Uncertainty analysis of the temperature-resistance relationship of temperature sensing fabric

This paper reports the uncertainty analysis of the temperature-resistance (TR) data of the newly developed temperature sensing fabric (TSF), which is a double-layer knitted structure fabricated on an electronic flat-bed knitting machine, made of polyester as a basal yarn, and embedded with fine metallic wire as sensing element. The measurement principle of the TSF is identical to temperature resistance detector (RTD); that is, change in resistance due to change in temperature. The regression uncertainty (uncertainty within repeats) and repeatability uncertainty (uncertainty among repeats) were estimated by analysing more than 300 TR experimental repeats of 50 TSF samples. The experiments were performed under dynamic heating and cooling environments on a purpose-built test rig within the temperature range of 20-50 °C. The continuous experimental data was recorded through LabVIEW-based graphical user interface. The result showed that temperature and resistance values were not only repeatable but reproducible, with only minor variations. The regression uncertainty was found to be less than ±0.3 °C; the TSF sample made of Ni andWwires showed regression uncertainty of < ±0.13 °C in comparison to Cu-based TSF samples ( > ±0.18 °C). The cooling TR data showed considerably reduced values (±0.07 °C) of uncertainty in comparison with the heating TR data (±0.24 °C). The repeatability uncertainty was found to be less than ±0.5 °C. By increasing the number of samples and repeats, the uncertainties may be reduced further. The TSF could be used for continuous measurement of the temperature profile on the surface of the human body.