Song Bai, Ying Zeng, Tudi Huang, Yan‐Feng Li, Hong‐Zhong Huang
The load history exerts a considerable impact on the low cycle fatigue (LCF) life of turbine discs. Thus, oversimplifying the load history leads to substantial errors in fatigue life prediction. This study introduces a probabilistic fatigue life prediction method for turbine discs, accounting for the randomness inherent in LCF load history. The method involves quantifying the randomness of load history through numerical simulation and employing a surrogate model enhanced with learning functions to balance computational efficiency and accuracy. The probabilistic LCF life prediction of full‐scale turbine disc was conducted, demonstrating that the fatigue life scatter predicted by the proposed method more closely aligns with experimental data compared to the original approach. By refining the numerical simulation process, the proposed method better accounts for uncertainties in load history while maintaining computational efficiency, offering significant insights for the fatigue reliability design of turbine discs.
{"title":"Probabilistic LCF life prediction framework for turbine discs considering random load history","authors":"Song Bai, Ying Zeng, Tudi Huang, Yan‐Feng Li, Hong‐Zhong Huang","doi":"10.1002/qre.3582","DOIUrl":"https://doi.org/10.1002/qre.3582","url":null,"abstract":"The load history exerts a considerable impact on the low cycle fatigue (LCF) life of turbine discs. Thus, oversimplifying the load history leads to substantial errors in fatigue life prediction. This study introduces a probabilistic fatigue life prediction method for turbine discs, accounting for the randomness inherent in LCF load history. The method involves quantifying the randomness of load history through numerical simulation and employing a surrogate model enhanced with learning functions to balance computational efficiency and accuracy. The probabilistic LCF life prediction of full‐scale turbine disc was conducted, demonstrating that the fatigue life scatter predicted by the proposed method more closely aligns with experimental data compared to the original approach. By refining the numerical simulation process, the proposed method better accounts for uncertainties in load history while maintaining computational efficiency, offering significant insights for the fatigue reliability design of turbine discs.","PeriodicalId":56088,"journal":{"name":"Quality and Reliability Engineering International","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140968295","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}
Wind power fluctuation significantly impacts the safe and stable operation of the wind farm power grid. As the installed capacity of grid‐connected wind power expands to a certain threshold, these fluctuations can detrimentally affect the wind farm's operations. Consequently, wind power prediction emerges as a critical technology for ensuring safe, stable and efficient wind power generation. To optimize power grid dispatching and enhance wind farm operation and maintenance, precise wind power prediction is essential. In this context, we introduce a joint deep learning model that integrates a compact pyramid structure with a residual attention encoder, aiming to bolster wind farm operational safety and reliability. The model employs a compact pyramid architecture to extract multi‐time scale features from the input sequence, facilitating effective information exchange across different scales and enhancing the capture of long‐term sequence dependencies. To mitigate vanishing gradients, the residual transformer encoder is applied, augmenting the original attention mechanism with a global dot product attention pathway. This approach improves the gradient descent process, making it more accessible without introducing additional hyperparameters. The model's efficacy is validated using a dataset from an actual wind farm in China. Experimental outcomes reveal a notable enhancement in wind power prediction accuracy, thereby contributing to the operational safety of wind farms.
