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Wireless SAW sensor temperature measurement relies heavily on the quality of the transmitted signal. The standard approach of averaging consecutive signals to improve the quality is suboptimal in a rotational setting, where the In this paper we introduce a novel approach to pre-filtering the data by averaging a selection of useful signals. Three selection strategies are presented. By means of experiments we show that the method is superior to standard averaging. The algorithm particularly stands out when applied in an industrial application of rotational mold casting, where only ca. 10% of the data are useful. While the sensor is invisible to standard averaging, our algorithm makes process monitoring of up to around 300 °C possible.

C1.4 - A Selective Algorithm for Wireless Temperature Monitoring of a Rotational Mold Casting Process with Surface Acoustic Wave (SAW) Sensors