Modeling HIV/AIDS progression is critical for understanding disease dynamics and improving patient care. This study compares the Exponential and Weibull survival models, focusing on their ability to capture state-spec...Modeling HIV/AIDS progression is critical for understanding disease dynamics and improving patient care. This study compares the Exponential and Weibull survival models, focusing on their ability to capture state-specific failure rates in HIV/AIDS progression. While the Exponential model offers simplicity with a constant hazard rate, it often fails to accommodate the complexities of dynamic disease progression. In contrast, the Weibull model provides flexibility by allowing hazard rates to vary over time. Both models are evaluated within the frameworks of the Cox Proportional Hazards (Cox PH) and Accelerated Failure Time (AFT) models, incorporating critical covariates such as age, gender, CD4 count, and ART status. Statistical evaluation metrics, including Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), log-likelihood, and Pseudo-R2, were employed to assess model performance across diverse patient subgroups. Results indicate that the Weibull model consistently outperforms the Exponential model in dynamic scenarios, such as younger patients and those with co-infections, while maintaining robustness in stable contexts. This study highlights the trade-off between flexibility and simplicity in survival modeling, advocating for tailored model selection to balance interpretability and predictive accuracy. These findings provide valuable insights for optimizing HIV/AIDS management strategies and advancing survival analysis methodologies.展开更多
The frequent occurrence of rockburst and the difficulty in predicting were considered in deep engineering and underground engineering.In this work,laboratory experiments on rockburst under true triaxial conditions wer...The frequent occurrence of rockburst and the difficulty in predicting were considered in deep engineering and underground engineering.In this work,laboratory experiments on rockburst under true triaxial conditions were carried out with granite samples.Combined with the deformation characteristics of granite,acoustic emission(AE)technology was well applied in revealing the evolution law of micro-cracks in the process of rockburst.Based on the comprehensive analysis of acoustic emission parameters such as impact,ringing and energy,the phased characteristics of crack propagation and damage evolution in granite were obtained,which were consistent with the stages of rock deformation and failure.Subsequently,based on the critical point theory,the accelerated release characteristics of acoustic emission energy during rockburst were analyzed.Based on the damage theory,the damage evolution model of rock under different loading conditions was proposed,and the prediction interval of rock failure time was ascertained concurrently.Finally,regarding damage as an intermediate variable,the synergetic prediction model of rock failure time was constructed.The feasibility and validity of model were verified.展开更多
Aviation products would go through a multi-phase improvement in reliability performance during the research and development process.In the literature,most of the existing reliability growth models assume a constant fa...Aviation products would go through a multi-phase improvement in reliability performance during the research and development process.In the literature,most of the existing reliability growth models assume a constant failure intensity in each test phase,which inevitably limits the scope of the application.To address this problem,we propose two new models considering timevarying failure intensity in each stage.The proposed models borrow the idea from the accelerated failure-time models.It is assumed that time between failures follow the log-location-scale distribution and the scale parameters in each phase do not change,which forms the basis for integrating the data from all test stages.For the test-find-test scenario,an improvement factor is introduced to construct the relationship between two successive location parameters.Whereas for the test-fix-test scenario,the instantaneous cumulative time between failures is assumed to be consistent with Duane model and derive the formulation of location parameter.Likelihood ratio test is further utilized to test whether the assumption of constant failure intensity in each phase is suitable.Several applications with real reliability growth data show that the assumptions are reasonable and the proposed models outperform the existing models.展开更多
The data-driven phenomenological models based on deformation measurements have been widely utilized to predict the slope failure time(SFT).The observational and model uncertainties could lead the predicted SFT calcula...The data-driven phenomenological models based on deformation measurements have been widely utilized to predict the slope failure time(SFT).The observational and model uncertainties could lead the predicted SFT calculated from the phenomenological models to deviate from the actual SFT.Currently,very limited study has been conducted on how to evaluate the effect of such uncertainties on SFT prediction.In this paper,a comprehensive slope failure database was compiled.A Bayesian machine learning(BML)-based method was developed to learn the model and observational uncertainties involved in SFT prediction,through which the probabilistic distribution of the SFT can be obtained.This method was illustrated in detail with an example.Verification studies show that the BML-based method is superior to the traditional inverse velocity method(INVM)and the maximum likelihood method for predicting SFT.The proposed method in this study provides an effective tool for SFT prediction.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
