The zero_failure data research is a new field in the recent years, but it is required urgently in practical projects, so the work has more theory and practical values. In this paper, for zero_failure data (t i,n i...The zero_failure data research is a new field in the recent years, but it is required urgently in practical projects, so the work has more theory and practical values. In this paper, for zero_failure data (t i,n i) at moment t i , if the prior distribution of the failure probability p i=p{T【t i} is quasi_exponential distribution, the author gives the p i Bayesian estimation and hierarchical Bayesian estimation and the reliability under zero_failure date condition is also obtained.展开更多
In the face of data scarcity in the optimization of maintenance strategies for civil aircraft,traditional failure data-driven methods are encountering challenges owing to the increasing reliability of aircraft design....In the face of data scarcity in the optimization of maintenance strategies for civil aircraft,traditional failure data-driven methods are encountering challenges owing to the increasing reliability of aircraft design.This study addresses this issue by presenting a novel combined data fusion algorithm,which serves to enhance the accuracy and reliability of failure rate analysis for a specific aircraft model by integrating historical failure data from similar models as supplementary information.Through a comprehensive analysis of two different maintenance projects,this study illustrates the application process of the algorithm.Building upon the analysis results,this paper introduces the innovative equal integral value method as a replacement for the conventional equal interval method in the context of maintenance schedule optimization.The Monte Carlo simulation example validates that the equivalent essential value method surpasses the traditional method by over 20%in terms of inspection efficiency ratio.This discovery indicates that the equal critical value method not only upholds maintenance efficiency but also substantially decreases workload and maintenance costs.The findings of this study open up novel perspectives for airlines grappling with data scarcity,offer fresh strategies for the optimization of aviation maintenance practices,and chart a new course toward achieving more efficient and cost-effective maintenance schedule optimization through refined data analysis.展开更多
Together,the heart and lung sound comprise the thoracic cavity sound,which provides informative details that reflect patient conditions,particularly heart failure(HF)patients.However,due to the limitations of human he...Together,the heart and lung sound comprise the thoracic cavity sound,which provides informative details that reflect patient conditions,particularly heart failure(HF)patients.However,due to the limitations of human hearing,a limited amount of information can be auscultated from thoracic cavity sounds.With the aid of artificial intelligence–machine learning,these features can be analyzed and aid in the care of HF patients.Machine learning of thoracic cavity sound data involves sound data pre-processing by denoising,resampling,segmentation,and normalization.Afterwards,the most crucial step is feature extraction and se-lection where relevant features are selected to train the model.The next step is classification and model performance evaluation.This review summarizes the currently available studies that utilized different machine learning models,different feature extraction and selection methods,and different classifiers to generate the desired output.Most studies have analyzed the heart sound component of thoracic cavity sound to distinguish between normal and HF patients.Additionally,some studies have aimed to classify HF patients based on thoracic cavity sounds in their entirety,while others have focused on risk strati-fication and prognostic evaluation of HF patients using thoracic cavity sounds.Overall,the results from these studies demonstrate a promisingly high level of accuracy.Therefore,future prospective studies should incorporate these machine learning models to expedite their integration into daily clinical practice for managing HF patients.展开更多
Parametric survival models are essential for analyzing time-to-event data in fields such as engineering and biomedicine.While the log-logistic distribution is popular for its simplicity and closed-form expressions,it ...Parametric survival models are essential for analyzing time-to-event data in fields such as engineering and biomedicine.While the log-logistic distribution is popular for its simplicity and closed-form expressions,it often lacks the flexibility needed to capture complex hazard patterns.In this article,we propose a novel extension of the classical log-logistic distribution,termed the new exponential log-logistic(NExLL)distribution,designed to provide enhanced flexibility in modeling time-to-event data with complex failure behaviors.The NExLL model incorporates a new exponential generator to expand the shape adaptability of the baseline log-logistic distribution,allowing it to capture a wide range of hazard rate shapes,including increasing,decreasing,J-shaped,reversed J-shaped,modified bathtub,and unimodal forms.A key feature of the NExLL distribution is its formulation as a mixture of log-logistic densities,offering both symmetric and asymmetric patterns suitable for diverse real-world reliability scenarios.We establish several theoretical properties of the model,including closed-form expressions for its probability density function,cumulative distribution function,moments,hazard rate function,and quantiles.Parameter estimation is performed using seven classical estimation techniques,with extensive Monte Carlo simulations used to evaluate and compare their performance under various conditions.The practical utility and flexibility of the proposed model are illustrated using two real-world datasets from reliability and engineering applications,where the NExLL model demonstrates superior fit and predictive performance compared to existing log-logistic-basedmodels.This contribution advances the toolbox of parametric survivalmodels,offering a robust alternative formodeling complex aging and failure patterns in reliability,engineering,and other applied domains.展开更多