{"title":"A pyramidal residual attention model of short‐term wind power forecasting for wind farm safety","authors":"Hai‐Kun Wang, Jiahui Du, Danyang Li, Feng Chen","doi":"10.1002/qre.3562","DOIUrl":"https://doi.org/10.1002/qre.3562","url":null,"abstract":"Wind power fluctuation significantly impacts the safe and stable operation of the wind farm power grid. As the installed capacity of grid‐connected wind power expands to a certain threshold, these fluctuations can detrimentally affect the wind farm's operations. Consequently, wind power prediction emerges as a critical technology for ensuring safe, stable and efficient wind power generation. To optimize power grid dispatching and enhance wind farm operation and maintenance, precise wind power prediction is essential. In this context, we introduce a joint deep learning model that integrates a compact pyramid structure with a residual attention encoder, aiming to bolster wind farm operational safety and reliability. The model employs a compact pyramid architecture to extract multi‐time scale features from the input sequence, facilitating effective information exchange across different scales and enhancing the capture of long‐term sequence dependencies. To mitigate vanishing gradients, the residual transformer encoder is applied, augmenting the original attention mechanism with a global dot product attention pathway. This approach improves the gradient descent process, making it more accessible without introducing additional hyperparameters. The model's efficacy is validated using a dataset from an actual wind farm in China. Experimental outcomes reveal a notable enhancement in wind power prediction accuracy, thereby contributing to the operational safety of wind farms.","PeriodicalId":56088,"journal":{"name":"Quality and Reliability Engineering International","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140937395","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}
Exploring on reliability modeling and analysis on a marine equipment in a dynamic environment is a meaningful and challenging issue, because the system commonly carries out the task at sea away from land and suffers a distinct influence of environment. Thus, a reliability model of a multi‐state repairable system operating in dynamic environment is developed by introducing the background of the marine power system in this paper. The novelty of the research lies in the modeling and computing methods are relatively innovative by employing the aggregated stochastic processes, Hadamard production and matrix‐analytic method. First, the working modes of the marine power system under several different kinds of conditions are introduced. Then, the evolution of both the system states and environment are described as discrete‐time Markov chains with multiple and different transition probability matrices. The failure probability and repair probability of components are also distinct in different environments. Furthermore, some performance indexes, especially the index relevant to the environment, are derived, respectively. Finally, the conclusion is obtained by a numerical example of the marine power system, which also illustrates the validity and applicability of the proposed model.
{"title":"System reliability modeling and analysis for a marine power equipment operating in a discrete‐time dynamic environment","authors":"Yan Li, Wei Zhang, Lirong Cui, Hongda Gao","doi":"10.1002/qre.3577","DOIUrl":"https://doi.org/10.1002/qre.3577","url":null,"abstract":"Exploring on reliability modeling and analysis on a marine equipment in a dynamic environment is a meaningful and challenging issue, because the system commonly carries out the task at sea away from land and suffers a distinct influence of environment. Thus, a reliability model of a multi‐state repairable system operating in dynamic environment is developed by introducing the background of the marine power system in this paper. The novelty of the research lies in the modeling and computing methods are relatively innovative by employing the aggregated stochastic processes, Hadamard production and matrix‐analytic method. First, the working modes of the marine power system under several different kinds of conditions are introduced. Then, the evolution of both the system states and environment are described as discrete‐time Markov chains with multiple and different transition probability matrices. The failure probability and repair probability of components are also distinct in different environments. Furthermore, some performance indexes, especially the index relevant to the environment, are derived, respectively. Finally, the conclusion is obtained by a numerical example of the marine power system, which also illustrates the validity and applicability of the proposed model.","PeriodicalId":56088,"journal":{"name":"Quality and Reliability Engineering International","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140937394","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}
Chien‐Wei Wu, Armin Darmawan, Zih‐Huei Wang, Meng‐Tzu Lin
Manufacturers must meet high‐quality standards and exceed customer expectations to stay competitive due to significant technological advancements in recent decades. While implementing the yield measure is useful for achieving process performance by focusing on products that fall within specified limits, it does not accommodate specific customer requirements, particularly when a product's quality characteristic deviates from target value. To address this need, the quality‐yield index (Q‐yield) has been proposed, which combines the process‐yield index and loss‐based capability index, providing a more advanced performance measure. However, the Q‐yield index's confidence interval is challenging to derive due to the complicated sampling distribution involved. Several existing methods have attempted to construct an approximate confidence interval but none have performed well. Therefore, this article proposes an innovative approach, called the generalized confidence intervals (GCIs), that utilizes the idea of generalized pivotal quantities to establish the confidence interval for the Q‐yield index. The proposed approach is evaluated through simulations and compared to existing methods. The results reveal that the proposed approach provides the most accurate results for constructing the lower confidence bound of the Q‐yield index. This approach is recommended to evaluate process performance using the Q‐yield index for high‐quality customer requirements.
