A drought is when reduced rainfall leads to a water crisis,impacting daily life.Over recent decades,droughts have affected various regions,including South Sulawesi,Indonesia.This study aims to map the probability of m...A drought is when reduced rainfall leads to a water crisis,impacting daily life.Over recent decades,droughts have affected various regions,including South Sulawesi,Indonesia.This study aims to map the probability of meteo-rological drought months using the 1-month Standardized Precipitation Index(SPI)in South Sulawesi.Based on SPI,meteorological drought characteristics are inversely proportional to drought event intensity,which can be modeled using a Non-Homogeneous Poisson Process,specifically the Power Law Process.The estimation method employs Maximum Likelihood Estimation(MLE),where drought event intensities are treated as random variables over a set time interval.Future drought months are estimated using the cumulative Power Law Process function,with theβandγparameters more significant than 0.The probability of drought months is determined using the Non-Homogeneous Poisson Process,which models event occurrence over time,considering varying intensities.The results indicate that,of the 24 districts/cities in South Sulawesi,14 experienced meteorological drought based on the SPI and Power Law Process model.The estimated number of months of drought occurrence in the next 12 months is one month of drought with an occurrence probability value of 0.37 occurring in November in the Selayar,Bulukumba,Bantaeng,Jeneponto,Takalar and Gowa areas,in October in the Sinjai,Barru,Bone,Soppeng,Pinrang and Pare-pare areas,as well as in December in the Maros and Makassar areas.展开更多
Due to the simplicity and flexibility of the power law process,it is widely used to model the failures of repairable systems.Although statistical inference on the parameters of the power law process has been well deve...Due to the simplicity and flexibility of the power law process,it is widely used to model the failures of repairable systems.Although statistical inference on the parameters of the power law process has been well developed,numerous studies largely depend on complete failure data.A few methods on incomplete data are reported to process such data,but they are limited to their specific cases,especially to that where missing data occur at the early stage of the failures.No framework to handle generic scenarios is available.To overcome this problem,from the point of view of order statistics,the statistical inference of the power law process with incomplete data is established in this paper.The theoretical derivation is carried out and the case studies demonstrate and verify the proposed method.Order statistics offer an alternative to the statistical inference of the power law process with incomplete data as they can reformulate current studies on the left censored failure data and interval censored data in a unified framework.The results show that the proposed method has more flexibility and more applicability.展开更多
Reliability analysis is the key to evaluate software’s quality. Since the early 1970s, the Power Law Process, among others, has been used to assess the rate of change of software reliability as time-varying function ...Reliability analysis is the key to evaluate software’s quality. Since the early 1970s, the Power Law Process, among others, has been used to assess the rate of change of software reliability as time-varying function by using its intensity function. The Bayesian analysis applicability to the Power Law Process is justified using real software failure times. The choice of a loss function is an important entity of the Bayesian settings. The analytical estimate of likelihood-based Bayesian reliability estimates of the Power Law Process under the squared error and Higgins-Tsokos loss functions were obtained for different prior knowledge of its key parameter. As a result of a simulation analysis and using real data, the Bayesian reliability estimate under the Higgins-Tsokos loss function not only is robust as the Bayesian reliability estimate under the squared error loss function but also performed better, where both are superior to the maximum likelihood reliability estimate. A sensitivity analysis resulted in the Bayesian estimate of the reliability function being sensitive to the prior, whether parametric or non-parametric, and to the loss function. An interactive user interface application was additionally developed using Wolfram language to compute and visualize the Bayesian and maximum likelihood estimates of the intensity and reliability functions of the Power Law Process for a given data.展开更多