In this paper, an estimation method for reliability parameter in the case of zero-failuare data-synthetic estimation method is given. For zero-failure data of double-parameter exponential distribution, a hierarchical ...In this paper, an estimation method for reliability parameter in the case of zero-failuare data-synthetic estimation method is given. For zero-failure data of double-parameter exponential distribution, a hierarchical Bayesian estimation of the failure probability is presented. After failure information is introduced, hierarchical Bayesian estimation and synthetic estimation of the failure probability, as well as synthetic estimation of reliability are given. Calculation and analysis are performed regarding practical problems in case that life distribution of an engine obeys double-parameter exponential distribution.展开更多
Heart failure prediction is crucial as cardiovascular diseases become the leading cause of death worldwide,exacerbated by the COVID-19 pandemic.Age,cholesterol,and blood pressure datasets are becoming inadequate becau...Heart failure prediction is crucial as cardiovascular diseases become the leading cause of death worldwide,exacerbated by the COVID-19 pandemic.Age,cholesterol,and blood pressure datasets are becoming inadequate because they cannot capture the complexity of emerging health indicators.These high-dimensional and heterogeneous datasets make traditional machine learning methods difficult,and Skewness and other new biomarkers and psychosocial factors bias the model’s heart health prediction across diverse patient profiles.Modern medical datasets’complexity and high dimensionality challenge traditional predictionmodels like SupportVectorMachines and Decision Trees.Quantum approaches include QSVM,QkNN,QDT,and others.These Constraints drove research.The“QHF-CS:Quantum-Enhanced Heart Failure Prediction using Quantum CNN with Optimized Feature Qubit Selection with Cuckoo Search in Skewed Clinical Data”system was developed in this research.This novel system leverages a Quantum Convolutional Neural Network(QCNN)-based quantum circuit,enhanced by meta-heuristic algorithms—Cuckoo SearchOptimization(CSO),Artificial BeeColony(ABC),and Particle SwarmOptimization(PSO)—for feature qubit selection.Among these,CSO demonstrated superior performance by consistently identifying the most optimal and least skewed feature subsets,which were then encoded into quantum states for circuit construction.By integrating advanced quantum circuit feature maps like ZZFeatureMap,RealAmplitudes,and EfficientSU2,the QHF-CS model efficiently processes complex,high-dimensional data,capturing intricate patterns that classical models overlook.The QHF-CS model improves precision,recall,F1-score,and accuracy to 0.94,0.95,0.94,and 0.94.Quantum computing could revolutionize heart failure diagnostics by improving model accuracy and computational efficiency,enabling complex healthcare diagnostic breakthroughs.展开更多
This paper introduces a new method, E-Bayesian estimation method, to estimate the reliability in zero-failure data. The definition of E-Bayesian estimation of the reliability is given. Based on the definition,the form...This paper introduces a new method, E-Bayesian estimation method, to estimate the reliability in zero-failure data. The definition of E-Bayesian estimation of the reliability is given. Based on the definition,the formulas of E-Bayesian estimation and hierarchical Bayesian estimation of the reliability are provided, and property of the E-Bayesian estimation, i.e. relation between E-Bayesian estimation and hierarchical Bayesian estimation, is discussed. Calculations performed on practical problems show that the proposed new method is feasible and easy to operate.展开更多
For many products,distributions of their life mostly comply with increasing failure rates in average(IFRA).Aiming to these distributions,using properties of IFRA classification,this paper gives a non-parametric method...For many products,distributions of their life mostly comply with increasing failure rates in average(IFRA).Aiming to these distributions,using properties of IFRA classification,this paper gives a non-parametric method for processing zero-failure data.Estimations of reliabilities in any time are first obtained,and based on a regression model of failure rates,estimations of reliability indexes are given.Finally,a practical example is processed with this method.展开更多
The bearings of a certain type have their lives following a Weibull distribution. In a life test with 20 sets of bearings, only one set failed within the specified time, and none of the remainder failed even after th...The bearings of a certain type have their lives following a Weibull distribution. In a life test with 20 sets of bearings, only one set failed within the specified time, and none of the remainder failed even after the time of test has been extended. With a set of testing data like that in Table 1, it is required to estimate the reliability at the mission time. In this paper, we first use hierarchical Bayesian method to determine the prior distribution and the Bayesian estimates of various probabilities of failures, p i 's, then use the method of least squares to estimate the parameters of the Weibull distribution and the reliability. Actual computation shows that the estimates so obtained are rather robust. And the results have been adopted for practical use.展开更多
The small sample prediction problem which commonly exists in reliability analysis was discussed with the progressive prediction method in this paper.The modeling and estimation procedure,as well as the forecast and co...The small sample prediction problem which commonly exists in reliability analysis was discussed with the progressive prediction method in this paper.The modeling and estimation procedure,as well as the forecast and confidence limits formula of the progressive auto regressive(PAR) method were discussed in great detail.PAR model not only inherits the simple linear features of auto regressive(AR) model,but also has applicability for nonlinear systems.An application was illustrated for predicting the future fatigue failure for Tantalum electrolytic capacitors.Forecasting results of PAR model were compared with auto regressive moving average(ARMA) model,and it can be seen that the PAR method can be considered good and shows a promise for future applications.展开更多
