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TE-MIO: A Gas Sensor Array Optimization Method for Rubber Mooney Detection
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DOI：10.1109/TIM.2025.3619266.png)
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Abstract
En 中文
Current rubber Mooney viscosity measurement methods are severely limited by their destructive nature and the long time delays in providing real-time feedback on rubber quality. Gas sensing technology has emerged as a promising solution to enable real-time Mooney viscosity measurement due to its noninvasive advantages. However, the high cost and long sampling periods required for diverse sensors pose significant challenges to system feasibility. In this article, we propose a time-efficient mutual information optimization (TE-MIO) method for sensor array optimization. This method aims to reduce the number of sensors and shorten measurement time while ensuring measurement performance. The proposed method is implemented in a designed olfactory-based Mooney viscosity measurement system. A feature extractor is developed for multiscale temporal feature extraction, enabling comprehensive analysis of sensor responses at different time scales. Furthermore, metrics such as correlation, efficiency, and interference resistance are incorporated into an improved mutual information optimization model, enhancing the anti-interference capability of the optimized sensor array. Finally, physical experiments validate the effectiveness and performance of the proposed method. The experimental results demonstrate the effectiveness of the proposed method and its advantages over the competitors. The results demonstrate that the TE-MIO method reduces the number of sensors by 25% and shortens the sampling time by 17%.
Keywords:
Feature extraction
rubber Mooney viscosity measurement
sensor array optimization
sensor sensitivity
time-efficient mutual information optimization (TE-MIO)
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5.9
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