Radar slope monitoring is now widely used across the world, for example, the slope stability radar(SSR)and the movement and surveying radar(MSR) are currently in use in many mines around the world.However, to fully re...Radar slope monitoring is now widely used across the world, for example, the slope stability radar(SSR)and the movement and surveying radar(MSR) are currently in use in many mines around the world.However, to fully realize the effectiveness of this radar in notifying mine personnel of an impending slope failure, a method that can confidently predict the time of failure is necessary. The model developed in this study is based on the inverse velocity method pioneered by Fukuzono in 1985. The model named the slope failure prediction model(SFPM) was validated with the displacement data from two slope failures monitored with the MSR. The model was found to be very effective in predicting the time to failure while providing adequate evacuation time once the progressive displacement stage is reached.展开更多
Objective:To compare the prognostic factors of mortality among melioidosis patients between lognormal accelerated failure time(AFT),Cox proportional hazards(PH),and Cox PH with time-varying coefficient(TVC)models.Meth...Objective:To compare the prognostic factors of mortality among melioidosis patients between lognormal accelerated failure time(AFT),Cox proportional hazards(PH),and Cox PH with time-varying coefficient(TVC)models.Methods:A retrospective study was conducted from 2014 to 2019 among 453 patients who were admitted to Hospital Sultanah Bahiyah,Kedah and Hospital Tuanku Fauziah,Perlis in Northern Malaysia due to confirmed-cultured melioidosis.The prognostic factors of mortality from melioidosis were obtained from AFT survival analysis,and Cox’s models and the findings were compared by using the goodness of fit methods.The analyses were done by using Stata SE version 14.0.Results:A total of 242 patients(53.4%)survived.In this study,the median survival time of melioidosis patients was 30.0 days(95%CI 0.0-60.9).Six significant prognostic factors were identified in the Cox PH model and Cox PH-TVC model.In AFT survival analysis,a total of seven significant prognostic factors were identified.The results were found to be only a slight difference between the identified prognostic factors among the models.AFT survival showed better results compared to Cox's models,with the lowest Akaike information criteria and best fitted Cox-snell residuals.Conclusions:AFT survival analysis provides more reliable results and can be used as an alternative statistical analysis for determining the prognostic factors of mortality in melioidosis patients in certain situations.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
An analytical moment-based method for calculating structuralfirst failure times under non-Gaussian stochastic behavior is proposed. In the method, a power series that constants can be obtained from response moments （...An analytical moment-based method for calculating structuralfirst failure times under non-Gaussian stochastic behavior is proposed. In the method, a power series that constants can be obtained from response moments （skewness, kurtosis, etc.） is used firstly to map a non-Gaussian structural response into a standard Gaussian process, then mean up-crossing rates, mean clump size and the initial passage probability of a critical barrier level by the original structural response are estimated, and finally, the formula for calculating first failure times is established on the assur^ption that corrected up-crossing rates are independent. An analysis of a nonlinear single-degree-of-freedom dynamical system excited by a Gaussian model of load not only demonstrates the usage of the proposed method but also shows the accuracy and efficiency of the proposed method by comparisons between the present method and other methods such as Monte Carlo simulation and the traditional Gaussian model.展开更多
This paper presents a robust adaptive state feedback control scheme for a class of parametric-strict-feedback nonlinear systems in the presence of time varying actuator failures. The designed adaptive controller compe...This paper presents a robust adaptive state feedback control scheme for a class of parametric-strict-feedback nonlinear systems in the presence of time varying actuator failures. The designed adaptive controller compensates a general class of actuator failures without any need for explicit fault detection. The parameters, times, and patterns of the considered failures are completely unknown. The proposed controller is constructed based on a backstepping design method. The global boundedness of all the closed-loop signals is guaranteed and the tracking error is proved to converge to a small neighborhood of the origin. The proposed approach is employed for a two-axis positioning stage system as well as an aircraft wing system. The simulation results show the correctness and effectiveness of the proposed robust adaptive actuator failure compensation approach.展开更多
The prediction of long term failure behaviors and lifetime of aged glass polymers from the short term tests of reduced rupture creep compliance （or strain） is one of difficult problems in polymer science and enginee...The prediction of long term failure behaviors and lifetime of aged glass polymers from the short term tests of reduced rupture creep compliance （or strain） is one of difficult problems in polymer science and engineering. A new ＂universal reduced rupture creep approach＂ with exact theoretical analysis and computations is proposed in this work. Failure by creep for polymeric material is an important problem to be addressed in the engineering. A universal equation on reduced extensional failure creep compliance for PMMA has been derived. It is successful in relating the reduced extensional failure creep compliance with aging time, temperature, levels of stress, the average growth dimensional number and the parameter in K-W-W function. Based on the universal equation, a method for the prediction of failure behavior, failure strain criterion, failure time of PMMA has been developed which is named as a universal ＂reduced rupture creep approach＂. The results show that the predicted failure strain and failure time of PMMA at different aging times for different levels of stress are all in agreement with those obtained directly from experiments, and the proposed method is reliable and practical. The dependences of reduced extensional failure creep compliance on the conditions of aging time, failure creep stress, the structure of fluidized-domain constituent chains are discussed. The shifting factor, exponent for time-stress superposition at different levels of stress and the shifting factor, exponent for time-time aging superposition at different aging time are theoretically defined respectively.展开更多
