文章基本信息

标题：A Bias Compensated Cross-Relation approach to Thermocouple Characterisation
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作者：Philip D. Gillespie ; Daniel Gaida ; Peter C. Hung 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2016
卷号：49
期号：5
页码：43-48
DOI：10.1016/j.ifacol.2016.07.087
语种：English
出版社：Elsevier
摘要：AbstractThe 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 influence of the measurement environment and changing conditions such as gas velocity, the model must be estimatedin-situ. The cross-relation method of blind deconvolution is one approach forin-situcharacterisation 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.
关键词：KeywordsCross-relationBlind sensor characterisationTemperature measurement