This article describes the development of an unmanned monitoring system for the detection of hot spots inside a power substation. It uses an infrared camera connected to a robot which moves along the substation via a steel cable inside the substation. The robot is remotely controlled by a wireless system, operating at a frequency of 2.45 GHz (WiFi). The camera is dynamically directed to different substation components through a pan-tilt system. The whole structure is controlled by a microcomputer software located in a control room, around 200 m away from the robot. Aside of controlling the robot pan-tilt movements, the software also performs the data acquisition and analysis of thermographic images, thus detecting the referred hot spots. IndexTerms--Thermography, robotic, wireless communication. J. A. Jardini is with the

We propose an extension of the generalized bivariate Marshall-Olkin model assuming dependence between the random variables involved. Probabilistic, aging properties, and survival copula representation of the extended model are obtained and illustrated by examples. Bayesian analysis is performed and possible applications are discussed. A dual version of extended Marshall-Olkin model is introduced and related stochastic order comparisons are presented. IntroductionA variety of bivariate (multivariate) extensions of the univariate exponential distribution have been considered in the literature. These include the distributions of [6,10,11,24], see a full review in [2].The vector (X 1 , X 2 ) meets the Marshall-Olkin model (MO hereafter) whenever it admits the stochastic representationwhere the random variables T i are independent and exponentially distributed with parameters λ i > 0, respectively, i = 1, 2, 3. The random variables T 1 and T 2 in (6.1) can be interpreted as the arrival time of individual shocks for two different components, while T 3 represents the time of arrival of a shock common to both components. Several applications of (6.1) can be mentioned. In reliability theory it can describe the lifetime of a system of two components operating in a random environment and subject to three independent

No abstract

Our main goal is to characterize in terms of copulas the linear Sibuya bivariate lack of memory property recently introduced in [12]. As a particular case, one can obtain nonaging copulas considered in the literature.