An effective maintenance policy optimization model can reduce maintenance cost and system operation risk. For mission-oriented systems, the degradation process changes dynamically and is monotonous and irreversible. M...An effective maintenance policy optimization model can reduce maintenance cost and system operation risk. For mission-oriented systems, the degradation process changes dynamically and is monotonous and irreversible. Meanwhile, the risk of early failure is high. Therefore, this paper proposes a dynamic condition-based maintenance(CBM) optimization model for mission-oriented system based on inverse Gaussian(IG) degradation process. Firstly, the IG process with random drift coefficient is used to describe the degradation process and the relevant probability distributions are obtained. Secondly, the dynamic preventive maintenance threshold(DPMT) function is used to control the early failure risk of the mission-oriented system, and the influence of imperfect preventive maintenance(PM)on the degradation amount and degradation rate is analysed comprehensively. Thirdly, according to the mission availability requirement, the probability formulas of different types of renewal policies are obtained, and the CBM optimization model is constructed. Finally, a numerical example is presented to verify the proposed model. The comparison with the fixed PM threshold model and the sensitivity analysis show the effectiveness and application value of the optimization model.展开更多
In crosswell seismic exploration,the imaging section produced by migration based on a wave equation has a serious arc phenomenon at its edge and a small effective range because of geometrical restrictions.Another imag...In crosswell seismic exploration,the imaging section produced by migration based on a wave equation has a serious arc phenomenon at its edge and a small effective range because of geometrical restrictions.Another imaging section produced by the VSP-CDP stack imaging method employed with ray-tracing theory is amplitude-preserved.However,imaging 3D complex lithological structures accurately with this method is difficult.Therefore,this study proposes inverse Gaussian beam stack imaging in the 3D crosswell seismic exploration of deviated wells on the basis of Gaussian beam ray-tracing theory.By employing Gaussian beam ray-tracing theory in 3D crosswell seismic exploration,we analyzed the energy relationship between seismic wave fields and their effective rays.In imaging,the single-channel seismic wave fi eld data in the common shot point gather are converted into multiple effective wave fields in the common reflection point gather by the inverse Gaussian beam.The process produces a large fold number of intensive reflection points.Selected from the horizontal and vertical directions of the 2D measuring line,the wave fi elds of the eff ective reflection points in the same stack bin are projected onto the 2D measuring line,chosen according to the distribution characteristics of the reflection points,and stacked into an imaging section.The method is applied to X oilfi eld to identify the internal structure of the off shore gas cloud area.The results provided positive support for the inverse Gaussian beam stack imaging of 3D complex lithological structures and proved that technology is a powerful imaging tool for 3D crosswell seismic data processing.展开更多
Modern highly reliable products may have two or more quality characteristics(QCs) because of their complex structures and abundant functions. Relations between the QCs should be considered when assessing the reliabili...Modern highly reliable products may have two or more quality characteristics(QCs) because of their complex structures and abundant functions. Relations between the QCs should be considered when assessing the reliability of these products. This paper conducts a Bayesian analysis for a bivariate constant-stress accelerated degradation model based on the inverse Gaussian(IG) process. We assume that the product considered has two QCs and each of the QCs is governed by an IG process. The relationship between the QCs is described by a Frank copula function. We also assume that the stress on the products affects not only the parameters of the IG processes, but also the parameter of the Frank copula function. The Bayesian MCMC method is developed to calculate the maximum likelihood estimators(MLE) of the model parameters. The reliability function and the mean-time-to-failure(MTTF) are estimated through the calculation of the posterior samples. Finally, a simulation example is presented to illustrate the proposed bivariate constant-stress accelerated degradation model.展开更多
High frequency financial data is characterized by non-normality: asymmetric, leptokurtic and fat-tailed behaviour. The normal distribution is therefore inadequate in capturing these characteristics. To this end, vario...High frequency financial data is characterized by non-normality: asymmetric, leptokurtic and fat-tailed behaviour. The normal distribution is therefore inadequate in capturing these characteristics. To this end, various flexible distributions have been proposed. It is well known that mixture distributions produce flexible models with good statistical and probabilistic properties. In this work, a finite mixture of two special cases of Generalized Inverse Gaussian distribution has been constructed. Using this finite mixture as a mixing distribution to the Normal Variance Mean Mixture we get a Normal Weighted Inverse Gaussian (NWIG) distribution. The second objective, therefore, is to construct and obtain properties of the NWIG distribution. The maximum likelihood parameter estimates of the proposed model are estimated via EM algorithm and three data sets are used for application. The result shows that the proposed model is flexible and fits the data well.展开更多
