Identification of Time-Varying Time Constants of Thermocouple Sensors and Its Application to Temperature Measurement

摘要

The present study addresses the problem of estimating time-varying time constants asso-nciated with thermocouple sensors by a set of basis functions. By expanding each time-nvarying time constant onto a finite set of basis sequences, the time-varying identificationnproblem reduces to a parameter estimation problem of a time-invariant system. Thenproposed algorithm, to be called as orthogonal least-squares with basis function expan-nsion algorithm, combines the orthogonal least-squares algorithm with an error reductionnratio test to include significant basis functions into the model, which results in a parsi-nmonious model structure. The performance of the method was compared with a linearnKalman filter. Simulations on engine data have demonstrated that the proposed methodnperforms satisfactorily and is better than the Kalman filter. The new technique has beennapplied in a Stirling cycle compressor. The sinusoidal variations in time constant arentracked properly using the new technique, but the linear Kalman filter fails to do so. Bothnmodel validation and thermodynamic laws confirm that the new technique gives unbiasednestimates and that the assumed thermocouple model is adequate.