{"title":"Assessing high‐quality process performance using the quality‐yield index: An innovative methodology","authors":"Chien‐Wei Wu, Armin Darmawan, Zih‐Huei Wang, Meng‐Tzu Lin","doi":"10.1002/qre.3576","DOIUrl":"https://doi.org/10.1002/qre.3576","url":null,"abstract":"Manufacturers must meet high‐quality standards and exceed customer expectations to stay competitive due to significant technological advancements in recent decades. While implementing the yield measure is useful for achieving process performance by focusing on products that fall within specified limits, it does not accommodate specific customer requirements, particularly when a product's quality characteristic deviates from target value. To address this need, the quality‐yield index (Q‐yield) has been proposed, which combines the process‐yield index and loss‐based capability index, providing a more advanced performance measure. However, the Q‐yield index's confidence interval is challenging to derive due to the complicated sampling distribution involved. Several existing methods have attempted to construct an approximate confidence interval but none have performed well. Therefore, this article proposes an innovative approach, called the generalized confidence intervals (GCIs), that utilizes the idea of generalized pivotal quantities to establish the confidence interval for the Q‐yield index. The proposed approach is evaluated through simulations and compared to existing methods. The results reveal that the proposed approach provides the most accurate results for constructing the lower confidence bound of the Q‐yield index. This approach is recommended to evaluate process performance using the Q‐yield index for high‐quality customer requirements.","PeriodicalId":56088,"journal":{"name":"Quality and Reliability Engineering International","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140937470","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}
Aodi Yu, Ruixin Ruan, Xubo Zhang, Yuquan He, Kuantao Li
As an essential mechanical component, a rolling bearing can exhibit multiple failure modes that may occur independently or in correlation with one another. A reliability analysis method that meticulously accounts for the interdependencies among various bearing failure modes is presented in this paper. The examination of wear and fatigue failure mechanisms in rolling bearings is carried out using the Physics of Failure (PoF) approach. By considering the influence of uncertain variables, the limit state functions for individual failure modes are formulated through the application of stress‐strength interference theory. In the context of wear failure, the limit state function is derived using working clearance as the characteristic quantity. On the other hand, the limit state function for fatigue failure is constructed with a focus on fatigue damage accumulation. The Copula function is used to characterize the relationship between wear failure and fatigue failure, and a reliability calculation model for rolling bearings is developed, considering the correlation between these failure modes. Ultimately, the proposed method is utilized to assess the reliability of bearings under two different sets of test conditions. The feasibility of this method is confirmed through test data, demonstrating its effectiveness in predicting bearing reliability. Through the application of this method, engineers can optimize bearing size parameters, select appropriate initial clearances, and enhance the reliability design of bearing.
{"title":"Reliability analysis of rolling bearings considering failure mode correlations","authors":"Aodi Yu, Ruixin Ruan, Xubo Zhang, Yuquan He, Kuantao Li","doi":"10.1002/qre.3566","DOIUrl":"https://doi.org/10.1002/qre.3566","url":null,"abstract":"As an essential mechanical component, a rolling bearing can exhibit multiple failure modes that may occur independently or in correlation with one another. A reliability analysis method that meticulously accounts for the interdependencies among various bearing failure modes is presented in this paper. The examination of wear and fatigue failure mechanisms in rolling bearings is carried out using the Physics of Failure (PoF) approach. By considering the influence of uncertain variables, the limit state functions for individual failure modes are formulated through the application of stress‐strength interference theory. In the context of wear failure, the limit state function is derived using working clearance as the characteristic quantity. On the other hand, the limit state function for fatigue failure is constructed with a focus on fatigue damage accumulation. The Copula function is used to characterize the relationship between wear failure and fatigue failure, and a reliability calculation model for rolling bearings is developed, considering the correlation between these failure modes. Ultimately, the proposed method is utilized to assess the reliability of bearings under two different sets of test conditions. The feasibility of this method is confirmed through test data, demonstrating its effectiveness in predicting bearing reliability. Through the application of this method, engineers can optimize bearing size parameters, select appropriate initial clearances, and enhance the reliability design of bearing.","PeriodicalId":56088,"journal":{"name":"Quality and Reliability Engineering International","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140937396","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}
To improve the detecting abilities of upward and downward parameter changes in high‐quality process, two combined schemes by integrating memory‐type, that is, exponentially weighted moving average (EWMA) or cumulative sum (CUSUM), and memoryless, that is, Shewhart, are proposed for monitoring the time between events (TBE), which is modeled as an exponential distributed variable. A Monte Carlo simulation method is employed to obtain the Run Length () properties, that is average run length (), of the proposed schemes for different parameter settings. The nearly optimal monitoring parameter combinations under different change size are obtained by minimizing the out‐of‐control with the satisfied in‐control . Using the designed parameters, the performance of the proposed monitoring schemes is compared with the existing EWMA and CUSUM TBE. The results show that the proposed combined Shewhart–EWMA or Shewhart–CUSUM TBE generally perform better than the corresponding EWMA or CUSUM TBE for large changes and they also show better performance than the Shewhart TBE for small changes. Finally, a real dataset of organic light‐emitting diode (OLED) failure time from Sumsung company is employed to indicate the usage and implementation of combined TBE schemes.