Studying the propagation of cascading failures through the transmission network is key to asses and mitigate the risk faced the energy system. As complex systems the power grid failure is often studied using some prob...Studying the propagation of cascading failures through the transmission network is key to asses and mitigate the risk faced the energy system. As complex systems the power grid failure is often studied using some probability distributions. We apply 4 well-known probabilistic models, Poisson model, Power Law model, Generalized Poisson Branching process model and Borel-Tanner Branching process model, to a 14-year utility historical outage data from a regional power grid in China, computing probabilities of cascading line outages. For this data, the empirical distribution of the total number of line outages is well approximated by the initial line outages propagating according to a Borel-Tanner branching process. Also for this data, Power law model overestimates, while Generalized Possion branching process and Possion model underestimate, the probability of larger outages. Especially, the probability distribution generated by the Poisson model deviates heavily from the observed data, underestimating the probability of large events (total no. of outages over 5) by roughly a factor of 10-2 to 10-5. The observation is confirmed by a statistical test of model fitness. The results of this work indicate that further testing of Borel-Tanner branching process models of cascading failure is appropriate, and should be further discussed as it outperforms other more traditional models.展开更多
This work applies non-stationary random processes to resilience of power distribution under severe weather. Power distribution, the edge of the energy infrastructure, is susceptible to external hazards from severe wea...This work applies non-stationary random processes to resilience of power distribution under severe weather. Power distribution, the edge of the energy infrastructure, is susceptible to external hazards from severe weather. Large-scale power failures often occur, resulting in millions of people without electricity for days. However, the problem of large-scale power failure, recovery and resilience has not been formulated rigorously nor studied systematically. This work studies the resilience of power distribution from three aspects. First, we derive non-stationary random processes to model large-scale failures and recoveries. Transient Little’s Law then provides a simple approximation of the entire life cycle of failure and recovery through a queue at the network-level. Second, we define time-varying resilience based on the non-stationary model. The resilience metric characterizes the ability of power distribution to remain operational and recover rapidly upon failures. Third, we apply the non-stationary model and the resilience metric to large-scale power failures caused by Hurricane Ike. We use the real data from the electric grid to learn time-varying model parameters and the resilience metric. Our results show non-stationary evolution of failure rates and recovery times, and how the network resilience deviates from that of normal operation during the hurricane.展开更多
In this paper,the acceleratingly growing network model with intermittent processes is proposed.In thegrowing network,there exist both accelerating and intermittent processes.The network is grown from the number ofnode...In this paper,the acceleratingly growing network model with intermittent processes is proposed.In thegrowing network,there exist both accelerating and intermittent processes.The network is grown from the number ofnodes m<sub>o</sub> and the number of links added with each new node is a nonlinearly increasing function m+aN<sup>β</sup>（t）f（t）,whereN（t） is the number of nodes present at time t.f（t） is the periodic and bistable function with period T,whose values are1 and 0 indicating accelerating and intermittent processes,respectively.Here we denote the ratio r of acceleration timeto whole one.We