In this paper, we have studied the nonparameter accelerated failure time (AFT) additive regression model, whose covariates have a nonparametric effect on high-dimensional censored data. We give the asymptotic property...In this paper, we have studied the nonparameter accelerated failure time (AFT) additive regression model, whose covariates have a nonparametric effect on high-dimensional censored data. We give the asymptotic property of the penalty estimator based on GMCP in the nonparameter AFT model.展开更多
Most reliability studies assume large samples or independence among components,but these assump-tions often fail in practice,leading to imprecise inference.We address this issue by constructing confidence intervals(CI...Most reliability studies assume large samples or independence among components,but these assump-tions often fail in practice,leading to imprecise inference.We address this issue by constructing confidence intervals(CIs)for the reliability of two-component systems with Weibull distributed failure times under a copula-frailty framework.Our construction integrates gamma-distributed frailties to capture unobserved heterogeneity and a copula-based dependence structure for correlated failures.The main contribution of this work is to derive adjusted CIs that explicitly incorporate the copula parameter in the variance-covariance matrix,achieving near-nominal coverage probabilities even in small samples or highly dependent settings.Through simulation studies,we show that,although traditional methods may suffice with moderate dependence and large samples,the proposed CIs offer notable benefits when dependence is strong or data are sparse.We further illustrate our construction with a synthetic example illustrating how penalized estimation can mitigate the issue of a degenerate Hessian matrix under high dependence and limited observations,so enabling uncertainty quantification despite deviations from nominal assumptions.Overall,our results fill a gap in reliability modeling for systems prone to correlated failures,and contribute to more robust inference in engineering,industrial,and biomedical applications.展开更多
The identification of rock stability and the prediction of failure time are crucial for the early warning and prevention of sudden geological disasters such as landslides and collapses.To address these challenges,this...The identification of rock stability and the prediction of failure time are crucial for the early warning and prevention of sudden geological disasters such as landslides and collapses.To address these challenges,this study proposes three convolutional prediction models:CNN-LSTM-Attention,CNN-BiLSTM-Attention,and CNN-GRUAttention.The displacement coordination coefficient(DCC)index and stress curves were employed as input variables to evaluate the performance of each model in discriminating rock stability states under different data structures and input configurations.Furthermore,an innovative methodology for predicting rock failure time utilizing convolutional models was developed.The experimental results demonstrate that the CNN-LSTMAttention model,utilizing a 10×10×2 data structure,exhibits superior performance in rock stability state discrimination,achieving an accuracy of 95.25%on the validation set and a recall rate of 96%for samples in high-risk areas.Furthermore,when the DCC index was used as the input variable,the CNN-LSTM-Attention model achieved recall rates of 95.8%and 86.5%for medium-and high-risk areas,respectively,in the validation set.These findings indicate that the proposed convolutional models can effectively identify rock stability states by leveraging surface deformation characteristics.The CNN-LSTM-Attention model,with the DCC index as the input variable,is capable of predicting the final rock failure time in real-time once the DCC abrupt change exceeds 0.78.For different rocks,the model can predict the failure time within 20 s after the DCC reaches 0.78,with an error rate of less than 9%.The convolutional neural network model,developed based on the DCC index,provides a novel methodological approach for geohazard early warning research,facilitating slope instability monitoring and earthquake precursor identification using GNSS and other displacement measurement techniques.展开更多
Although Markov chain Monte Carlo（MCMC） algorithms are accurate, many factors may cause instability when they are utilized in reliability analysis; such instability makes these algorithms unsuitable for widespread e...Although Markov chain Monte Carlo（MCMC） algorithms are accurate, many factors may cause instability when they are utilized in reliability analysis; such instability makes these algorithms unsuitable for widespread engineering applications. Thus, a reliability modeling and assessment solution aimed at small-sample data of numerical control（NC） machine tools is proposed on the basis of Bayes theories. An expert-judgment process of fusing multi-source prior information is developed to obtain the Weibull parameters＇ prior distributions and reduce the subjective bias of usual expert-judgment methods. The grid approximation method is applied to two-parameter Weibull distribution to derive the formulas for the parameters＇ posterior distributions and solve the calculation difficulty of high-dimensional integration. The method is then applied to the real data of a type of NC machine tool to implement a reliability assessment and obtain the mean time between failures（MTBF）. The relative error of the proposed method is 5.8020×10-4 compared with the MTBF obtained by the MCMC algorithm. This result indicates that the proposed method is as accurate as MCMC. The newly developed solution for reliability modeling and assessment of NC machine tools under small-sample data is easy, practical, and highly suitable for widespread application in the engineering field; in addition, the solution does not reduce accuracy.展开更多