A new problem that classical statistical methods are incapable of solving is reliability modeling and assessment when multiple numerical control machine tools(NCMTs) reveal zero failures after a reliability test. Thus...A new problem that classical statistical methods are incapable of solving is reliability modeling and assessment when multiple numerical control machine tools(NCMTs) reveal zero failures after a reliability test. Thus, the zero-failure data form and corresponding Bayesian model are developed to solve the zero-failure problem of NCMTs, for which no previous suitable statistical model has been developed. An expert-judgment process that incorporates prior information is presented to solve the difficulty in obtaining reliable prior distributions of Weibull parameters. The equations for the posterior distribution of the parameter vector and the Markov chain Monte Carlo(MCMC) algorithm are derived to solve the difficulty of calculating high-dimensional integration and to obtain parameter estimators. The proposed method is applied to a real case; a corresponding programming code and trick are developed to implement an MCMC simulation in Win BUGS, and a mean time between failures(MTBF) of 1057.9 h is obtained. Given its ability to combine expert judgment, prior information, and data, the proposed reliability modeling and assessment method under the zero failure of NCMTs is validated.展开更多
We show that an aggregated Interest in Named Data Networking (NDN) may fail to retrieve desired data since the Interest previously sent upstream for the same content is judged as a duplicate one and then dropped by an...We show that an aggregated Interest in Named Data Networking (NDN) may fail to retrieve desired data since the Interest previously sent upstream for the same content is judged as a duplicate one and then dropped by an upstream node due to its multipath forwarding. Furthermore, we propose NDRUDAF, a NACK based mechanism that enhances the Interest forwarding and enables Detection and fast Recovery from such Unanticipated Data Access Failure. In the NDN enhanced with NDRUDAF, the router that aggregates the Interest detects such unanticipated data access failure based on a negative acknowledgement from the upstream node that judges the Interest as a duplicate one. Then the router retransmits the Interest as soon as possible on behalf of the requester whose Interest is aggregated to fast recover from the data access failure. We qualitatively and quantitatively analyze the performance of the NDN enhanced with our proposed NDRUDAF and compare it with that of the present NDN. Our experimental results validate that NDRUDAF improves the system performance in case of such unanticipated data access failure in terms of data access delay and network resource utilization efficiency at routers.展开更多
Interval-valued data and incomplete data are two key problems for failure analysis of thruster experimental data and have been basically solved by the proposed methods in this paper. Firstly, information data acquired...Interval-valued data and incomplete data are two key problems for failure analysis of thruster experimental data and have been basically solved by the proposed methods in this paper. Firstly, information data acquired from the simulation and evaluation system formed as intervalvalued information system （IIS） is classified by the interval similarity relation. Then, as an improvement of the classical rough set, a new kind of generalized information entropy called ＂H＇-information entropy＂ is suggested for the measurement of uncertainty and the classification ability of IIS. There is an innovative information filling technique using the properties of H＇-information entropy to replace missing data by some smaller estimation intervals. Finally, an improved method of failure analysis synthesized by the above achievements is presented to classify the thruster experimental data, complete the information, and extract the failure rules. The feasibility and advantage of this method is testified by an actual application of failure analysis, whose performance is evaluated by the quantification of E-condition entropy.展开更多
The development of cloud computing and virtualization technology has brought great challenges to the reliability of data center services.Data centers typically contain a large number of compute and storage nodes which...The development of cloud computing and virtualization technology has brought great challenges to the reliability of data center services.Data centers typically contain a large number of compute and storage nodes which may fail and affect the quality of service.Failure prediction is an important means of ensuring service availability.Predicting node failure in cloud-based data centers is challenging because the failure symptoms reflected have complex characteristics,and the distribution imbalance between the failure sample and the normal sample is widespread,resulting in inaccurate failure prediction.Targeting these challenges,this paper proposes a novel failure prediction method FP-STE(Failure Prediction based on Spatio-temporal Feature Extraction).Firstly,an improved recurrent neural network HW-GRU(Improved GRU based on HighWay network)and a convolutional neural network CNN are used to extract the temporal features and spatial features of multivariate data respectively to increase the discrimination of different types of failure symptoms which improves the accuracy of prediction.Then the intermediate results of the two models are added as features into SCSXGBoost to predict the possibility and the precise type of node failure in the future.SCS-XGBoost is an ensemble learning model that is improved by the integrated strategy of oversampling and cost-sensitive learning.Experimental results based on real data sets confirm the effectiveness and superiority of FP-STE.展开更多
Multivariate failure time data are frequently encountered in biomedical research.In this article,we model marginal hazards with accelerated hazards model to analyze multivariate failure time data.Estimating equations ...Multivariate failure time data are frequently encountered in biomedical research.In this article,we model marginal hazards with accelerated hazards model to analyze multivariate failure time data.Estimating equations are derived analogous to generalized estimating equation method.Under certain regular conditions,the resultant estimators for the regression parameters are shown to be asymptotically normal.Furthermore,we also establish the weak convergence of estimators for the baseline cumulative hazard functions.展开更多
