Abstract
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 treat interferences involved in both interrogator to interrogator and sensor to interrogator communications.
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This work is supported by the Franco-Swiss INTERREG IV program, in the context of the MainPreSI project.
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Gombé, B.O., Mérou, G.G., Breschi, K. et al. A SAW wireless sensor network platform for industrial predictive maintenance. J Intell Manuf 30, 1617–1628 (2019). https://doi.org/10.1007/s10845-017-1344-0
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DOI: https://doi.org/10.1007/s10845-017-1344-0