Winding is an important part of the electrical machine and plays a key role in reliability.In this paper,the reliability of multiphase winding structure in permanent magnet machines is evaluated based on the Markov mo...Winding is an important part of the electrical machine and plays a key role in reliability.In this paper,the reliability of multiphase winding structure in permanent magnet machines is evaluated based on the Markov model.The mean time to failure is used to compare the reliability of different windings structure.The mean time to failure of multiphase winding is derived in terms of the underlying parameters.The mean time to failure of winding is affected by the number of phases,the winding failure rate,the fault-tolerant mechanism success probability,and the state transition success probability.The influence of the phase number,winding distribution types,multi three-phase structure,and fault-tolerant mechanism success probability on the winding reliability is investigated.The results of reliability analysis lay the foundation for the reliability design of permanent magnet machines.展开更多
The reliability of electromechanical product is usually determined by the fault number and working time traditionally. The shortcoming of this method is that the product must be in service. To design and enhance the r...The reliability of electromechanical product is usually determined by the fault number and working time traditionally. The shortcoming of this method is that the product must be in service. To design and enhance the reliability of the electromechanical product, the reliability evaluation method must be feasible and correct. Reliability evaluation method and algorithm were proposed. The reliability of product can be calculated by the reliability of subsystems which can be gained by experiment or historical data. The reliability of the machining center was evaluated by the method and algorithm as one example. The calculation result shows that the solution accuracy of mean time between failures is 97.4% calculated by the method proposed in this article compared by the traditional method. The method and algorithm can be used to evaluate the reliability of electromechanical product before it is in service.展开更多
A parallel system with two active components and a cold standby unit is studied in this paper. The two simultaneously working components are dependent and the copula function is used to model their dependence. An expl...A parallel system with two active components and a cold standby unit is studied in this paper. The two simultaneously working components are dependent and the copula function is used to model their dependence. An explicit expression is obtained for the mean time to failure of the system in terms of the copula function and marginal lifetime distributions in two different cases. As an application,numerical calculations are presented corresponding to two different copula functions and marginal lifetime distributions.展开更多
This study considers an age replacement policy(ARP) for a repairable product with an increasing failure rate with and without a product warranty. As for the warranty policy to consider in association with such an age ...This study considers an age replacement policy(ARP) for a repairable product with an increasing failure rate with and without a product warranty. As for the warranty policy to consider in association with such an age replacement policy, we adapt a renewable minimal repair-replacement warrant(MRRW) policy with 2D factors of failure time of the product and its corresponding repair time. The expected cost rate during the life cycle of the product is utilized as a criterion to find the optimal policies for both with and without the product warranty. We determine the optimal replacement age that minimizes the objective function which evaluates the expected cost rate during the product cycle and investigate the impact of several factors on the optimal replacement age. The main objective of this study lies on the generalization of the classical age replacement policy to the situation where a renewable warranty depending on 2D factors is in effect. We present some interesting observations regarding the effect of relevant factors based on numerical analysis.展开更多
Often in longitudinal studies, some subjects complete their follow-up visits, but others miss their visits due to various reasons. For those who miss follow-up visits, some of them might learn that the event of intere...Often in longitudinal studies, some subjects complete their follow-up visits, but others miss their visits due to various reasons. For those who miss follow-up visits, some of them might learn that the event of interest has already happened when they come back. In this case, not only are their event times interval-censored, but also their time-dependent measurements are incomplete. This problem was motivated by a national longitudinal survey of youth data. Maximum likelihood estimation (MLE) method based on expectation-maximization (EM) algorithm is used for parameter estimation. Then missing information principle is applied to estimate the variance-covariance matrix of the MLEs. Simulation studies demonstrate that the proposed method works well in terms of bias, standard error, and power for samples of moderate size. The national longitudinal survey of youth 1997 (NLSY97) data is analyzed for illustration.展开更多