Remaining useful life(RUL) prediction is one of the most crucial elements in prognostics and health management(PHM). Aiming at the imperfect prior information, this paper proposes an RUL prediction method based on a n...Remaining useful life(RUL) prediction is one of the most crucial elements in prognostics and health management(PHM). Aiming at the imperfect prior information, this paper proposes an RUL prediction method based on a nonlinear random coefficient regression(RCR) model with fusing failure time data.Firstly, some interesting natures of parameters estimation based on the nonlinear RCR model are given. Based on these natures,the failure time data can be fused as the prior information reasonably. Specifically, the fixed parameters are calculated by the field degradation data of the evaluated equipment and the prior information of random coefficient is estimated with fusing the failure time data of congeneric equipment. Then, the prior information of the random coefficient is updated online under the Bayesian framework, the probability density function(PDF) of the RUL with considering the limitation of the failure threshold is performed. Finally, two case studies are used for experimental verification. Compared with the traditional Bayesian method, the proposed method can effectively reduce the influence of imperfect prior information and improve the accuracy of RUL prediction.展开更多
We study the two-dimensional(2D)Cauchy problem of nonhomogeneous Boussinesq system for magnetohydrodynamics convection without heat diffusion in the whole plane.Based on delicate weighted estimates,we derive the globa...We study the two-dimensional(2D)Cauchy problem of nonhomogeneous Boussinesq system for magnetohydrodynamics convection without heat diffusion in the whole plane.Based on delicate weighted estimates,we derive the global existence and uniqueness of strong solutions.In particular,the initial data can be arbitrarily large and the initial density may contain vacuum states and even have compact support.展开更多
As a bladder accumulator is a high reliable and long life component in a hydraulic system,its cost is high and it takes a lot of time to test its reliability,therefore,a reliability test with small sample is performed...As a bladder accumulator is a high reliable and long life component in a hydraulic system,its cost is high and it takes a lot of time to test its reliability,therefore,a reliability test with small sample is performed,and no failure data is obtained using the method of fixed time truncation. In the case of Weibull distribution,a life reliability model of bladder energy storage is established by Bayesian method using the optimal confidence intervals method,a model of one-sided lower confidence intervals of the reliability and one-sided lower confidence intervals model of the reliability life are established. Results of experiments show that the evaluation method of no failure data under Weibull distribution is a good way to evaluate the reliability of the accumulator,which is convenient for engineering application,and the reliability of the accumulator has theoretical and practical significance.展开更多
Current traffic signal split failure (SF) estimations derived from high-resolution controller event data rely on detector occupancy ratios and preset thresholds. The reliability of these techniques depends on the sele...Current traffic signal split failure (SF) estimations derived from high-resolution controller event data rely on detector occupancy ratios and preset thresholds. The reliability of these techniques depends on the selected thresholds, detector lengths, and vehicle arrival patterns. Connected vehicle (CV) trajectory data can more definitively show when a vehicle split fails by evaluating the number of stops it experiences as it approaches an intersection, but it has limited market penetration. This paper compares cycle-by-cycle SF estimations from both high-resolution controller event data and CV trajectory data, and evaluates the effect of data aggregation on SF agreement between the two techniques. Results indicate that, in general, split failure events identified from CV data are likely to also be captured from high-resolution data, but split failure events identified from high-resolution data are less likely to be captured from CV data. This is due to the CV market penetration rate (MPR) of ~5% being too low to capture representative data for every controller cycle. However, data aggregation can increase the ratio in which CV data captures split failure events. For example, day-of-week data aggregation increased the percentage of split failures identified with high-resolution data that were also captured with CV data from 35% to 56%. It is recommended that aggregated CV data be used to estimate SF as it provides conservative and actionable results without the limitations of intersection and detector configuration. As the CV MPR increases, the accuracy of CV-based SF estimation will also improve.展开更多
Falls remain a prevalent source of injury in daily life and underlying aetiology of falls are often complex and multi-factorial.[1,2]Older persons living with heart failure(OPLHF)are of a particular interest when disc...Falls remain a prevalent source of injury in daily life and underlying aetiology of falls are often complex and multi-factorial.[1,2]Older persons living with heart failure(OPLHF)are of a particular interest when discussing falls as multiple factors associated with heart failure(HF)aetiology and treatment are assumedly implicated in falls occurrence.A retrospective study reported a 14%increased risk of falls among OPLHF,and prospective data has shown that up to 40%of HF patients may experience a fall within a year from diagnosis.展开更多
文摘The zero_failure data research is a new field in the recent years, but it is required urgently in practical projects, so the work has more theory and practical values. In this paper, for zero_failure data (t i,n i) at moment t i , if the prior distribution of the failure probability p i=p{T【t i} is quasi_exponential distribution, the author gives the p i Bayesian estimation and hierarchical Bayesian estimation and the reliability under zero_failure date condition is also obtained.
