Blind Characterisation of Sensors with Second-Order Dynamic Response

Philip Gillespie, Peter Hung, Robert Kee, Sean McLoone

Research output: Contribution to conferencePaperpeer-review

2 Citations (Scopus)

Abstract

Compensation for the dynamic response of a temperature sensor usually involves the estimation of its input on the basis of the measured output and model parameters. In the case of temperature measurement, the sensor dynamic response is strongly dependent on the measurement environment and fluid velocity. Estimation of time-varying sensor model parameters therefore requires continuous textit{in situ} identification. This can be achieved by employing two sensors with different dynamic properties, and exploiting structural redundancy to deduce the sensor models from the resulting data streams. Most existing approaches to this problem assume first-order sensor dynamics. In practice, however second-order models are more reflective of the dynamics of real temperature sensors, particularly when they are encased in a protective sheath. As such, this paper presents a novel difference equation approach to solving the blind identification problem for sensors with second-order models. The approach is based on estimating an auxiliary ARX model whose parameters are related to the desired sensor model parameters through a set of coupled non-linear algebraic equations. The ARX model can be estimated using conventional system identification techniques and the non-linear equations can be solved analytically to yield estimates of the sensor models. Simulation results are presented to demonstrate the efficiency of the proposed approach under various input and parameter conditions.
Original languageEnglish
Publication statusPublished - Oct 2015
Event17th IFAC Symposium on System Identification - Beijing, China
Duration: 19 Oct 201521 Oct 2015

Conference

Conference17th IFAC Symposium on System Identification
CountryChina
CityBeijing
Period19/10/201521/10/2015

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