文摘Modeling HIV/AIDS progression is critical for understanding disease dynamics and improving patient care. This study compares the Exponential and Weibull survival models, focusing on their ability to capture state-specific failure rates in HIV/AIDS progression. While the Exponential model offers simplicity with a constant hazard rate, it often fails to accommodate the complexities of dynamic disease progression. In contrast, the Weibull model provides flexibility by allowing hazard rates to vary over time. Both models are evaluated within the frameworks of the Cox Proportional Hazards (Cox PH) and Accelerated Failure Time (AFT) models, incorporating critical covariates such as age, gender, CD4 count, and ART status. Statistical evaluation metrics, including Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), log-likelihood, and Pseudo-R2, were employed to assess model performance across diverse patient subgroups. Results indicate that the Weibull model consistently outperforms the Exponential model in dynamic scenarios, such as younger patients and those with co-infections, while maintaining robustness in stable contexts. This study highlights the trade-off between flexibility and simplicity in survival modeling, advocating for tailored model selection to balance interpretability and predictive accuracy. These findings provide valuable insights for optimizing HIV/AIDS management strategies and advancing survival analysis methodologies.
基金Projects(52074294,51574246,51674008)supported by the National Natural Science Foundation of ChinaProjects(2017YFC0804201,2017YFC0603000)supported by the National Key Research and Development Program of ChinaProject(2011QZ01)supported by the Fundamental Research Funds for the Central Universities,China。
文摘The frequent occurrence of rockburst and the difficulty in predicting were considered in deep engineering and underground engineering.In this work,laboratory experiments on rockburst under true triaxial conditions were carried out with granite samples.Combined with the deformation characteristics of granite,acoustic emission(AE)technology was well applied in revealing the evolution law of micro-cracks in the process of rockburst.Based on the comprehensive analysis of acoustic emission parameters such as impact,ringing and energy,the phased characteristics of crack propagation and damage evolution in granite were obtained,which were consistent with the stages of rock deformation and failure.Subsequently,based on the critical point theory,the accelerated release characteristics of acoustic emission energy during rockburst were analyzed.Based on the damage theory,the damage evolution model of rock under different loading conditions was proposed,and the prediction interval of rock failure time was ascertained concurrently.Finally,regarding damage as an intermediate variable,the synergetic prediction model of rock failure time was constructed.The feasibility and validity of model were verified.
基金co-supported by the National Natural Science Foundation of China(No.52075019)the Academic Excellence Foundation of BUAA for PhD Students,China。
文摘Aviation products would go through a multi-phase improvement in reliability performance during the research and development process.In the literature,most of the existing reliability growth models assume a constant failure intensity in each test phase,which inevitably limits the scope of the application.To address this problem,we propose two new models considering timevarying failure intensity in each stage.The proposed models borrow the idea from the accelerated failure-time models.It is assumed that time between failures follow the log-location-scale distribution and the scale parameters in each phase do not change,which forms the basis for integrating the data from all test stages.For the test-find-test scenario,an improvement factor is introduced to construct the relationship between two successive location parameters.Whereas for the test-fix-test scenario,the instantaneous cumulative time between failures is assumed to be consistent with Duane model and derive the formulation of location parameter.Likelihood ratio test is further utilized to test whether the assumption of constant failure intensity in each phase is suitable.Several applications with real reliability growth data show that the assumptions are reasonable and the proposed models outperform the existing models.
基金substantially supported by the Shuguang Program from Shanghai Education Development FoundationShanghai Municipal Education Commission, China (Grant No. 19SG19)+1 种基金National Natural Science Foundation of China (Grant No. 42072302)Fundamental Research Funds for the Central Universities, China
文摘The data-driven phenomenological models based on deformation measurements have been widely utilized to predict the slope failure time(SFT).The observational and model uncertainties could lead the predicted SFT calculated from the phenomenological models to deviate from the actual SFT.Currently,very limited study has been conducted on how to evaluate the effect of such uncertainties on SFT prediction.In this paper,a comprehensive slope failure database was compiled.A Bayesian machine learning(BML)-based method was developed to learn the model and observational uncertainties involved in SFT prediction,through which the probabilistic distribution of the SFT can be obtained.This method was illustrated in detail with an example.Verification studies show that the BML-based method is superior to the traditional inverse velocity method(INVM)and the maximum likelihood method for predicting SFT.The proposed method in this study provides an effective tool for SFT prediction.
