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A Bias Compensated Cross-Relation approach to Thermocouple Characterisation

机译：偏置补偿交叉关系法进行热电偶表征

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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 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.
机译：由于温度传感器的固有带宽有限，因此快速变化的温度波动的测量是一个具有挑战性的问题。这导致测量信号是输入的滞后和衰减版本。如果可以使用精确的参数化传感器模型，则可以执行补偿。但是，要考虑测量环境和变化条件（例如气体速度）的影响，必须就地估算模型。盲反卷积的互相关方法是传感器原位表征的一种方法。但是，该方法的缺点在于，它在高噪声水平下会变得正向偏置且不稳定。本文在离散时域中采用了互相关方法，并提出了一种偏差补偿方法。结果表明，所提出的补偿方案是鲁棒的，并且产生的无偏估计具有比无补偿版本低的估计方差。所有结果均使用蒙特卡洛模拟进行了验证。

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