In order to evaluate the reliability of long-lifetime products with degradation data, a new proportional hazard degradation model is proposed. By the similarity between time-degradation data and stress-accelerated lif...In order to evaluate the reliability of long-lifetime products with degradation data, a new proportional hazard degradation model is proposed. By the similarity between time-degradation data and stress-accelerated lifetime, and the failure rate function of degradation data which is assumed to be proportional to the time covariate, the reliability assessment based on a proportional hazard degradation model is realized. The least squares method is used to estimate the model＇s parameters. Based on the failure rate of the degradation data and the proportion function of the known time, the failure rate and the reliability function under the given time and the predetermined failure threshold can be extrapolated. A long life GaAs laser is selected as a case study and its reliability is evaluated. The results show that the proposed method can accurately describe the degradation process and it is effective for the reliability assessment of long lifetime products.展开更多
Considering the dependence and competitive relation-ship between traumatic failure and degradation,the reliability assessment of products based on competing failure analysis is studied.The hazard rate of traumatic fai...Considering the dependence and competitive relation-ship between traumatic failure and degradation,the reliability assessment of products based on competing failure analysis is studied.The hazard rate of traumatic failure is regarded as a Weibull distribution of the degradation performance,and the Wiener process is used to describe the degradation process.The parameters are estimated with the maximum likelihood estimation（MLE）method.A reliability model based on competing failure analysis is proposed.A case study of the GaAs lasers is given to validate the effectiveness of the model and its solving method.The results indicate that if only the degradation failure is considered,the estimated result will be comparably optimistic.Meanwhile,the correlation between the degradation and traumatic failure has a great influence on the accuracy of reliability assessment.展开更多
Remaining Useful Life(RUL)is one of the most important indicators to detect a component failure.RUL can be predicted by historical data by adopting a model-based method.The stochastic process models have become the mo...Remaining Useful Life(RUL)is one of the most important indicators to detect a component failure.RUL can be predicted by historical data by adopting a model-based method.The stochastic process models have become the most popular way to model degradation data for high-quality products,such as the Wiener process,gamma process and inverse Gaussian process.However,this leads to poor reliability assessment if the model is misspecified.Application of the Tweedie exponential dispersion(TED)process,including the above-mentioned classical stochastic processes as special cases,transforms the model selection problem into a parameter estimation problem dexterously.In this paper,we propose a TED process with random drifts for degradation data and a TeD process with random drifts and covariates for accelerated degradation data.A hierarchical Bayesian method is adopted to estimate the parameters of the proposed models.We also derive the failure-time distribution and the remaining useful life distribution for the proposed models.The simulation study shows that the proposed model outperforms the wrongly specified models.Two illustrative examples demonstrate the performance of the proposed TED process with random drifts and the TED process with random drifts and covariates.展开更多
基金The National Natural Science Foundation of China (No.50405021)
文摘In order to evaluate the reliability of long-lifetime products with degradation data, a new proportional hazard degradation model is proposed. By the similarity between time-degradation data and stress-accelerated lifetime, and the failure rate function of degradation data which is assumed to be proportional to the time covariate, the reliability assessment based on a proportional hazard degradation model is realized. The least squares method is used to estimate the model＇s parameters. Based on the failure rate of the degradation data and the proportion function of the known time, the failure rate and the reliability function under the given time and the predetermined failure threshold can be extrapolated. A long life GaAs laser is selected as a case study and its reliability is evaluated. The results show that the proposed method can accurately describe the degradation process and it is effective for the reliability assessment of long lifetime products.
基金The National Natural Science Foundation of China(No.50405021)
文摘Considering the dependence and competitive relation-ship between traumatic failure and degradation,the reliability assessment of products based on competing failure analysis is studied.The hazard rate of traumatic failure is regarded as a Weibull distribution of the degradation performance,and the Wiener process is used to describe the degradation process.The parameters are estimated with the maximum likelihood estimation（MLE）method.A reliability model based on competing failure analysis is proposed.A case study of the GaAs lasers is given to validate the effectiveness of the model and its solving method.The results indicate that if only the degradation failure is considered,the estimated result will be comparably optimistic.Meanwhile,the correlation between the degradation and traumatic failure has a great influence on the accuracy of reliability assessment.
基金supported by the National Natural Science Foundation of China[grant number 12001266]Natural Science Foundation of Jiangsu Province of China[grant number BK20180813]supported by the National Natural Science Foundation of China[grant number 12271168,12531013]and the 111 Project of China[grant number B14019].
文摘Remaining Useful Life(RUL)is one of the most important indicators to detect a component failure.RUL can be predicted by historical data by adopting a model-based method.The stochastic process models have become the most popular way to model degradation data for high-quality products,such as the Wiener process,gamma process and inverse Gaussian process.However,this leads to poor reliability assessment if the model is misspecified.Application of the Tweedie exponential dispersion(TED)process,including the above-mentioned classical stochastic processes as special cases,transforms the model selection problem into a parameter estimation problem dexterously.In this paper,we propose a TED process with random drifts for degradation data and a TeD process with random drifts and covariates for accelerated degradation data.A hierarchical Bayesian method is adopted to estimate the parameters of the proposed models.We also derive the failure-time distribution and the remaining useful life distribution for the proposed models.The simulation study shows that the proposed model outperforms the wrongly specified models.Two illustrative examples demonstrate the performance of the proposed TED process with random drifts and the TED process with random drifts and covariates.
