A Multisource Signal-Conditioned Fiber Optic Sensing System for Microwave Temperature Acquisition and Prediction

• Published in: IEEE transactions on instrumentation and measurement Vol. 74; pp. 1 - 12
• Main Authors: Meng, Chenyang, Hao, Kuangrong, Cheng, Yan, Xu, Jinhai, Zhu, Xiuli
• Format: Journal Article
• Language: English
• Published: New York IEEE 2025; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Algorithms; Automatic temperature control algorithm; Brain; brain inspiration; Control algorithms; Data acquisition; Data collection; Data processing; Data structures; Electromagnetic heating; fiber optic temperature sensing system; Fiber optics; Heating; Heating systems; Microwave heating; Microwave measurement; Microwave ovens; multisensor data; Optical fibers; Prediction algorithms; Predictive control; Sensor arrays; Sensors; Standing waves; Temperature; Temperature control; Temperature distribution; Temperature measurement; Temperature sensors; temperature time series data prediction
• ISSN: 0018-9456; 1557-9662
• DOI: 10.1109/TIM.2025.3527547

Due to the existence of electromagnetic standing waves, uniform heating and accurate temperature data collection during microwave cooking have always been a challenge. This article presents a multisource temperature data acquisition system for microwave heating environments and introduces a brain-inspired prediction algorithm based on data structure to enhance temperature control logic. First, a temperature monitoring system for microwave heating environments, termed fiber optic temperature sensing system (FOTS), is developed. This system consists of an eight-probe fiber optic temperature sensor (FOT) array and an infrared temperature sensor. Then, a brain-inspired audiovisual (BIAV) predictive algorithm is proposed to simulate the brain's integration mechanism. The core of this algorithm extracts key time series data, calculates trend features to output the related key moment labels, and puts forward a vision-enhanced LSTM for prediction. Finally, experiments demonstrate the accuracy of BIAV in predicting food temperatures using sensor data, and existing control algorithms are optimized and validated. The proposed FOTS and BIAV algorithm in microwave fields provide new ideas and methods for multisensor data processing. In addition, existing control algorithms are optimized using predicted accurate temperature values and 2-D temperature fields, making microwave heating more uniform and safer, which is of great significance for industrial control.