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.
基金supported by the Centennial Trust Fund, School of Mining Engineering, University of the Witwatersrand, South Africa
文摘Radar slope monitoring is now widely used across the world, for example, the slope stability radar(SSR)and the movement and surveying radar(MSR) are currently in use in many mines around the world.However, to fully realize the effectiveness of this radar in notifying mine personnel of an impending slope failure, a method that can confidently predict the time of failure is necessary. The model developed in this study is based on the inverse velocity method pioneered by Fukuzono in 1985. The model named the slope failure prediction model(SFPM) was validated with the displacement data from two slope failures monitored with the MSR. The model was found to be very effective in predicting the time to failure while providing adequate evacuation time once the progressive displacement stage is reached.
文摘Objective:To compare the prognostic factors of mortality among melioidosis patients between lognormal accelerated failure time(AFT),Cox proportional hazards(PH),and Cox PH with time-varying coefficient(TVC)models.Methods:A retrospective study was conducted from 2014 to 2019 among 453 patients who were admitted to Hospital Sultanah Bahiyah,Kedah and Hospital Tuanku Fauziah,Perlis in Northern Malaysia due to confirmed-cultured melioidosis.The prognostic factors of mortality from melioidosis were obtained from AFT survival analysis,and Cox’s models and the findings were compared by using the goodness of fit methods.The analyses were done by using Stata SE version 14.0.Results:A total of 242 patients(53.4%)survived.In this study,the median survival time of melioidosis patients was 30.0 days(95%CI 0.0-60.9).Six significant prognostic factors were identified in the Cox PH model and Cox PH-TVC model.In AFT survival analysis,a total of seven significant prognostic factors were identified.The results were found to be only a slight difference between the identified prognostic factors among the models.AFT survival showed better results compared to Cox's models,with the lowest Akaike information criteria and best fitted Cox-snell residuals.Conclusions:AFT survival analysis provides more reliable results and can be used as an alternative statistical analysis for determining the prognostic factors of mortality in melioidosis patients in certain situations.
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Project supported by the National Natural Science Foundation Of China (No.50478017)
文摘An analytical moment-based method for calculating structuralfirst failure times under non-Gaussian stochastic behavior is proposed. In the method, a power series that constants can be obtained from response moments （skewness, kurtosis, etc.） is used firstly to map a non-Gaussian structural response into a standard Gaussian process, then mean up-crossing rates, mean clump size and the initial passage probability of a critical barrier level by the original structural response are estimated, and finally, the formula for calculating first failure times is established on the assur^ption that corrected up-crossing rates are independent. An analysis of a nonlinear single-degree-of-freedom dynamical system excited by a Gaussian model of load not only demonstrates the usage of the proposed method but also shows the accuracy and efficiency of the proposed method by comparisons between the present method and other methods such as Monte Carlo simulation and the traditional Gaussian model.
基金supported by Esfahan Regional Electric Company(EREC)
文摘This paper presents a robust adaptive state feedback control scheme for a class of parametric-strict-feedback nonlinear systems in the presence of time varying actuator failures. The designed adaptive controller compensates a general class of actuator failures without any need for explicit fault detection. The parameters, times, and patterns of the considered failures are completely unknown. The proposed controller is constructed based on a backstepping design method. The global boundedness of all the closed-loop signals is guaranteed and the tracking error is proved to converge to a small neighborhood of the origin. The proposed approach is employed for a two-axis positioning stage system as well as an aircraft wing system. The simulation results show the correctness and effectiveness of the proposed robust adaptive actuator failure compensation approach.
文摘The prediction of long term failure behaviors and lifetime of aged glass polymers from the short term tests of reduced rupture creep compliance （or strain） is one of difficult problems in polymer science and engineering. A new ＂universal reduced rupture creep approach＂ with exact theoretical analysis and computations is proposed in this work. Failure by creep for polymeric material is an important problem to be addressed in the engineering. A universal equation on reduced extensional failure creep compliance for PMMA has been derived. It is successful in relating the reduced extensional failure creep compliance with aging time, temperature, levels of stress, the average growth dimensional number and the parameter in K-W-W function. Based on the universal equation, a method for the prediction of failure behavior, failure strain criterion, failure time of PMMA has been developed which is named as a universal ＂reduced rupture creep approach＂. The results show that the predicted failure strain and failure time of PMMA at different aging times for different levels of stress are all in agreement with those obtained directly from experiments, and the proposed method is reliable and practical. The dependences of reduced extensional failure creep compliance on the conditions of aging time, failure creep stress, the structure of fluidized-domain constituent chains are discussed. The shifting factor, exponent for time-stress superposition at different levels of stress and the shifting factor, exponent for time-time aging superposition at different aging time are theoretically defined respectively.