文摘In the face of data scarcity in the optimization of maintenance strategies for civil aircraft,traditional failure data-driven methods are encountering challenges owing to the increasing reliability of aircraft design.This study addresses this issue by presenting a novel combined data fusion algorithm,which serves to enhance the accuracy and reliability of failure rate analysis for a specific aircraft model by integrating historical failure data from similar models as supplementary information.Through a comprehensive analysis of two different maintenance projects,this study illustrates the application process of the algorithm.Building upon the analysis results,this paper introduces the innovative equal integral value method as a replacement for the conventional equal interval method in the context of maintenance schedule optimization.The Monte Carlo simulation example validates that the equivalent essential value method surpasses the traditional method by over 20%in terms of inspection efficiency ratio.This discovery indicates that the equal critical value method not only upholds maintenance efficiency but also substantially decreases workload and maintenance costs.The findings of this study open up novel perspectives for airlines grappling with data scarcity,offer fresh strategies for the optimization of aviation maintenance practices,and chart a new course toward achieving more efficient and cost-effective maintenance schedule optimization through refined data analysis.
文摘Together,the heart and lung sound comprise the thoracic cavity sound,which provides informative details that reflect patient conditions,particularly heart failure(HF)patients.However,due to the limitations of human hearing,a limited amount of information can be auscultated from thoracic cavity sounds.With the aid of artificial intelligence–machine learning,these features can be analyzed and aid in the care of HF patients.Machine learning of thoracic cavity sound data involves sound data pre-processing by denoising,resampling,segmentation,and normalization.Afterwards,the most crucial step is feature extraction and se-lection where relevant features are selected to train the model.The next step is classification and model performance evaluation.This review summarizes the currently available studies that utilized different machine learning models,different feature extraction and selection methods,and different classifiers to generate the desired output.Most studies have analyzed the heart sound component of thoracic cavity sound to distinguish between normal and HF patients.Additionally,some studies have aimed to classify HF patients based on thoracic cavity sounds in their entirety,while others have focused on risk strati-fication and prognostic evaluation of HF patients using thoracic cavity sounds.Overall,the results from these studies demonstrate a promisingly high level of accuracy.Therefore,future prospective studies should incorporate these machine learning models to expedite their integration into daily clinical practice for managing HF patients.
文摘Parametric survival models are essential for analyzing time-to-event data in fields such as engineering and biomedicine.While the log-logistic distribution is popular for its simplicity and closed-form expressions,it often lacks the flexibility needed to capture complex hazard patterns.In this article,we propose a novel extension of the classical log-logistic distribution,termed the new exponential log-logistic(NExLL)distribution,designed to provide enhanced flexibility in modeling time-to-event data with complex failure behaviors.The NExLL model incorporates a new exponential generator to expand the shape adaptability of the baseline log-logistic distribution,allowing it to capture a wide range of hazard rate shapes,including increasing,decreasing,J-shaped,reversed J-shaped,modified bathtub,and unimodal forms.A key feature of the NExLL distribution is its formulation as a mixture of log-logistic densities,offering both symmetric and asymmetric patterns suitable for diverse real-world reliability scenarios.We establish several theoretical properties of the model,including closed-form expressions for its probability density function,cumulative distribution function,moments,hazard rate function,and quantiles.Parameter estimation is performed using seven classical estimation techniques,with extensive Monte Carlo simulations used to evaluate and compare their performance under various conditions.The practical utility and flexibility of the proposed model are illustrated using two real-world datasets from reliability and engineering applications,where the NExLL model demonstrates superior fit and predictive performance compared to existing log-logistic-basedmodels.This contribution advances the toolbox of parametric survivalmodels,offering a robust alternative formodeling complex aging and failure patterns in reliability,engineering,and other applied domains.
