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Gas Sensor Array Dynamic Measurement Uncertainty Evaluation and Optimization Algorithm
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DOI：10.1109/ACCESS.2019.2898881.png)
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Abstract
En 中文
Metal oxide semiconductor (MOS) gas sensor array dynamic measurement uncertainty evaluation, which currently mainly uses static measurement approximation estimations, cannot accurately distinguish the measured value of the sudden change of the measured value caused by the dynamic mutation of the measured gas or the actual sensor array local fault caused by the sudden change of the measured value, thereby resulting in a dynamic measurement process of the MOS gas sensor array uncertainty evaluation results and the validation measurement value reliability being greatly reduced. This paper presents a dynamic adaptive Kalman filter andGray bootstrap comprehensive modified prediction model for the dynamic measurement uncertainty evaluation. The dynamic adaptive Kalman filter and Gray bootstrap method is used to estimate the good performance of the probability distribution function of the measured value, and the uncertainty evaluation of the dynamic measurement state of the MOS gas sensor array is realized. Using the correlation of the dynamic adaptive Kalman filter and Gray model multisensor output, a new MOS gas sensor array measurement value confirmation algorithm is proposed to distinguish the measured value mutation caused by the normal dynamic mutation of the measured gas and the fault of the real sensor array. Finally, the MOS gas sensor array measurement is set up, and the experiments show that the proposed MOS gas sensor array dynamic measurement uncertainty evaluation and an optimization algorithm is effective.
Keywords:
Sensor array
dynamic measurement
uncertainty
dynamic adaptive Kalman filter
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