"journal":{"name":"Quality and Reliability Engineering International","volume":"1 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140565828","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 study examines reliability of equipment (RoE) through three approaches: equipment design reliability, human reliability (HR), and maintenance‐based reliability. HR plays a key role in minimizing human error (HE) and subsequently enhancing RoE. Given that equipment reliability is influenced by numerous factors, these technologies come with various constraints, multiple outputs, and inputs. Compared to mathematical programming and analytical models, simulation methods in the field of reliability are relatively limited. However, system dynamics (SD) modeling is well‐suited to capture dynamics and complexity of systems, making it a valuable tool for long‐term strategic decision‐making. In this study, a combination of SD and regression approaches has been employed to explore the relationship between reliability and key variables such as HE and profit. Initially, the variables influencing reliability are identified, and then SD is utilized to understand the processes and interactions among these variables. Furthermore, linear regression is employed to establish the relationship between affective variables, reliability, and HE. To validate the results obtained from the proposed method, a sensitivity analysis is conducted. The results demonstrate the effectiveness of the proposed model. Simulation results indicate that implementing policies such as employee training and preventive maintenance significantly enhances RoE, leading to increased sales and profits for the organization. Therefore, managers should prioritize these variables and allocate adequate attention and resources to them.
本研究通过三种方法研究设备的可靠性（RoE）：设备设计可靠性、人的可靠性（HR）和基于维护的可靠性。人的可靠性在最大限度地减少人为错误（HE）和提高 RoE 方面发挥着关键作用。鉴于设备可靠性受多种因素影响，这些技术具有各种制约因素、多种输出和输入。与数学编程和分析模型相比，可靠性领域的模拟方法相对有限。然而，系统动力学（SD）建模非常适合捕捉系统的动态性和复杂性，使其成为长期战略决策的重要工具。在本研究中，采用了 SD 与回归相结合的方法来探讨可靠性与 HE 和利润等关键变量之间的关系。首先，确定影响可靠性的变量，然后利用自变量来了解这些变量之间的过程和相互作用。此外，还采用线性回归法来确定情感变量、可靠性和 HE 之间的关系。为了验证所提方法得出的结果，进行了敏感性分析。结果表明了建议模型的有效性。模拟结果表明，实施员工培训和预防性维护等政策可显著提高 RoE，从而增加组织的销售额和利润。因此，管理者应优先考虑这些变量，并为其分配足够的关注和资源。
{"title":"Presenting a new model for evaluating the factors affecting equipment reliability using system dynamics","authors":"Azam Modares, Alireza Pooya, Vahideh Bafandegan Emroozi, Pardis Roozkhosh","doi":"10.1002/qre.3553","DOIUrl":"https://doi.org/10.1002/qre.3553","url":null,"abstract":"This study examines reliability of equipment (RoE) through three approaches: equipment design reliability, human reliability (HR), and maintenance‐based reliability. HR plays a key role in minimizing human error (HE) and subsequently enhancing RoE. Given that equipment reliability is influenced by numerous factors, these technologies come with various constraints, multiple outputs, and inputs. Compared to mathematical programming and analytical models, simulation methods in the field of reliability are relatively limited. However, system dynamics (SD) modeling is well‐suited to capture dynamics and complexity of systems, making it a valuable tool for long‐term strategic decision‐making. In this study, a combination of SD and regression approaches has been employed to explore the relationship between reliability and key variables such as HE and profit. Initially, the variables influencing reliability are identified, and then SD is utilized to understand the processes and interactions among these variables. Furthermore, linear regression is employed to establish the relationship between affective variables, reliability, and HE. To validate the results obtained from the proposed method, a sensitivity analysis is conducted. The results demonstrate the effectiveness of the proposed model. Simulation results indicate that implementing policies such as employee training and preventive maintenance significantly enhances RoE, leading to increased sales and profits for the organization. Therefore, managers should prioritize these variables and allocate adequate attention and resources to them.","PeriodicalId":56088,"journal":{"name":"Quality and Reliability Engineering International","volume":"239 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140565830","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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