文摘In this paper, we have studied the nonparameter accelerated failure time (AFT) additive regression model, whose covariates have a nonparametric effect on high-dimensional censored data. We give the asymptotic property of the penalty estimator based on GMCP in the nonparameter AFT model.
基金supported by the Colombian government through COLCIENCIA scholarships,National Doctoral Program,Call 727 of 2015C.Castro gratefully acknowledges partial financial support from the Centro de Matematica da Universidade do Minho(CMAT/UM),through UID/00013V.Leiva acknowledges funding from the Agencia Nacional de Investigacion y Desarrollo(ANID)of the Chilean Ministry of Science,Technology,Knowledge and Innovation,through FONDECYT project grant 1200525.
文摘Most reliability studies assume large samples or independence among components,but these assump-tions often fail in practice,leading to imprecise inference.We address this issue by constructing confidence intervals(CIs)for the reliability of two-component systems with Weibull distributed failure times under a copula-frailty framework.Our construction integrates gamma-distributed frailties to capture unobserved heterogeneity and a copula-based dependence structure for correlated failures.The main contribution of this work is to derive adjusted CIs that explicitly incorporate the copula parameter in the variance-covariance matrix,achieving near-nominal coverage probabilities even in small samples or highly dependent settings.Through simulation studies,we show that,although traditional methods may suffice with moderate dependence and large samples,the proposed CIs offer notable benefits when dependence is strong or data are sparse.We further illustrate our construction with a synthetic example illustrating how penalized estimation can mitigate the issue of a degenerate Hessian matrix under high dependence and limited observations,so enabling uncertainty quantification despite deviations from nominal assumptions.Overall,our results fill a gap in reliability modeling for systems prone to correlated failures,and contribute to more robust inference in engineering,industrial,and biomedical applications.
基金supported by the National Natural Science Foundation of China(No.52474106).
文摘The identification of rock stability and the prediction of failure time are crucial for the early warning and prevention of sudden geological disasters such as landslides and collapses.To address these challenges,this study proposes three convolutional prediction models:CNN-LSTM-Attention,CNN-BiLSTM-Attention,and CNN-GRUAttention.The displacement coordination coefficient(DCC)index and stress curves were employed as input variables to evaluate the performance of each model in discriminating rock stability states under different data structures and input configurations.Furthermore,an innovative methodology for predicting rock failure time utilizing convolutional models was developed.The experimental results demonstrate that the CNN-LSTMAttention model,utilizing a 10×10×2 data structure,exhibits superior performance in rock stability state discrimination,achieving an accuracy of 95.25%on the validation set and a recall rate of 96%for samples in high-risk areas.Furthermore,when the DCC index was used as the input variable,the CNN-LSTM-Attention model achieved recall rates of 95.8%and 86.5%for medium-and high-risk areas,respectively,in the validation set.These findings indicate that the proposed convolutional models can effectively identify rock stability states by leveraging surface deformation characteristics.The CNN-LSTM-Attention model,with the DCC index as the input variable,is capable of predicting the final rock failure time in real-time once the DCC abrupt change exceeds 0.78.For different rocks,the model can predict the failure time within 20 s after the DCC reaches 0.78,with an error rate of less than 9%.The convolutional neural network model,developed based on the DCC index,provides a novel methodological approach for geohazard early warning research,facilitating slope instability monitoring and earthquake precursor identification using GNSS and other displacement measurement techniques.
