Quality and Reliability Engineering International最新文献

{"title":"Control chart for detecting the scale parameter of the zero‐inflated Poisson model","authors":"Aijun Zhao, Liu Liu, Xin Lai, Ka Chun Chong","doi":"10.1002/qre.3574","DOIUrl":"https://doi.org/10.1002/qre.3574","url":null,"abstract":"When monitoring risk in public health, count data commonly exhibit an excessive number of zero, and the zero‐inflated Poisson (ZIP) model is often used to fit this type of data. Most previous methods for monitoring of the ZIP model have focused on the changes in the location parameter and the existence of the scale parameter and usually assumed that the scale parameter is zero in the <jats:italic>H</jats:italic><jats:sub>0</jats:sub> stage. However, in an objective environment, data often have certain fluctuations, meaning that the scale parameter always exists. Therefore, it is more meaningful to monitor the changes in the scale parameter on top of the predefined baseline than to monitor its existence. In this study, we derive a score test statistic based on the generalized Henderson's joint likelihood function, construct a risk‐adjusted exponentially weighted moving average (EWMA) control chart to monitor the variability of the random effects variance component in the ZIP mixed‐effects model. And the convergence property of the score test statistic is proved through derivation, which shows that the new method has theoretical reliability. The simulation results Indicate that when the scale parameter has different predefined baselines and different variation amplitudes, the proposed method is more effective than the existing RA‐ZIP and PR‐ZIP control charts. In addition, the proposed method is applied to real data from a Hong Kong hospital for online influenza surveillance to demonstrate its practicability.","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":"140937536","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A critique on the use of the belief statistic for process monitoring","authors":"Abdul Haq, William H. Woodall","doi":"10.1002/qre.3573","DOIUrl":"https://doi.org/10.1002/qre.3573","url":null,"abstract":"Recently, several control charts have been proposed in the statistical process monitoring literature based on a belief statistic. In this article we compare the zero‐state average run‐lengths and conditional expected delay profiles of the exponentially weighted moving average (EWMA) and belief statistic‐based (BSB) charts. We show that an ordinary EWMA chart is far better than the BSB chart when detecting delayed shifts in the mean of a normally distributed process.","PeriodicalId":56088,"journal":{"name":"Quality and Reliability Engineering International","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140889102","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Remaining useful life prediction method for rolling bearings based on hybrid dilated convolution transfer","authors":"Bo Zhang, Changhua Hu, Hao Zhang, Jianfei Zheng, Jianxun Zhang, Hong Pei","doi":"10.1002/qre.3563","DOIUrl":"https://doi.org/10.1002/qre.3563","url":null,"abstract":"It is difficult to effectively predict remaining useful life (RUL) due to limited training samples and lack of life labels in some operating conditions of practical engineering. When existing deep learning methods predict the RUL of equipment in such operating conditions using a model trained on other operating conditions, the poor generalization of the model caused by large distribution differences cannot be ignored. In this study, an RUL prediction method based on integrated dilated convolution transfer is proposed. This method jointly adjusts the model parameters by inverting the loss function of the RUL prediction module and the domain adaptive module, and then realizes the extraction of domain‐invariant features between different operating condition data through the feature extraction module, which provides support for transfer RUL prediction between different operating conditions. In the feature extraction module, a one‐dimensional convolution network with a large‐size kernel reduces noise in the original data, which reduces the erroneous effect of noise on the trending expression of the original data, and a hybrid dilated convolution network extracts the features of the different sensory fields of the noise‐reduced data, which increases the richness of the extracted features and thus improves the accuracy of the modeling. Next, the extracted features are fed into the RUL prediction module to predict RUL; into the classification model in the domain adaptation module to divide the source and target domains; and into the distribution difference measurement model in the domain adaptation module to identify the feature distribution differences between the source and target domains, and inversely adjust the model parameters by reducing the distribution differences. Furthermore, domain invariant characteristics of the features in different receptive fields under multiple operating conditions are obtained to enhance the model's generalization ability and achieve RUL prediction across various operating conditions. Monte Carlo (MC) dropout simulation technology is used to quantify the uncertainty of prediction results. Finally, the effectiveness and superiority of the proposed method are verified using the