This paper describes an inverse Gaussian process-based model to characterize the growth of metal-loss corrosion defects on energy pipelines.The model parameters are evaluated using the Bayesian methodology by combinin...This paper describes an inverse Gaussian process-based model to characterize the growth of metal-loss corrosion defects on energy pipelines.The model parameters are evaluated using the Bayesian methodology by combining the inspection data obtained from multiple inspections with the prior distributions.The Markov Chain Monte Carlo(MCMC)simulation techniques are employed to numerically evaluate the posterior marginal distribution of each individual parameter.The measurement errors associated with the ILI tools are considered in the Bayesian inference.The application of the growth model is illustrated using an example involving real inspection data collected from an in-service pipeline in Alberta,Canada.The results indicate that the model in general can predict the growth of corrosion defects reasonably well.Parametric analyses associated with the growth model as well as reliability assessment of the pipeline based on the growth model are also included in the example.The proposed model can be used to facilitate the development and application of reliability-based pipeline corrosion management.展开更多
Accurate modeling and parameter estimation of sea clutter are fundamental for effective sea surface target detection.With the improvement of radar resolution,sea clutter exhibits a pronounced heavy-tailed characterist...Accurate modeling and parameter estimation of sea clutter are fundamental for effective sea surface target detection.With the improvement of radar resolution,sea clutter exhibits a pronounced heavy-tailed characteristic,rendering traditional distribution models and parameter estimation methods less effective.To address this,this paper proposes a dual compound-Gaussian model with inverse Gaussian texture(CG-IG)distribution model and combines it with an improved Adam algorithm to introduce a method for parameter correction.This method effectively fits sea clutter with heavy-tailed characteristics.Experiments with real measured sea clutter data show that the dual CGIG distribution model,after parameter correction,accurately describes the heavy-tailed phenomenon in sea clutter amplitude distribution,and the overall mean square error of the distribution is reduced.展开更多
Fiber optical gyroscope(FOG)is a highly reliable navigation element,and the degradation trajectories of its two accuracy indexes are monotonic and non-monotonic respectively.In this paper,a flexible accelerated degrad...Fiber optical gyroscope(FOG)is a highly reliable navigation element,and the degradation trajectories of its two accuracy indexes are monotonic and non-monotonic respectively.In this paper,a flexible accelerated degradation testing(ADT)model is used for analyzing the bivariate dependent degradation process of FOG.The time-varying copulas are employed to consider the dynamic dependency structure between two marginal degradation processes as the Wiener process and the inverse Gaussian process.The statistical inference is implemented by utilizing an inference function for the margins(IFM)approach.It is demonstrated that the proposed method is powerful in modeling the joint distribution with various margins.展开更多
基金supported by the National Natural Science Foundation of China (71901216)。
文摘An effective maintenance policy optimization model can reduce maintenance cost and system operation risk. For mission-oriented systems, the degradation process changes dynamically and is monotonous and irreversible. Meanwhile, the risk of early failure is high. Therefore, this paper proposes a dynamic condition-based maintenance(CBM) optimization model for mission-oriented system based on inverse Gaussian(IG) degradation process. Firstly, the IG process with random drift coefficient is used to describe the degradation process and the relevant probability distributions are obtained. Secondly, the dynamic preventive maintenance threshold(DPMT) function is used to control the early failure risk of the mission-oriented system, and the influence of imperfect preventive maintenance(PM)on the degradation amount and degradation rate is analysed comprehensively. Thirdly, according to the mission availability requirement, the probability formulas of different types of renewal policies are obtained, and the CBM optimization model is constructed. Finally, a numerical example is presented to verify the proposed model. The comparison with the fixed PM threshold model and the sensitivity analysis show the effectiveness and application value of the optimization model.
基金This research work is funded by the Scientific Research Program of Shaanxi Provincial Education Department(No.19JK0668)the Natural Science Basic Research Plan in Shaanxi Province of China(No.2021JQ-588).
文摘In crosswell seismic exploration,the imaging section produced by migration based on a wave equation has a serious arc phenomenon at its edge and a small effective range because of geometrical restrictions.Another imaging section produced by the VSP-CDP stack imaging method employed with ray-tracing theory is amplitude-preserved.However,imaging 3D complex lithological structures accurately with this method is difficult.Therefore,this study proposes inverse Gaussian beam stack imaging in the 3D crosswell seismic exploration of deviated wells on the basis of Gaussian beam ray-tracing theory.By employing Gaussian beam ray-tracing theory in 3D crosswell seismic exploration,we analyzed the energy relationship between seismic wave fields and their effective rays.In imaging,the single-channel seismic wave fi eld data in the common shot point gather are converted into multiple effective wave fields in the common reflection point gather by the inverse Gaussian beam.The process produces a large fold number of intensive reflection points.Selected from the horizontal and vertical directions of the 2D measuring line,the wave fi elds of the eff ective reflection points in the same stack bin are projected onto the 2D measuring line,chosen according to the distribution characteristics of the reflection points,and stacked into an imaging section.The method is applied to X oilfi eld to identify the internal structure of the off shore gas cloud area.The results provided positive support for the inverse Gaussian beam stack imaging of 3D complex lithological structures and proved that technology is a powerful imaging tool for 3D crosswell seismic data processing.
