A Bias Compensated Cross-Relation approach to Thermocouple Characterisation

Philip Gillespie, Daniel Gaida, Peter Hung, Robert Kee, Sean McLoone

Research output: Contribution to conferencePaper

2 Citations (Scopus)

Abstract

The measurement of fast changing temperature fluctuations is a challenging problem due to the inherent limited bandwidth of temperature sensors. This results in a measured signal that is a lagged and attenuated version of the input. Compensation can be performed provided an accurate, parameterised sensor model is available. However, to account for the in influence of the measurement environment and changing conditions such as gas velocity, the model must be estimated in-situ. The cross-relation method of blind deconvolution is one approach for in-situ characterisation of sensors. However, a drawback with the method is that it becomes positively biased and unstable at high noise levels. In this paper, the cross-relation method is cast in the discrete-time domain and a bias compensation approach is developed. It is shown that the proposed compensation scheme is robust and yields unbiased estimates with lower estimation variance than the uncompensated version. All results are verified using Monte-Carlo simulations.
Original languageEnglish
Publication statusPublished - Jun 2016
Event4th IFAC International Conference on Intelligent Control and Automation Sciences - Reims, France
Duration: 01 Jun 201603 Jun 2016
Conference number: 4
http://icons2016.univ-reims.fr/

Conference

Conference4th IFAC International Conference on Intelligent Control and Automation Sciences
Abbreviated titleICONS 2016
CountryFrance
CityReims
Period01/06/201603/06/2016
Internet address

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    Gillespie, P., Gaida, D., Hung, P., Kee, R., & McLoone, S. (2016). A Bias Compensated Cross-Relation approach to Thermocouple Characterisation. Paper presented at 4th IFAC International Conference on Intelligent Control and Automation Sciences, Reims, France.