prognostics and health management (PHM) 2012 bearing dataset.","PeriodicalId":56088,"journal":{"name":"Quality and Reliability Engineering International","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140889105","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Two Bayesian approaches of monitoring mean of Gaussian process using Bayes factor","authors":"Yaxin Tan, Amitava Mukherjee, Jiujun Zhang","doi":"10.1002/qre.3567","DOIUrl":"https://doi.org/10.1002/qre.3567","url":null,"abstract":"This paper develops two novel process monitoring schemes for the mean of a Gaussian process: the Bayes factor (BF) and the improved Bayes factor (IBF) schemes. Conjugate priors are used to construct the plotting statistics. The performance of the proposed schemes is evaluated in terms of average run length (ARL), standard deviation of run length (SDRL), and several percentiles, and these performance metrics across different hyper‐parameters and various sample sizes are evaluated via Monte Carlo simulations. Both zero‐state and steady‐state out‐of‐control (OOC) performances are investigated comprehensively. The simulation results show that the IBF scheme outperforms the existing Bayesian exponentially weighted moving average (EWMA) schemes under different loss functions in zero‐state. In steady‐state conditions, the IBF scheme outperforms for small shifts. Finally, we present two examples to illustrate the practical application of the proposed schemes.","PeriodicalId":56088,"journal":{"name":"Quality and Reliability Engineering International","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140833849","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A neural network copula function approach for solving joint basic probability assignment in structural reliability analysis","authors":"Rui‐Shi Yang, Li‐Jun Sun, Hai‐Bin Li, Yong Yang","doi":"10.1002/qre.3568","DOIUrl":"https://doi.org/10.1002/qre.3568","url":null,"abstract":"Applying evidence theory to structural reliability analysis under epistemic uncertainty, it is necessary to consider the correlation of evidence variables. Among them, solving the joint basic probability assignment (BPA) of the evidence variables is a crucial link. In this study, a solution method of joint BPA based on neural network copula function is proposed. This method is to automatically construct copula function through neural network, which avoids the process of selecting the optimal copula function. Firstly, the neural network copula function is constructed based on the sample set of evidence variables. Then, the expression for solving the joint BPA using the neural network copula function is derived through vectors. Furthermore, the expression is used to map the marginal BPA of evidence variables to joint BPA, thus realizing the solution of joint BPA. Finally, the effectiveness of this method is verified by three examples. The results show that the neural network copula function describes the data distribution better than the optimal copula function selected by the traditional method. In addition, there is actually an error in solving the reliability intervals using the traditional optimal copula function method, whereas the results of this paper's neural network copula function method are more accurate and better for decision making.","PeriodicalId":56088,"journal":{"name":"Quality and Reliability Engineering International","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140833784","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Research and verification on parameter solution of mixed shock model for common cause failure based on particle swarm algorithm","authors":"Yinxiao Hu, Hongjuan Ge, Pei He, Hui Jin, Huang Li, Chunran Zou","doi":"10.1002/qre.3569","DOIUrl":"https://doi.org/10.1002/qre.3569","url":null,"abstract":"Mixed shock model is an explicit construction method of failure probability model based on component independent failure, system nonfatal shock, and fatal shock failure, which considers common cause failure (CCF) in redundant system. For aerospace systems, a modified mixed shock model is proposed, which considers several components may fail independently and simultaneously in operation. In order to solve the issue that the parameters of the mixed shock model cannot be solved directly based on the failure probability data, a parameter solving method based on particle swarm optimization (PSO) algorithm is proposed. Additionally, the relationship between the failure probability and the gradient of the parameter change is deduced, and the reduced‐order (RO) solution based on the gradient of the parameter change is proposed to improve the efficiency of the solution. A fitness function construction method based on the relative error of the solution probability and the true probability is proposed to improve the probability solution accuracy of multicomponent failure. The nonlinear inertia factor optimization method combined with fitness change is studied to improve the particle swarm dynamics. The accuracy of the results of different parameters solving sequence and different PSO methods are compared, and the effectiveness of the RO solution is verified. The results of the mixed shock model before and after modification are compared with the different CCF data, which verifies the effectiveness and wide applicability