{"title":"Combined Shewhart–EWMA and Shewhart–CUSUM monitoring schemes for time between events","authors":"Xuelong Hu, Fan Xia, Jiujun Zhang, Zhi Song","doi":"10.1002/qre.3571","DOIUrl":"https://doi.org/10.1002/qre.3571","url":null,"abstract":"To improve the detecting abilities of upward and downward parameter changes in high‐quality process, two combined schemes by integrating memory‐type, that is, exponentially weighted moving average (EWMA) or cumulative sum (CUSUM), and memoryless, that is, Shewhart, are proposed for monitoring the time between events (TBE), which is modeled as an exponential distributed variable. A Monte Carlo simulation method is employed to obtain the Run Length () properties, that is average run length (), of the proposed schemes for different parameter settings. The nearly optimal monitoring parameter combinations under different change size are obtained by minimizing the out‐of‐control with the satisfied in‐control . Using the designed parameters, the performance of the proposed monitoring schemes is compared with the existing EWMA and CUSUM TBE. The results show that the proposed combined Shewhart–EWMA or Shewhart–CUSUM TBE generally perform better than the corresponding EWMA or CUSUM TBE for large changes and they also show better performance than the Shewhart TBE for small changes. Finally, a real dataset of organic light‐emitting diode (OLED) failure time from Sumsung company is employed to indicate the usage and implementation of combined TBE schemes.","PeriodicalId":56088,"journal":{"name":"Quality and Reliability Engineering International","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140937665","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 fatigue behavior of large wind turbine blades is complex and stochastic due to their complex structure and operating environment. This paper focuses on developing a probabilistic fatigue life assessment method for wind turbine blades considering the uncertainties from wind velocity, material mechanical properties, pitch angle, and layer thickness. To improve the efficiency of stochastic fatigue behavior analysis of wind turbine blade, unidirectional fluid‐structure coupling (UFSC) and bidirectional fluid‐structure coupling (BFSC) analysis are employed to analyze the stochastic response. Then, Gaussian process regression (GPR) and Bayesian updating are combined to establish the stochastic fatigue behavior prediction model for wind turbine blade. On this basis, a modified S‐N curve formulation is proposed, and the fatigue life of wind turbine blade is analyzed by the modified S‐N curve and compared with the three‐parameter Weibull model. The results indicate that the proposed method for fatigue life assessment has better accuracy. The proposed probabilistic fatigue life assessment method with high accuracy and high efficiency, which is beneficial for the fatigue reliability design of wind turbine blades.