基金the National Natural Science Foundation of China(No.11671080)the Jiangsu Provincial Key Laboratory of Networked Collective Intelligence(No.BM2017002)
文摘Modern highly reliable products may have two or more quality characteristics(QCs) because of their complex structures and abundant functions. Relations between the QCs should be considered when assessing the reliability of these products. This paper conducts a Bayesian analysis for a bivariate constant-stress accelerated degradation model based on the inverse Gaussian(IG) process. We assume that the product considered has two QCs and each of the QCs is governed by an IG process. The relationship between the QCs is described by a Frank copula function. We also assume that the stress on the products affects not only the parameters of the IG processes, but also the parameter of the Frank copula function. The Bayesian MCMC method is developed to calculate the maximum likelihood estimators(MLE) of the model parameters. The reliability function and the mean-time-to-failure(MTTF) are estimated through the calculation of the posterior samples. Finally, a simulation example is presented to illustrate the proposed bivariate constant-stress accelerated degradation model.
文摘High frequency financial data is characterized by non-normality: asymmetric, leptokurtic and fat-tailed behaviour. The normal distribution is therefore inadequate in capturing these characteristics. To this end, various flexible distributions have been proposed. It is well known that mixture distributions produce flexible models with good statistical and probabilistic properties. In this work, a finite mixture of two special cases of Generalized Inverse Gaussian distribution has been constructed. Using this finite mixture as a mixing distribution to the Normal Variance Mean Mixture we get a Normal Weighted Inverse Gaussian (NWIG) distribution. The second objective, therefore, is to construct and obtain properties of the NWIG distribution. The maximum likelihood parameter estimates of the proposed model are estimated via EM algorithm and three data sets are used for application. The result shows that the proposed model is flexible and fits the data well.
基金financial support provided by the Natural Sciences and Engineering Research Council(NSERC)of Canada and TransCanada Corporation through the Collaborative Research and Development(CRD)program.
文摘This paper describes an inverse Gaussian process-based model to characterize the growth of metal-loss corrosion defects on energy pipelines.The model parameters are evaluated using the Bayesian methodology by combining the inspection data obtained from multiple inspections with the prior distributions.The Markov Chain Monte Carlo(MCMC)simulation techniques are employed to numerically evaluate the posterior marginal distribution of each individual parameter.The measurement errors associated with the ILI tools are considered in the Bayesian inference.The application of the growth model is illustrated using an example involving real inspection data collected from an in-service pipeline in Alberta,Canada.The results indicate that the model in general can predict the growth of corrosion defects reasonably well.Parametric analyses associated with the growth model as well as reliability assessment of the pipeline based on the growth model are also included in the example.The proposed model can be used to facilitate the development and application of reliability-based pipeline corrosion management.
文摘Accurate modeling and parameter estimation of sea clutter are fundamental for effective sea surface target detection.With the improvement of radar resolution,sea clutter exhibits a pronounced heavy-tailed characteristic,rendering traditional distribution models and parameter estimation methods less effective.To address this,this paper proposes a dual compound-Gaussian model with inverse Gaussian texture(CG-IG)distribution model and combines it with an improved Adam algorithm to introduce a method for parameter correction.This method effectively fits sea clutter with heavy-tailed characteristics.Experiments with real measured sea clutter data show that the dual CGIG distribution model,after parameter correction,accurately describes the heavy-tailed phenomenon in sea clutter amplitude distribution,and the overall mean square error of the distribution is reduced.
基金supported by the National Key R&D Program of China(2018YFB0104504).
文摘Fiber optical gyroscope(FOG)is a highly reliable navigation element,and the degradation trajectories of its two accuracy indexes are monotonic and non-monotonic respectively.In this paper,a flexible accelerated degradation testing(ADT)model is used for analyzing the bivariate dependent degradation process of FOG.The time-varying copulas are employed to consider the dynamic dependency structure between two marginal degradation processes as the Wiener process and the inverse Gaussian process.The statistical inference is implemented by utilizing an inference function for the margins(IFM)approach.It is demonstrated that the proposed method is powerful in modeling the joint distribution with various margins.
