Predictive maintenance predicts the system health, based on the current condition, and defines the needed maintenance activities accordingly. This way, the system is only taken out of service if direct evidence exists that deterioration has actually taken place. This increases maintenance efficiency and productivity on one hand, and decreases maintenance support costs and logistics footprints on the other. We propose a system based on wireless sensor network to monitor industrial systems in order to prevent faults and damages. The sensors use the surface acoustic wave technology with an architecture composed of an electronic interrogation device and a passive sensor (without energy at the transducer) which is powered by the radio frequency transmitted by the interrogation unit. The radio frequency link transfers energy to the sensor to perform its measurement and to transmit the result to the interrogation unit-or in a description closer to the implemented, characterize the cooperative target cross section characteristics to recover the physical quantity defining the transducer material properties. We use this sensing architecture to measure the temperature of industrial machine components and we evaluate the robustness of the method. This technology can be applied to other physical parameters to be monitored. Captured information is transmitted to the base station through multi-hop communications. We also Violeta Felea
Efficient broadcast in wireless sensor networks can exploit backbone structures, in relation to connected dominating sets in graphs. This paper proposes the construction of a backbone for broadcast in heterogeneous wireless sensor networks having minimum energy consumption and a delay bounded by a predefined constant hmax. For the purpose of the paper, we assume the sensors composing the network have an energy belonging to [Wmin; Wmax] such that Wmax = c * Wmin where c is a predefined constant. To address this problem we propose two different approaches. For small instances, we propose an integer linear program that computes an optimal solution for the problem. Since solving an integer linear program is NP-hard, obtaining solutions for large network instances may be impossible in a reasonable time, thus we propose an approximation algorithm that computes a solution for the problem in polynomial time and whose approximation ratio is c * hmax.
Three dimensional (3D) voxel phantoms are numerical representations of human bodies, used by physicians in very different contexts. In the controlled context of hospitals, where from 2 to 10 subjects may arrive per day, phantoms are used to verify computations before therapeutic exposure to radiation of cancerous tumors. In addition, 3D phantoms are used to diagnose the gravity of accidental exposure to radiation. In such cases, there may be from 10 to more than 1000 subjects to be diagnosed simultaneously. In all of these cases, computation accuracy depends on a single such representation. In this paper, we present EquiVox which is a tool composed of several distributed functions and enables to create, as quickly and as accurately as possible, 3D numerical phantoms that fit anyone, whatever the context. It is based on a multi-agent system. Agents are convenient for this kind of structure, they can interact together and they may have individual capacities. In EquiVox, the phantoms adaptation is a key phase based on artificial neural network (ANN) interpolations. Thus, ANNs must be trained regularly in order to take into account newly capitalized subjects and to increase interpolation accuracy. However, ANN training is a time-consuming process. Consequently, we have built Equivox to optimize this process. Thus, in this paper, we present our architecture, based on agents and ANN, and we put the stress on the adaptation module. We propose, next, some experimentations in order to show the efficiency of the EquiVox architecture.
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