study the degree distribution p（k） of the model,focusing on the dependence of p（k） on the networkparameters τ,T,m,α,N,and β.It is found that there exists a phase transition point,k<sub>c</sub> such that if k【k<sub>c</sub>,then p（k）obeys a power-law distribution with exponent -γ<sub>1</sub>,while if k】k<sub>c</sub>,then p（k） exhibits a power-law distribution withexponent-γ<sub>2</sub>.Moreover,the exponents γ<sub>1</sub> and γ<sub>2</sub> are independent of τ,T,m,a,and N,while they depend only onthe parameter β.More interesting,the phase transition point is described by k<sub>c</sub>=aN<sup>β</sup>,which is equal to the value atwhich p（k） is maximum in GM model.展开更多
Strength degradation is a stochastic and irreversible process.Gamma process is an independent nonnegative increment process which can be used to describe the characteristics of strength degradation.It is unreasonable ...Strength degradation is a stochastic and irreversible process.Gamma process is an independent nonnegative increment process which can be used to describe the characteristics of strength degradation.It is unreasonable to choose a linear function to describe the strength degradation trend considering that the degradation rate may change over time.So,a non-linear power law is proposed to describe the strength degradation trend based on preliminary work.It is more general compared with the linear one and the inadequacy of strict linear assumption is overcome.Then,the model parameters of non-stationary Gamma process are estimated based on the maximum likelihood method.Finally,it is proved that the non-stationary Gamma process can accurately reflect the strength degradation law through comparative analysis of a real example.展开更多
起动控制规律对于辅助动力装置(Auxiliary Power Unit,APU)至关重要。针对APU起动控制规律设计中的控制时序、供油规律、起动过程中的限制及保护等关键技术点进行深入研究,提出一种适应性较好的起动控制规律。经多型APU整机试验验证,提...起动控制规律对于辅助动力装置(Auxiliary Power Unit,APU)至关重要。针对APU起动控制规律设计中的控制时序、供油规律、起动过程中的限制及保护等关键技术点进行深入研究,提出一种适应性较好的起动控制规律。经多型APU整机试验验证,提出的起动控制规律可缩短整机调试时间,提高研制效率。展开更多
基于DIC(Deviance Information Criterion)信息准则、BGR(Brooks-Gelman-Rubin)诊断原理、蒙特卡洛仿真误差及模型参数和可靠性指标后验估计的区间长度,提出了数控机床贝叶斯可靠性模型的综合评价方法.给出了不同先验下用于Gibbs抽样的...基于DIC(Deviance Information Criterion)信息准则、BGR(Brooks-Gelman-Rubin)诊断原理、蒙特卡洛仿真误差及模型参数和可靠性指标后验估计的区间长度,提出了数控机床贝叶斯可靠性模型的综合评价方法.给出了不同先验下用于Gibbs抽样的幂律过程模型参数的后验分布,并利用马尔科夫链蒙特卡洛法获得了模型参数和可靠性指标的贝叶斯点估计和区间估计.通过2个工程实例进行验证,结果表明,幂律过程模型各项评价指标均优于Weibull分布模型,适用于小样本故障数据数控机床的可靠性评估.展开更多
在多种合理的无信息先验分布下,基于Markov Chain Monte Carlo方法,提出了一种简单且易于抽样的幂律过程的Bayesian分析方法.所提方法将失效、时间截尾数据统一分析,能快捷地获取幂律过程模型参数的Markov Chain Monte Carlo样本,利用...在多种合理的无信息先验分布下,基于Markov Chain Monte Carlo方法,提出了一种简单且易于抽样的幂律过程的Bayesian分析方法.所提方法将失效、时间截尾数据统一分析,能快捷地获取幂律过程模型参数的Markov Chain Monte Carlo样本,利用该样本不但能直接给出模型参数函数的后验分布,还能给出单样预测和双样预测的分析.一个经典工程数值算例说明了所提方法的可行性、合理性与有效性.该方法具有一定的优越性,可为小子样可靠性增长分析提供一种值得参考的方法.展开更多
基金funded by Hasanuddin University,grant number 00309/UN4.22/PT.01.03/2024.
文摘A drought is when reduced rainfall leads to a water crisis,impacting daily life.Over recent decades,droughts have affected various regions,including South Sulawesi,Indonesia.This study aims to map the probability of meteo-rological drought months using the 1-month Standardized Precipitation Index(SPI)in South Sulawesi.Based on SPI,meteorological drought characteristics are inversely proportional to drought event intensity,which can be modeled using a Non-Homogeneous Poisson Process,specifically the Power Law Process.The estimation method employs Maximum Likelihood Estimation(MLE),where drought event intensities are treated as random variables over a set time interval.Future drought months are estimated using the cumulative Power Law Process function,with theβandγparameters more significant than 0.The probability of drought months is determined using the Non-Homogeneous Poisson Process,which models event occurrence over time,considering varying intensities.The results indicate that,of the 24 districts/cities in South Sulawesi,14 experienced meteorological drought based on the SPI and Power Law Process model.The estimated number of months of drought occurrence in the next 12 months is one month of drought with an occurrence probability value of 0.37 occurring in November in the Selayar,Bulukumba,Bantaeng,Jeneponto,Takalar and Gowa areas,in October in the Sinjai,Barru,Bone,Soppeng,Pinrang and Pare-pare areas,as well as in December in the Maros and Makassar areas.