文摘In this paper, an estimation method for reliability parameter in the case of zero-failuare data-synthetic estimation method is given. For zero-failure data of double-parameter exponential distribution, a hierarchical Bayesian estimation of the failure probability is presented. After failure information is introduced, hierarchical Bayesian estimation and synthetic estimation of the failure probability, as well as synthetic estimation of reliability are given. Calculation and analysis are performed regarding practical problems in case that life distribution of an engine obeys double-parameter exponential distribution.
文摘Heart failure prediction is crucial as cardiovascular diseases become the leading cause of death worldwide,exacerbated by the COVID-19 pandemic.Age,cholesterol,and blood pressure datasets are becoming inadequate because they cannot capture the complexity of emerging health indicators.These high-dimensional and heterogeneous datasets make traditional machine learning methods difficult,and Skewness and other new biomarkers and psychosocial factors bias the model’s heart health prediction across diverse patient profiles.Modern medical datasets’complexity and high dimensionality challenge traditional predictionmodels like SupportVectorMachines and Decision Trees.Quantum approaches include QSVM,QkNN,QDT,and others.These Constraints drove research.The“QHF-CS:Quantum-Enhanced Heart Failure Prediction using Quantum CNN with Optimized Feature Qubit Selection with Cuckoo Search in Skewed Clinical Data”system was developed in this research.This novel system leverages a Quantum Convolutional Neural Network(QCNN)-based quantum circuit,enhanced by meta-heuristic algorithms—Cuckoo SearchOptimization(CSO),Artificial BeeColony(ABC),and Particle SwarmOptimization(PSO)—for feature qubit selection.Among these,CSO demonstrated superior performance by consistently identifying the most optimal and least skewed feature subsets,which were then encoded into quantum states for circuit construction.By integrating advanced quantum circuit feature maps like ZZFeatureMap,RealAmplitudes,and EfficientSU2,the QHF-CS model efficiently processes complex,high-dimensional data,capturing intricate patterns that classical models overlook.The QHF-CS model improves precision,recall,F1-score,and accuracy to 0.94,0.95,0.94,and 0.94.Quantum computing could revolutionize heart failure diagnostics by improving model accuracy and computational efficiency,enabling complex healthcare diagnostic breakthroughs.
基金the Ningbo University of Technology Science Foundation and Ningbo Natural Science Foundation(No.2013A610108)
文摘This paper introduces a new method, E-Bayesian estimation method, to estimate the reliability in zero-failure data. The definition of E-Bayesian estimation of the reliability is given. Based on the definition,the formulas of E-Bayesian estimation and hierarchical Bayesian estimation of the reliability are provided, and property of the E-Bayesian estimation, i.e. relation between E-Bayesian estimation and hierarchical Bayesian estimation, is discussed. Calculations performed on practical problems show that the proposed new method is feasible and easy to operate.
文摘For many products,distributions of their life mostly comply with increasing failure rates in average(IFRA).Aiming to these distributions,using properties of IFRA classification,this paper gives a non-parametric method for processing zero-failure data.Estimations of reliabilities in any time are first obtained,and based on a regression model of failure rates,estimations of reliability indexes are given.Finally,a practical example is processed with this method.
文摘The bearings of a certain type have their lives following a Weibull distribution. In a life test with 20 sets of bearings, only one set failed within the specified time, and none of the remainder failed even after the time of test has been extended. With a set of testing data like that in Table 1, it is required to estimate the reliability at the mission time. In this paper, we first use hierarchical Bayesian method to determine the prior distribution and the Bayesian estimates of various probabilities of failures, p i 's, then use the method of least squares to estimate the parameters of the Weibull distribution and the reliability. Actual computation shows that the estimates so obtained are rather robust. And the results have been adopted for practical use.
基金Supported by Fanzhou Science and Research Foundation for Young Scholars(Grant No.20100511)
文摘The small sample prediction problem which commonly exists in reliability analysis was discussed with the progressive prediction method in this paper.The modeling and estimation procedure,as well as the forecast and confidence limits formula of the progressive auto regressive(PAR) method were discussed in great detail.PAR model not only inherits the simple linear features of auto regressive(AR) model,but also has applicability for nonlinear systems.An application was illustrated for predicting the future fatigue failure for Tantalum electrolytic capacitors.Forecasting results of PAR model were compared with auto regressive moving average(ARMA) model,and it can be seen that the PAR method can be considered good and shows a promise for future applications.
