Metrological Software Test for Simulating the Method of Determining the Thermocouple Error in Situ During Operation

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The simplified metrological software test (MST) for modeling the method of determining the thermocouple (TC) error in situ during operation is considered in the paper. The interaction between the proposed MST and a temperature measuring system is also reflected in order to study the error of determining the TC error in situ during operation. The modelling studies of the random error influence of the temperature measuring system, as well as interference magnitude (both the common and normal mode noises) on the error of determining the TC error in situ during operation using the proposed MST, have been carried out. The noise and interference of the order of 5-6 μV cause the error of about 0.2-0.3°C. It is shown that high noise immunity is essential for accurate temperature measurements using TCs.

[1] Józwik, J., Mika, D. (2015). Diagnostics of workpiece surface condition based on cutting tool vibrations during machining. Advances in Science and Technology Research Journal, 9 (26), 57-65.

[2] Glowacz, A., Glowacz, Z. (2016). Diagnostics of stator faults of the single-phase induction motor using thermal images, MoASoS and selected classifiers. Measurement, 93, 86-93.

[3] Fraczyk, A., Kucharski, J. (2017). Surface temperature control of a rotating cylinder heated by moving inductors. Applied Thermal Engineering, 125, 767-779.

[4] Krolczyk, G.M., Maruda, R.W., Nieslony, P., Wieczorowski, M. (2016). Surface morphology analysis of Duplex Stainless Steel (DSS) in Clean Production using the Power Spectral Density. Measurement, 94, 464-470.

[5] Kočí, V., Maděra, J., Jerman, M., Trník, A., Černý, R. (2014). Determination of the equivalent thermal conductivity of complex material systems with large-scale heterogeneities. International Journal of Thermal Sciences, 86, 365-373.

[6] Novák, M., Náprstková, N., Józwik, J. (2015). Analysis of the surface profile and its material share during the grinding inconel 718 alloy. Advances in Science and Technology Research Journal, 9 (26), 41-48.

[7] Wang, W.L., Zhang, H., Du, X.J., Sun, Y.Y. (2016). PCB-integrated thin film thermocouples for transient temperature measurement. Electronics Letters, 52 (13), 1140-1141.

[8] Mikhalieva, M., Hots, N., Mykyychuk, M., Dzikovska, Y. (2017). Use electric and acoustic technologies for automated control of water. In Advances in Intelligent Systems and Computing: Selected Papers from the International Conference on Computer Science and Information Technologies. Springer, Vol. 512, 293-303.

[9] Feldshtein, E., Józwik, J., Legutko, J. (2016). The influence of the conditions of emulsion mist formation on the surface roughness of AISI 1045 steel after finish turning. Advances in Science and Technology Research Journal, 10 (30), 144-149.

[10] Mellal, I., Laghrouche, M., Tien Bui, H. (2017). Field programmable gate array (FPGA) respiratory monitoring system using a flow microsensor and an accelerometer. Measurement Science Review, 17 (2), 61-67.

[11] Ferrero, A., Scotti, V. (2013). Forensic metrology: A new application field for measurement experts across techniques and ethics. IEEE Instrumentation & Measurement Magazine, 16 (1), 14-17.

[12] Birch, J. (2003). Benefit of legal metrology for the economy and society. A study for the International Committee of Legal Metrology.

[13] Webster, J. (1999). Measurement, Instrumentation, and Sensors Handbook. CRC Press.

[14] Childs, P.R.N., Greenwood, J.R., Long, C.A. (2000). Review of temperature measurement. Review of Scientific Instruments, 71, 2959-2978.

[15] Kochan, O., Kochan, R., Bojko, O., Chyrka, M. (2007). Temperature measurement system based on thermocouple with controlled temperature field. In 4th IEEE Workshop on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS 2007), 6-8 September 2007. IEEE, 47-51.

[16] Jun, S., Kochan, O., Kochan, V., Wang, C. (2016). Development and investigation of the method for compensating thermoelectric inhomogeneity error. International Journal of Thermophysics, 37 (1).