基金supported by the National Natural Science Foundation of China(51775090)。
文摘Due to the simplicity and flexibility of the power law process,it is widely used to model the failures of repairable systems.Although statistical inference on the parameters of the power law process has been well developed,numerous studies largely depend on complete failure data.A few methods on incomplete data are reported to process such data,but they are limited to their specific cases,especially to that where missing data occur at the early stage of the failures.No framework to handle generic scenarios is available.To overcome this problem,from the point of view of order statistics,the statistical inference of the power law process with incomplete data is established in this paper.The theoretical derivation is carried out and the case studies demonstrate and verify the proposed method.Order statistics offer an alternative to the statistical inference of the power law process with incomplete data as they can reformulate current studies on the left censored failure data and interval censored data in a unified framework.The results show that the proposed method has more flexibility and more applicability.
文摘Reliability analysis is the key to evaluate software’s quality. Since the early 1970s, the Power Law Process, among others, has been used to assess the rate of change of software reliability as time-varying function by using its intensity function. The Bayesian analysis applicability to the Power Law Process is justified using real software failure times. The choice of a loss function is an important entity of the Bayesian settings. The analytical estimate of likelihood-based Bayesian reliability estimates of the Power Law Process under the squared error and Higgins-Tsokos loss functions were obtained for different prior knowledge of its key parameter. As a result of a simulation analysis and using real data, the Bayesian reliability estimate under the Higgins-Tsokos loss function not only is robust as the Bayesian reliability estimate under the squared error loss function but also performed better, where both are superior to the maximum likelihood reliability estimate. A sensitivity analysis resulted in the Bayesian estimate of the reliability function being sensitive to the prior, whether parametric or non-parametric, and to the loss function. An interactive user interface application was additionally developed using Wolfram language to compute and visualize the Bayesian and maximum likelihood estimates of the intensity and reliability functions of the Power Law Process for a given data.
文摘Studying the propagation of cascading failures through the transmission network is key to asses and mitigate the risk faced the energy system. As complex systems the power grid failure is often studied using some probability distributions. We apply 4 well-known probabilistic models, Poisson model, Power Law model, Generalized Poisson Branching process model and Borel-Tanner Branching process model, to a 14-year utility historical outage data from a regional power grid in China, computing probabilities of cascading line outages. For this data, the empirical distribution of the total number of line outages is well approximated by the initial line outages propagating according to a Borel-Tanner branching process. Also for this data, Power law model overestimates, while Generalized Possion branching process and Possion model underestimate, the probability of larger outages. Especially, the probability distribution generated by the Poisson model deviates heavily from the observed data, underestimating the probability of large events (total no. of outages over 5) by roughly a factor of 10-2 to 10-5. The observation is confirmed by a statistical test of model fitness. The results of this work indicate that further testing of Borel-Tanner branching process models of cascading failure is appropriate, and should be further discussed as it outperforms other more traditional models.
文摘This work applies non-stationary random processes to resilience of power distribution under severe weather. Power distribution, the edge of the energy infrastructure, is susceptible to external hazards from severe weather. Large-scale power failures often occur, resulting in millions of people without electricity for days. However, the problem of large-scale power failure, recovery and resilience has not been formulated rigorously nor studied systematically. This work studies the resilience of power distribution from three aspects. First, we derive non-stationary random processes to model large-scale failures and recoveries. Transient Little’s Law then provides a simple approximation of the entire life cycle of failure and recovery through a queue at the network-level. Second, we define time-varying resilience based on the non-stationary model. The resilience metric characterizes the ability of power distribution to remain operational and recover rapidly upon failures. Third, we apply the non-stationary model and the resilience metric to large-scale power failures caused by Hurricane Ike. We use the real data from the electric grid to learn time-varying model parameters and the resilience metric. Our results show non-stationary evolution of failure rates and recovery times, and how the network resilience deviates from that of normal operation during the hurricane.