基金Project(2014ZX04014-011)supported by State Key Science&Technology Program of ChinaProject([2016]414)supported by the 13th Five-year Program of Education Department of Jilin Province,China
文摘A new problem that classical statistical methods are incapable of solving is reliability modeling and assessment when multiple numerical control machine tools(NCMTs) reveal zero failures after a reliability test. Thus, the zero-failure data form and corresponding Bayesian model are developed to solve the zero-failure problem of NCMTs, for which no previous suitable statistical model has been developed. An expert-judgment process that incorporates prior information is presented to solve the difficulty in obtaining reliable prior distributions of Weibull parameters. The equations for the posterior distribution of the parameter vector and the Markov chain Monte Carlo(MCMC) algorithm are derived to solve the difficulty of calculating high-dimensional integration and to obtain parameter estimators. The proposed method is applied to a real case; a corresponding programming code and trick are developed to implement an MCMC simulation in Win BUGS, and a mean time between failures(MTBF) of 1057.9 h is obtained. Given its ability to combine expert judgment, prior information, and data, the proposed reliability modeling and assessment method under the zero failure of NCMTs is validated.
基金supported in part by the National Natural Science Foundation of China (No.61602114)part by the National Key Research and Development Program of China (2017YFB0801703)+1 种基金part by the CERNET Innovation Project (NGII20170406)part by Jiangsu Provincial Key Laboratory of Network and Information Security (BM2003201)
文摘We show that an aggregated Interest in Named Data Networking (NDN) may fail to retrieve desired data since the Interest previously sent upstream for the same content is judged as a duplicate one and then dropped by an upstream node due to its multipath forwarding. Furthermore, we propose NDRUDAF, a NACK based mechanism that enhances the Interest forwarding and enables Detection and fast Recovery from such Unanticipated Data Access Failure. In the NDN enhanced with NDRUDAF, the router that aggregates the Interest detects such unanticipated data access failure based on a negative acknowledgement from the upstream node that judges the Interest as a duplicate one. Then the router retransmits the Interest as soon as possible on behalf of the requester whose Interest is aggregated to fast recover from the data access failure. We qualitatively and quantitatively analyze the performance of the NDN enhanced with our proposed NDRUDAF and compare it with that of the present NDN. Our experimental results validate that NDRUDAF improves the system performance in case of such unanticipated data access failure in terms of data access delay and network resource utilization efficiency at routers.
基金jointly supported by the National Natural Science Foundation (Nos.61175008,60935001)National Basic Research Program of China (No.2009CB824900)+1 种基金the Space Foundation of Supporting-Technology (No.2011-HTSHJD002)the Aeronautical Science Foundation of China (No.20105557007)
文摘Interval-valued data and incomplete data are two key problems for failure analysis of thruster experimental data and have been basically solved by the proposed methods in this paper. Firstly, information data acquired from the simulation and evaluation system formed as intervalvalued information system （IIS） is classified by the interval similarity relation. Then, as an improvement of the classical rough set, a new kind of generalized information entropy called ＂H＇-information entropy＂ is suggested for the measurement of uncertainty and the classification ability of IIS. There is an innovative information filling technique using the properties of H＇-information entropy to replace missing data by some smaller estimation intervals. Finally, an improved method of failure analysis synthesized by the above achievements is presented to classify the thruster experimental data, complete the information, and extract the failure rules. The feasibility and advantage of this method is testified by an actual application of failure analysis, whose performance is evaluated by the quantification of E-condition entropy.
基金supported in part by National Key Research and Development Program of China(2019YFB2103200)NSFC(61672108),Open Subject Funds of Science and Technology on Information Transmission and Dissemination in Communication Networks Laboratory(SKX182010049)+1 种基金the Fundamental Research Funds for the Central Universities(5004193192019PTB-019)the Industrial Internet Innovation and Development Project 2018 of China.
文摘The development of cloud computing and virtualization technology has brought great challenges to the reliability of data center services.Data centers typically contain a large number of compute and storage nodes which may fail and affect the quality of service.Failure prediction is an important means of ensuring service availability.Predicting node failure in cloud-based data centers is challenging because the failure symptoms reflected have complex characteristics,and the distribution imbalance between the failure sample and the normal sample is widespread,resulting in inaccurate failure prediction.Targeting these challenges,this paper proposes a novel failure prediction method FP-STE(Failure Prediction based on Spatio-temporal Feature Extraction).Firstly,an improved recurrent neural network HW-GRU(Improved GRU based on HighWay network)and a convolutional neural network CNN are used to extract the temporal features and spatial features of multivariate data respectively to increase the discrimination of different types of failure symptoms which improves the accuracy of prediction.Then the intermediate results of the two models are added as features into SCSXGBoost to predict the possibility and the precise type of node failure in the future.SCS-XGBoost is an ensemble learning model that is improved by the integrated strategy of oversampling and cost-sensitive learning.Experimental results based on real data sets confirm the effectiveness and superiority of FP-STE.