[17] Maruda, R.W., Krolczyk, G.M., Nieslony, P., Wojciechowski, S., Michalski, M., Legutko, S. (2016). The influence of the cooling conditions on the cutting tool wear and the chip formation mechanism. Journal of Manufacturing Processes, 24 (1), 107-115.

[18] Glowacz, A., Glowacz, A., Glowacz, Z. (2015). Recognition of thermal images of direct current motor with application of area perimeter vector and bayes classifier. Measurement Science Review, 15 (3), 119-126.

[19] Stadnyk, B., Yatsyshyn, S., Skoropad, P. (2012). Research in nanothermometry. Part 4. Amorphous alloys of thermo-resistive thermometry. Sensors and Transducers, 141 (6), 1-7.

[20] Machin, G., Bojkovski, J., del Campo, D. et al. (2014). A European roadmap for thermometry. International Journal of Thermophysics, 35 (3-4), 385-394.

[21] Palenčár, R., Sopkuliak, P., Palenčár, J., Ďuriš, S., Suroviak, E., Halaj, M. (2017). Application of Monte Carlo Method for evaluation of uncertainties of ITS-90 by Standard Platinum Resistance Thermometer. Measurement Science Review, 17 (3), 108-116.

[22] Park, R.M. (1993). Manual on the Use of Thermocouples in Temperature Measurement. ASTM International.

[23] Smalcerz, A., Przylucki, R. (2013). Impact of electromagnetic field upon temperature measurement of induction heated charges. International Journal of Thermophysics, 34 (4), 667-679.

[24] Kochan, R., Kochan, O., Chyrka, M., Vasylkiv, N. Precision data acquisition (DAQ) module with remote reprogramming. In IEEE Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS 2005), 5-7 September, 2005. IEEE, 279-282.

[25] Duan, Q. (2009). Research of a cold end temperature compensation for thermal couple. In IEEE International Conference on Automation and Logistics (ICAL '09), 5-7 August, 2009. IEEE, 921-924.

[26] Körtvélyessy, L. (1998). Thermoelement Praxis. Essen, Germany: Vulkan-Verlag. (in German).

[27] Sachenko, A., Kochan, V., Turchenko, V. (2000). Sensor drift prediction using neural networks. In International Workshop on Virtual and Intelligent Measurement Systems (VIMS 2000), 29-30 April, 2000, 88-92.

[28] Rogelberg, N., Nuzhnov, A., Pokrovskaya, G. et al. (1969). The stability of the chromel-alumel thermocouples’ thermoelectric power at temperatures up to 1200°C. Investigation of alloys for thermocouples. In Proceedings of the Giprotsvetmetobrabotka.

[29] Sloneker, K.C. (2009). Thermocouple inhomogeneity. Ceramic Industry, 159 (4), 13-18.

[30] Vasylkiv, N., Kochan, O., Kochan, R., Chyrka, M. (2009). The control system of the profile of temperature field. In IEEE International Workshop on Intelligent Data Acquisition and Advanced Computing Systems (IDAACS 2009), 21-23 September, 2009. IEEE, 201-206.

[31] Jun, S., Kochan, O., Kochan, R. (2016). Thermocouples with built-in self-testing. International Journal of Thermophysics, 37 (4).

[32] Woolley, J.W., Woodbury, K.A. (2009). Using computational models to account for thermocouple conduction error in cast metal/mold interfacial heat transfer experiments. In 113th Metalcasting Congress, 7-10 April 2009, Vol. 117, 31-40.

[33] Shu, C., Kochan, O. (2013). Method of thermocouples self verification on operation place. Sensors & Transducers, 160 (12), 55-61.

[34] Vasylkiv, N. (2010). Improvement of metrology software test in computer systems of temperature measurement. International Journal of Computing, 9 (2), 175-182.

[35] Jun, S., Kochan, O., Levkiv, M. (2017). Metrological software test for studying the method of thermocouple error determination during operation. In 11th International Conference on Measurement (Measurement 2017), 29-31 May, 2017. Bratislava, Slovakia: Institute of Measurement Science SAS, 171-174.

[36] International Organization for Standardization. (2010). Guide to the Expression of Uncertainty in Measurement (GUM).

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