Design of a Smart Pressure Transmitter and Its Temperature Compensation Using Artificial Neural Network
- 87 Downloads
This paper presents a smart pressure transmitter using bellow as primary sensor. The deflection of bellow is converted into electrical output using hall probe sensor as secondary sensor. The output Hall voltage is affected by change in input parameters like temperature. So firstly the effect of temperature on Hall voltage is derived mathematically and then experimentally analyzed. This effect of temperature on output Hall voltage is then compensated using artificial neural network. The compensated output Hall voltage is then converted into (4–20) mA current signal using signal conditioning circuit. The proposed design, experimental and testing results are reported in this paper.
KeywordsArtificial neural network (ANN) Pressure measurement Bellows Temperature compensation
- Bentley, P. (1995). Principles of measurement systems. Singapore: Longman Singapore publishers Ltd.Google Scholar
- Chattopadhyay, S., & Sarkar, J. (2012). Design and development of a reluctance type pressure transmitter. In 7th IEEE international conference on electrical & computer engineering (ICECE) (pp. 70–73). https://doi.org/10.1109/ICECE.2012.6471487.
- Chen, G., Sun, T., Wang, P., & Sun, B. (2006). Design of temperature compensation system of pressure sensors. In IEEE international conference on information acquisition (pp. 1042–1046). https://doi.org/10.1109/ICIA.2006.305883.
- Liptak, B. G. (1999). Process measurement and analysis. Oxford: Butterworth Heinman.Google Scholar
- Rath, S. K., Patra, J. C., & Kot, A. C. (2000). An intelligent pressure sensor with self-calibration capability using artificial neural networks. In IEEE international conference on systems, man, and cybernetics (pp. 2563–2568). https://doi.org/10.1109/ICSMC.2000.884379.
- Reverter Cubarsí, F., Horak, G., Bilas, V., & Gasulla Forner, M. (2009). Novel and low-cost temperature compensation technique for piezoresistive pressure sensors. In Fundamental and applied metrology XIX IMEKO World Congress (pp. 2084–2087). http://hdl.handle.net/2117/12816.
- Yegnanarayana, B. (2005). Artificial neural networks. New York: Academic.Google Scholar