基金Supported by Research on Reliability Assessment and Test Methods of Heavy Machine Tools,China(State Key Science&Technology Project High-grade NC Machine Tools and Basic Manufacturing Equipment,Grant No.2014ZX04014-011)Reliability Modeling of Machining Centers Considering the Cutting Loads,China(Science&Technology Development Plan for Jilin Province,Grant No.3D513S292414)Graduate Innovation Fund of Jilin University,China(Grant No.2014053)
文摘Although Markov chain Monte Carlo（MCMC） algorithms are accurate, many factors may cause instability when they are utilized in reliability analysis; such instability makes these algorithms unsuitable for widespread engineering applications. Thus, a reliability modeling and assessment solution aimed at small-sample data of numerical control（NC） machine tools is proposed on the basis of Bayes theories. An expert-judgment process of fusing multi-source prior information is developed to obtain the Weibull parameters＇ prior distributions and reduce the subjective bias of usual expert-judgment methods. The grid approximation method is applied to two-parameter Weibull distribution to derive the formulas for the parameters＇ posterior distributions and solve the calculation difficulty of high-dimensional integration. The method is then applied to the real data of a type of NC machine tool to implement a reliability assessment and obtain the mean time between failures（MTBF）. The relative error of the proposed method is 5.8020×10-4 compared with the MTBF obtained by the MCMC algorithm. This result indicates that the proposed method is as accurate as MCMC. The newly developed solution for reliability modeling and assessment of NC machine tools under small-sample data is easy, practical, and highly suitable for widespread application in the engineering field; in addition, the solution does not reduce accuracy.
文摘Winding is an important part of the electrical machine and plays a key role in reliability.In this paper,the reliability of multiphase winding structure in permanent magnet machines is evaluated based on the Markov model.The mean time to failure is used to compare the reliability of different windings structure.The mean time to failure of multiphase winding is derived in terms of the underlying parameters.The mean time to failure of winding is affected by the number of phases,the winding failure rate,the fault-tolerant mechanism success probability,and the state transition success probability.The influence of the phase number,winding distribution types,multi three-phase structure,and fault-tolerant mechanism success probability on the winding reliability is investigated.The results of reliability analysis lay the foundation for the reliability design of permanent magnet machines.
基金Project(2013ZX04013047)supported by the Major Program of National Natural Science Foundation of ChinaProject(51275014)supported by the National Natural Science Foundation of China
文摘The reliability of electromechanical product is usually determined by the fault number and working time traditionally. The shortcoming of this method is that the product must be in service. To design and enhance the reliability of the electromechanical product, the reliability evaluation method must be feasible and correct. Reliability evaluation method and algorithm were proposed. The reliability of product can be calculated by the reliability of subsystems which can be gained by experiment or historical data. The reliability of the machining center was evaluated by the method and algorithm as one example. The calculation result shows that the solution accuracy of mean time between failures is 97.4% calculated by the method proposed in this article compared by the traditional method. The method and algorithm can be used to evaluate the reliability of electromechanical product before it is in service.
基金Funded by the Natural Science Foundation of China under grant No.71101095
文摘A parallel system with two active components and a cold standby unit is studied in this paper. The two simultaneously working components are dependent and the copula function is used to model their dependence. An explicit expression is obtained for the mean time to failure of the system in terms of the copula function and marginal lifetime distributions in two different cases. As an application,numerical calculations are presented corresponding to two different copula functions and marginal lifetime distributions.
基金the National Research Foundation of Korea Grant(NRF-2014S1A5A8012594)the 2014Hongik University Research Fund,the Basic Science Research Program Through the National Research Foundation of Korea(Nos.2013-2058436 and 2011-0022397)the Basic Science Research Program Through the National Research Foundation of Korea
文摘This study considers an age replacement policy(ARP) for a repairable product with an increasing failure rate with and without a product warranty. As for the warranty policy to consider in association with such an age replacement policy, we adapt a renewable minimal repair-replacement warrant(MRRW) policy with 2D factors of failure time of the product and its corresponding repair time. The expected cost rate during the life cycle of the product is utilized as a criterion to find the optimal policies for both with and without the product warranty. We determine the optimal replacement age that minimizes the objective function which evaluates the expected cost rate during the product cycle and investigate the impact of several factors on the optimal replacement age. The main objective of this study lies on the generalization of the classical age replacement policy to the situation where a renewable warranty depending on 2D factors is in effect. We present some interesting observations regarding the effect of relevant factors based on numerical analysis.
文摘Often in longitudinal studies, some subjects complete their follow-up visits, but others miss their visits due to various reasons. For those who miss follow-up visits, some of them might learn that the event of interest has already happened when they come back. In this case, not only are their event times interval-censored, but also their time-dependent measurements are incomplete. This problem was motivated by a national longitudinal survey of youth data. Maximum likelihood estimation (MLE) method based on expectation-maximization (EM) algorithm is used for parameter estimation. Then missing information principle is applied to estimate the variance-covariance matrix of the MLEs. Simulation studies demonstrate that the proposed method works well in terms of bias, standard error, and power for samples of moderate size. The national longitudinal survey of youth 1997 (NLSY97) data is analyzed for illustration.