of the modified mixed shock model. The results show that the modified mixed shock model for CCF and its parameter solution method can significantly improve the probability solution accuracy of all components failure, and also provide a new theoretical basis and solution method for the quantitative analysis of multiredundant system failure.","PeriodicalId":56088,"journal":{"name":"Quality and Reliability Engineering International","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140833725","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluating the lifetime distribution parameters and reliability of products using successive approximation method","authors":"Jin Guo, Xiangwei Kong, Ningxiang Wu, Liyang Xie","doi":"10.1002/qre.3559","DOIUrl":"https://doi.org/10.1002/qre.3559","url":null,"abstract":"The Weibull distribution is an extensively used statistical model for analyzing the reliability of mechanical and electrical components. Due to the complexity of the nonlinear equations and the scarcity of failure data, the common method may not provide satisfactory results of the reliability. In this case, a new approach for Weibull parameter estimation and reliability analysis, based on successive approximation schemes, is presented. The shape and scale parameters are estimated by maximizing the likelihood functions, and the location parameter is obtained by constructing an approximate correction model between it and the failure data. In order to show the performance of the proposed method, an extensive Monte‐Carlo simulation study is conducted. Simulation results show that the proposed method provides better estimates and efficient confidence intervals for Weibull parameters. In addition, the proposed method works well in presence of small sample sizes. Finally, two real examples are analyzed to illustrate the application of the proposed method.","PeriodicalId":56088,"journal":{"name":"Quality and Reliability Engineering International","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140662713","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reliability evaluation of spacecraft power generation performance with competitive failure processes under irradiation","authors":"Tingyu Zhang, Ying Zeng, Xin Huang, Jing Li, Fan Xia","doi":"10.1002/qre.3560","DOIUrl":"https://doi.org/10.1002/qre.3560","url":null,"abstract":"The performance of space power systems is crucial for space products as it determines the operational capabilities, endurance, and efficiency of satellites, spacecraft, and other extraterrestrial devices. Unlike reliability analysis in aerospace systems, studying spacecraft power generation performance requires consideration of both hardware and software aspects. Existing failure models do not fully capture the self‐recovery process of control programs. Therefore, this study presents an impact degradation model for space power systems that incorporates competitive failures under irradiation conditions. The model analyzes solar arrays and power controllers to derive a performance degradation model by considering the defect formation mechanism of amorphous semiconductor materials. Additionally, two shock types are defined based on redundancy backup in power controllers and scrubbing frequency in field‐programmable gate array (FPGA) units. Within the case analysis section, the research meticulously investigates and elucidates the correlation probabilities among varying proton irradiation doses, scrubbing frequencies, and the aforementioned shock types.","PeriodicalId":56088,"journal":{"name":"Quality and Reliability Engineering International","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140669779","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sequential sampling for functional estimation via Sieve","authors":"Alessia Benevento, Pouya Ahadi, Swati Gupta, Massimo Pacella, K. Paynabar","doi":"10.1002/qre.3557","DOIUrl":"https://doi.org/10.1002/qre.3557","url":null,"abstract":"Sequential sampling methods are often used to estimate functions describing models subjected to time‐intensive simulations or expensive experiments. These methods provide guidelines for point selection in the domain to capture maximum information about the function. However, in most sequential sampling methods, determining a new point is a time‐consuming process. In this paper, we propose a new method, named Sieve, to sequentially select points of an initially unknown function based on the definition of proper intervals. In contrast with existing methods, Sieve does not involve function estimation at each iteration. Therefore, it presents a greater computational efficiency for achieving a given accuracy in estimation. Sieve brings in tools from computational geometry to subdivide regions of the domain efficiently. Further, we validate our proposed method through numerical simulations and two case studies on the calibration of internal combustion engines and the optimal exploration of an unknown environment by a mobile robot.","PeriodicalId":56088,"journal":{"name":"Quality and Reliability Engineering International","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140670987","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"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":null,"pages":null},"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}