{"title":"A probabilistic fatigue life assessment method for wind turbine blade based on Bayesian GPR with the effects of pitch angle","authors":"Xiaoling Zhang, Kejia Zhang, Zhongzhe Chen","doi":"10.1002/qre.3575","DOIUrl":"https://doi.org/10.1002/qre.3575","url":null,"abstract":"The fatigue behavior of large wind turbine blades is complex and stochastic due to their complex structure and operating environment. This paper focuses on developing a probabilistic fatigue life assessment method for wind turbine blades considering the uncertainties from wind velocity, material mechanical properties, pitch angle, and layer thickness. To improve the efficiency of stochastic fatigue behavior analysis of wind turbine blade, unidirectional fluid‐structure coupling (UFSC) and bidirectional fluid‐structure coupling (BFSC) analysis are employed to analyze the stochastic response. Then, Gaussian process regression (GPR) and Bayesian updating are combined to establish the stochastic fatigue behavior prediction model for wind turbine blade. On this basis, a modified S‐N curve formulation is proposed, and the fatigue life of wind turbine blade is analyzed by the modified S‐N curve and compared with the three‐parameter Weibull model. The results indicate that the proposed method for fatigue life assessment has better accuracy. The proposed probabilistic fatigue life assessment method with high accuracy and high efficiency, which is beneficial for the fatigue reliability design of wind turbine blades.","PeriodicalId":56088,"journal":{"name":"Quality and Reliability Engineering International","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140937397","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}
Hierarchical Bayes, or E‐Bayes, is frequently used to estimate the failure probability when solving a zero‐failure reliability evaluation model; however, the accuracy of the reliability estimation using these methods is not very good in practice. Due to this, a novel double‐modified hierarchical Bayes (DMH‐Bayes) is proposed for Weibull characteristic data in this study to enhance failure probability estimation and improve reliability point estimation accuracy. Meanwhile, in order to guarantee the preservation of the assessment findings' consistency and confidence level, the parametric Bootstrap method (P‐Bootstrap) and the L‐moment estimation method based on point estimation are introduced to obtain reliability confidence interval estimates. Based on Monte–Carlo simulation testing and analysis of a gyroscope bearing, the new model is confirmed to have better applicability and robustness while improving the accuracy of reliability assessment.
{"title":"Reliability evaluation in the zero‐failure Weibull case based on double‐modified hierarchical Bayes","authors":"Bo Zheng, Zuteng Long, Yang Ning, Xin Ma","doi":"10.1002/qre.3572","DOIUrl":"https://doi.org/10.1002/qre.3572","url":null,"abstract":"Hierarchical Bayes, or E‐Bayes, is frequently used to estimate the failure probability when solving a zero‐failure reliability evaluation model; however, the accuracy of the reliability estimation using these methods is not very good in practice. Due to this, a novel double‐modified hierarchical Bayes (DMH‐Bayes) is proposed for Weibull characteristic data in this study to enhance failure probability estimation and improve reliability point estimation accuracy. Meanwhile, in order to guarantee the preservation of the assessment findings' consistency and confidence level, the parametric Bootstrap method (P‐Bootstrap) and the L‐moment estimation method based on point estimation are introduced to obtain reliability confidence interval estimates. Based on Monte–Carlo simulation testing and analysis of a gyroscope bearing, the new model is confirmed to have better applicability and robustness while improving the accuracy of reliability assessment.","PeriodicalId":56088,"journal":{"name":"Quality and Reliability Engineering International","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140937398","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 lifetime data collected from the field are usually heavily censored, in which case, getting an accurate reliability evaluation based on heavily censored data is challenging. For heavily Type‐II censored data, the parameters estimation bias of traditional methods (i.e., maximum likelihood estimation (MLE) and least squares estimation (LSE)) are still large, and Bayesian methods are hard to specify the priors in practice. Therefore, considering the existing range of shape parameter for Weibull distribution, this study proposes two novel parameter estimation methods, the three‐step MLE method and the hybrid estimation method. For the three‐step MLE method, the initial estimates of shape and scale parameters are first respectively derived using MLE, then are updated by the single parameter MLE method with the range constraint of shape parameter. For the hybrid estimation method, the shape parameter is estimated by the LSE method with the existing range constraint of shape parameter, then the scale parameter estimate can be obtained by MLE. On this basis, two numerical examples are performed to demonstrate the consistency and effectiveness of the proposed methods. Finally, a case study on turbine engines is given to verify the effectiveness and applicability of the proposed methods.
