Buckling analysis of embedded graphene oxide powder-reinforced nanocomposite shells

In this study, the buckling analysis of a Graphene oxide powder reinforced (GOPR) nanocomposite shell is investigated. The effective material properties of the nanocomposite are estimated through Halpin-Tsai micromechanical scheme. Three distribution types of GOPs are considered, namely uniform, X and O. Also, a first-order shear deformation shell theory is incorporated with the principle of virtual work to derive the governing differential equations of the problem. The governing equations are solved via Galerkin's method, which is a powerful analytical method for static and dynamic problems. Comparison study is performed to verify the present formulation with those of previous data. New results for the buckling load of GOPR nanocomposite shells are presented regarding for different values of circumfer-ential wave number. Besides, the influences of weight fraction of nanofillers, length and radius to thickness ratios and elastic foundation on the critical buckling loads of GOP-reinforced nanocomposite shells are explored.     

Multistate reliability models

Most of the reliability literature deals with binary systems of binary components in which the only two states are functioning or failed. In this article two types of multistate coherent system (MCS) have been introduced. Various properties of our definitions are studied. The relationship between our definitions of MCS and other definitions of MCS in the literature are discussed.     

When are observed failures more informative than observed survivals?

A framework involving independent competing risks permits observing failures due to a specific cause and failures due to a competing cause, which constitute survival times from the cause of primary interest. Is observing more failures more informative than observing survivals? Intuitively, due to the definitiveness of failures, the answer seems to be the former. However, it has been shown before that this intuition holds when estimating the mean but not the failure rate of the exponential model with a gamma prior distribution for the failure rate. In this article, we address this question at a more general level. We show that for a certain class of distributions failures can be more informative than survivals for prediction of life length and vice versa for some others. We also show that for a large class of lifetime models, failure is less informative than survival for estimating the proportional hazards parameter with gamma, Jeffreys, and uniform priors. We further show that, for this class of lifetime models, on average, failure is more informative than survival for parameter estimation and for prediction. These results imply that the inferential purpose and properties of the lifetime distribution are germane for conducting life tests. © 2013 Wiley Periodicals, Inc. Naval Research Logistics, 2013     

Predictability of operational processes over finite horizon

Operational processes are usually studied in terms of stochastic processes. The main information measure used for predictability of stochastic processes is the entropy rate, which is asymptotic conditional entropy, thus not suitable for application over a finite horizon. We use the conditional entropy to study the predictability of stochastic processes over the finite horizon. It is well‐known that the conditional entropies of stationary processes decrease as the processes evolve, implying that, on average, their pasts become more informative about prediction of their future outcomes. Some important operational processes such as martingale, models for maintenance policies, nonhomogeneous Poisson, and mixed Poisson processes are nonstationary. We show that as a nonstationary process evolves, it may provide more information or less information about the future state of the system. We develop results for comparing the predictability of stochastic processes. © 2011 Wiley Periodicals, Inc. Naval Research Logistics, 2011     

New maximum entropy methods for modeling lifetime distributions

This article introduces two new maximum entropy (ME) methods for modeling the distribution of time to an event. One method is within the classical ME framework and provides characterizations of change point models such as the piecewise exponential distribution. The second method uses the entropy of the equilibrium distribution (ED) for the objective function and provides new characterizations of the exponential, Weibull, Pareto, and uniform distributions. With the same moment constraints, the classical ME and the maximum ED entropy algorithms generate different models for the interarrival time. © 2014 Wiley Periodicals, Inc. Naval Research Logistics 61: 427–434, 2014     

System reliability based on diffusion models for fatigue crack growth

Diffusion processes are commonly used to describe the dynamics of complex systems arising in a wide range of application fields. In this paper we propose, on the basis of diffusion processes, two models concerned with the stochastic behavior of fatigue cracks in a system. They are then used to get the distribution of the failure time, the first time the crack size of at least one of the cracks exceeds a given value. Several properties of our proposed models are presented, and the unknown parameters are estimated by the method of maximum likelihood. From these an estimate of failure time distribution is obtained. In this part, contrary to common practice, we do not assume availability of failure data. © 2004 Wiley Periodicals, Inc. Naval Research Logistics, 2005     

A nonparametric approach to accelerated life testing under multiple stresses

Accelerated life testing (ALT) is concerned with subjecting items to a series of stresses at several levels higher than those experienced under normal conditions so as to obtain the lifetime distribution of items under normal levels. A parametric approach to this problem requires two assumptions. First, the lifetime of an item is assumed to have the same distribution under all stress levels, that is, a change of stress level does not change the shape of the life distribution but changes only its scale. Second, a functional relationship is assumed between the parameters of the life distribution and the accelerating stresses. A nonparametric approach, on the other hand, assumes a functional relationship between the life distribution functions at the accelerated and nonaccelerated stress levels without making any assumptions on the forms of the distribution functions. In this paper, we treat the problem nonparametrically. In particular, we extend the methods of Shaked, Zimmer, and Ball [7] and Strelec and Viertl [8] and develop a nonparametric estimation procedure for a version of the generalized Arrhenius model with two stress variables assuming a linear acceleration function. We obtain consistent estimates as well as confidence intervals of the parameters of the life distribution under normal stress level and compare our nonparametric method with parametric methods assuming exponential, Weibull and lognormal life distributions using both real life and simulated data. © 1998 John Wiley & Sons, Inc. Naval Research Logistics 45: 629–644, 1998