基金The project supported by National Natural Science Foundation of China under Grant Nos.70571017 and 10247005the Innovation Project of Guangxi Graduate Education under Grant No.2006106020809M36
文摘In this paper,the acceleratingly growing network model with intermittent processes is proposed.In thegrowing network,there exist both accelerating and intermittent processes.The network is grown from the number ofnodes m<sub>o</sub> and the number of links added with each new node is a nonlinearly increasing function m+aN<sup>β</sup>（t）f（t）,whereN（t） is the number of nodes present at time t.f（t） is the periodic and bistable function with period T,whose values are1 and 0 indicating accelerating and intermittent processes,respectively.Here we denote the ratio r of acceleration timeto whole one.We study the degree distribution p（k） of the model,focusing on the dependence of p（k） on the networkparameters τ,T,m,α,N,and β.It is found that there exists a phase transition point,k<sub>c</sub> such that if k【k<sub>c</sub>,then p（k）obeys a power-law distribution with exponent -γ<sub>1</sub>,while if k】k<sub>c</sub>,then p（k） exhibits a power-law distribution withexponent-γ<sub>2</sub>.Moreover,the exponents γ<sub>1</sub> and γ<sub>2</sub> are independent of τ,T,m,a,and N,while they depend only onthe parameter β.More interesting,the phase transition point is described by k<sub>c</sub>=aN<sup>β</sup>,which is equal to the value atwhich p（k） is maximum in GM model.
基金National Natural Science Foundation of China(No.51265025)
文摘Strength degradation is a stochastic and irreversible process.Gamma process is an independent nonnegative increment process which can be used to describe the characteristics of strength degradation.It is unreasonable to choose a linear function to describe the strength degradation trend considering that the degradation rate may change over time.So,a non-linear power law is proposed to describe the strength degradation trend based on preliminary work.It is more general compared with the linear one and the inadequacy of strict linear assumption is overcome.Then,the model parameters of non-stationary Gamma process are estimated based on the maximum likelihood method.Finally,it is proved that the non-stationary Gamma process can accurately reflect the strength degradation law through comparative analysis of a real example.
文摘起动控制规律对于辅助动力装置(Auxiliary Power Unit,APU)至关重要。针对APU起动控制规律设计中的控制时序、供油规律、起动过程中的限制及保护等关键技术点进行深入研究,提出一种适应性较好的起动控制规律。经多型APU整机试验验证,提出的起动控制规律可缩短整机调试时间,提高研制效率。
文摘基于DIC(Deviance Information Criterion)信息准则、BGR(Brooks-Gelman-Rubin)诊断原理、蒙特卡洛仿真误差及模型参数和可靠性指标后验估计的区间长度,提出了数控机床贝叶斯可靠性模型的综合评价方法.给出了不同先验下用于Gibbs抽样的幂律过程模型参数的后验分布,并利用马尔科夫链蒙特卡洛法获得了模型参数和可靠性指标的贝叶斯点估计和区间估计.通过2个工程实例进行验证,结果表明,幂律过程模型各项评价指标均优于Weibull分布模型,适用于小样本故障数据数控机床的可靠性评估.
文摘在多种合理的无信息先验分布下,基于Markov Chain Monte Carlo方法,提出了一种简单且易于抽样的幂律过程的Bayesian分析方法.所提方法将失效、时间截尾数据统一分析,能快捷地获取幂律过程模型参数的Markov Chain Monte Carlo样本,利用该样本不但能直接给出模型参数函数的后验分布,还能给出单样预测和双样预测的分析.一个经典工程数值算例说明了所提方法的可行性、合理性与有效性.该方法具有一定的优越性,可为小子样可靠性增长分析提供一种值得参考的方法.
