Reliability Analysis of Repairable Systems Using Stochastic Point Processes

In order to analyze the failure data from repairable systems, the homogeneous Poisson process (HPP) is usually used. In general, HPP cannot be applied to analyze the entire life cycle of a complex, re-pairable system because the rate of occurrence of failures (ROCOF) of the system changes over time rather than remains stable. However, from a practical point of view, it is always preferred to apply the simplest method to address problems and to obtain useful practical results. Therefore, we attempted to use the HPP model to analyze the failure data from real repairable systems. A graphic method and the Laplace test were also used in the analysis. Results of numerical applications show that the HPP model may be a useful tool for the entire life cycle of repairable systems.

Arithmetic Reduction of Intensity or Age Models with Bathtub Failure Intensity

In this paper, the authors will study the estimation of maintenance efficiency in Arithmetic Reduction of Intensity （ARI） and Arithmetic Reduction of Age （ARA） models with a memory m. These models have been proposed by Doyen （2005）, the failure process is simply Non Homogeneous Poisson Process （NHPP）. Our models are defined by reformulation of ARI and ARA ones using bathtub failure intensity. This form is presented like a superposition of two NHPP and Homogeneous Poisson Process （HPP）. Moreover, the particularity of this model allows taking account of system state improvement in time course. The maintenance effect is characterized by the change induced on the failure intensity before and after failure during degradation period. To simplify study, the asymptotic properties of failure process are derived. Then, the asymptotic normality of several maintenance efficiency estimators can be proved in the case where the failure process without maintenance is known. Practically, the coverage rate of the asymptotic confidence intervals issued from those estimators is studied.

Capacity analysis for cognitive heterogeneous networks with ideal/non-ideal sensing

Due to irregular deployment of small base stations （SBSs）, the interference in cognitive heterogeneous networks （CHNs） becomes even more complex; in particular, the uncertainty of spectrum mobility aggravates the interference context. In this case, how to analyze system capacity to obtain a closed-form expression becomes a crucial problem. In this paper we employ stochastic methods to formulate the capacity of CHNs and achieve a closed-form expression. By using discrete-time Markov chains （DTMCs）, the spectrum mobility with respect to the arrival and departure of macro base station （MBS） users is modeled. Then an integral method is proposed to derive the interference based on stochastic geometry （SG）. Also, the effect of sensing accuracy on network capacity is discussed by concerning false-alarm and miss-detection events. Simulation results are illustrated to show that the proposed capacity analysis method for CHNs can approximate the conventional sum methods without rigorous requirement for channel station information （CSI）. Therefore, it turns out to be a feasible and efficient way to capture the network capacity in CHNs.