基金Supported by the National Natural Science Foundation of China (11171263)
文摘Multivariate failure time data are frequently encountered in biomedical research.In this article,we model marginal hazards with accelerated hazards model to analyze multivariate failure time data.Estimating equations are derived analogous to generalized estimating equation method.Under certain regular conditions,the resultant estimators for the regression parameters are shown to be asymptotically normal.Furthermore,we also establish the weak convergence of estimators for the baseline cumulative hazard functions.
基金supported by National Natural Science Foundation of China (61703410,61873175,62073336,61873273,61773386,61922089)。
文摘Remaining useful life(RUL) prediction is one of the most crucial elements in prognostics and health management(PHM). Aiming at the imperfect prior information, this paper proposes an RUL prediction method based on a nonlinear random coefficient regression(RCR) model with fusing failure time data.Firstly, some interesting natures of parameters estimation based on the nonlinear RCR model are given. Based on these natures,the failure time data can be fused as the prior information reasonably. Specifically, the fixed parameters are calculated by the field degradation data of the evaluated equipment and the prior information of random coefficient is estimated with fusing the failure time data of congeneric equipment. Then, the prior information of the random coefficient is updated online under the Bayesian framework, the probability density function(PDF) of the RUL with considering the limitation of the failure threshold is performed. Finally, two case studies are used for experimental verification. Compared with the traditional Bayesian method, the proposed method can effectively reduce the influence of imperfect prior information and improve the accuracy of RUL prediction.
文摘We study the two-dimensional(2D)Cauchy problem of nonhomogeneous Boussinesq system for magnetohydrodynamics convection without heat diffusion in the whole plane.Based on delicate weighted estimates,we derive the global existence and uniqueness of strong solutions.In particular,the initial data can be arbitrarily large and the initial density may contain vacuum states and even have compact support.
基金Supported by the National Natural Science Foundation of China(No.51405424,51675461,11673040)
文摘As a bladder accumulator is a high reliable and long life component in a hydraulic system,its cost is high and it takes a lot of time to test its reliability,therefore,a reliability test with small sample is performed,and no failure data is obtained using the method of fixed time truncation. In the case of Weibull distribution,a life reliability model of bladder energy storage is established by Bayesian method using the optimal confidence intervals method,a model of one-sided lower confidence intervals of the reliability and one-sided lower confidence intervals model of the reliability life are established. Results of experiments show that the evaluation method of no failure data under Weibull distribution is a good way to evaluate the reliability of the accumulator,which is convenient for engineering application,and the reliability of the accumulator has theoretical and practical significance.
文摘Current traffic signal split failure (SF) estimations derived from high-resolution controller event data rely on detector occupancy ratios and preset thresholds. The reliability of these techniques depends on the selected thresholds, detector lengths, and vehicle arrival patterns. Connected vehicle (CV) trajectory data can more definitively show when a vehicle split fails by evaluating the number of stops it experiences as it approaches an intersection, but it has limited market penetration. This paper compares cycle-by-cycle SF estimations from both high-resolution controller event data and CV trajectory data, and evaluates the effect of data aggregation on SF agreement between the two techniques. Results indicate that, in general, split failure events identified from CV data are likely to also be captured from high-resolution data, but split failure events identified from high-resolution data are less likely to be captured from CV data. This is due to the CV market penetration rate (MPR) of ~5% being too low to capture representative data for every controller cycle. However, data aggregation can increase the ratio in which CV data captures split failure events. For example, day-of-week data aggregation increased the percentage of split failures identified with high-resolution data that were also captured with CV data from 35% to 56%. It is recommended that aggregated CV data be used to estimate SF as it provides conservative and actionable results without the limitations of intersection and detector configuration. As the CV MPR increases, the accuracy of CV-based SF estimation will also improve.
文摘Falls remain a prevalent source of injury in daily life and underlying aetiology of falls are often complex and multi-factorial.[1,2]Older persons living with heart failure(OPLHF)are of a particular interest when discussing falls as multiple factors associated with heart failure(HF)aetiology and treatment are assumedly implicated in falls occurrence.A retrospective study reported a 14%increased risk of falls among OPLHF,and prospective data has shown that up to 40%of HF patients may experience a fall within a year from diagnosis.