{"title":"Reliability evaluation for Weibull distribution with heavily Type II censored data","authors":"Mengyu Liu, Huiling Zheng, Jun Yang","doi":"10.1002/qre.3570","DOIUrl":"https://doi.org/10.1002/qre.3570","url":null,"abstract":"The lifetime data collected from the field are usually heavily censored, in which case, getting an accurate reliability evaluation based on heavily censored data is challenging. For heavily Type‐II censored data, the parameters estimation bias of traditional methods (i.e., maximum likelihood estimation (MLE) and least squares estimation (LSE)) are still large, and Bayesian methods are hard to specify the priors in practice. Therefore, considering the existing range of shape parameter for Weibull distribution, this study proposes two novel parameter estimation methods, the three‐step MLE method and the hybrid estimation method. For the three‐step MLE method, the initial estimates of shape and scale parameters are first respectively derived using MLE, then are updated by the single parameter MLE method with the range constraint of shape parameter. For the hybrid estimation method, the shape parameter is estimated by the LSE method with the existing range constraint of shape parameter, then the scale parameter estimate can be obtained by MLE. On this basis, two numerical examples are performed to demonstrate the consistency and effectiveness of the proposed methods. Finally, a case study on turbine engines is given to verify the effectiveness and applicability of the proposed methods.","PeriodicalId":56088,"journal":{"name":"Quality and Reliability Engineering International","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140937602","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}
Establishing an accurate accelerated degradation model is paramount for ensuring precise reliability evaluation results. Unfortunately, current accelerated degradation tests often lack test groups for investigating multi‐stress coupled phenomena. Consequently, existing multi‐stress accelerated models fail to adequately consider the impact of stress coupling when data with stress coupling information is absent. This limitation leads to the development of inaccurate models, ultimately affecting the precision of reliability assessment. To address this challenge, this paper introduces a new modeling method for multi‐stress accelerated degradation models that takes into account stress coupling effects. The proposed modeling method aims to improve the accuracy of reliability assessment under multi‐stress conditions. In the proposed model, the main effect function of stress is determined based on existing single‐stress accelerated models. The coupling effect is first examined through the Multivariate Analysis of Variance (MANOVA), and then the functional form of the coupling effect function is determined from the given candidate functions through correlation analysis. Next, the coupling effect is incorporated into a Wiener process to establish a multi‐stress accelerated degradation model, and the two‐step estimation method combining Least Squares Method (LSM) and Differential Evolution Algorithm (DEA) is proposed. The accuracy and effectiveness of the coupling effect test method, model establishment, and parameter estimation method were validated using two Monte Carlo simulation experimental data sets. Finally, the superiority of the proposed model is demonstrated through examples, providing feasible ideas and technical support for the research on multi‐stress accelerated degradation modeling considering stress coupling.
{"title":"Acceleration model considering multi‐stress coupling effect and reliability modeling method based on nonlinear Wiener process","authors":"Xiaojian Yi, Zhezhe Wang, Shulin Liu, Qing Tang","doi":"10.1002/qre.3565","DOIUrl":"https://doi.org/10.1002/qre.3565","url":null,"abstract":"Establishing an accurate accelerated degradation model is paramount for ensuring precise reliability evaluation results. Unfortunately, current accelerated degradation tests often lack test groups for investigating multi‐stress coupled phenomena. Consequently, existing multi‐stress accelerated models fail to adequately consider the impact of stress coupling when data with stress coupling information is absent. This limitation leads to the development of inaccurate models, ultimately affecting the precision of reliability assessment. To address this challenge, this paper introduces a new modeling method for multi‐stress accelerated degradation models that takes into account stress coupling effects. The proposed modeling method aims to improve the accuracy of reliability assessment under multi‐stress conditions. In the proposed model, the main effect function of stress is determined based on existing single‐stress accelerated models. The coupling effect is first examined through the Multivariate Analysis of Variance (MANOVA), and then the functional form of the coupling effect function is determined from the given candidate functions through correlation analysis. Next, the coupling effect is incorporated into a Wiener process to establish a multi‐stress accelerated degradation model, and the two‐step estimation method combining Least Squares Method (LSM) and Differential Evolution Algorithm (DEA) is proposed. The accuracy and effectiveness of the coupling effect test method, model establishment, and parameter estimation method were validated using two Monte Carlo simulation experimental data sets. Finally, the superiority of the proposed model is demonstrated through examples, providing feasible ideas and technical support for the research on multi‐stress accelerated degradation modeling considering stress coupling.","PeriodicalId":56088,"journal":{"name":"Quality and Reliability Engineering International","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140937472","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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