Noncontact thermal mapping method based on local temperature data using deep neural network regression. (February 2022)

• Record Type: Journal Article
• Authors: Shin, Sanghun; Ko, Byeongjo; So, Hongyun
• Is Part Of: International journal of heat and mass transfer. Volume 183:Part C(2022)
• Publication Date: 2022-02
• Subjects: Thermal imaging -- Temperature mapping -- Virtual sensors -- Deep neural network -- Thermal management; Heat -- Transmission -- Periodicals; Mass transfer -- Periodicals; Chaleur -- Transmission -- Périodiques; Transfert de masse -- Périodiques; Electronic journals; 621.4022
• DOI: 10.1016/j.ijheatmasstransfer.2021.122236
• Languages: English
• ISSNs: 0017-9310

Abstract

Highlights: Noncontact temperature mapping method was developed using a deep neural network model. Overall temperature prediction was realized with an accuracy of 96.7% using three input data. Temperature mapping was achieved even with input data in a distant position from heat source. Using this DNN model, temperature field of packaged electronics can be easily estimated. Abstract: Temperature monitoring of electronic devices is important to prevent the active components from overheating. In this study, a novel method to obtain the overall temperature map of electronic modules with limited local temperature data was suggested using a deep-neural-network-based multi-output regression model. To predict the entire temperature distribution with minimum data, one to six random inputs considering their diverse arrangements were applied and compared by calculating the mean absolute error. In addition, the temperature prediction accuracy of the heating element was considered as an important parameter for the performance score. Consequently, a temperature prediction accuracy of ∼96.7% was realized using three input local data points close to the heat source. Furthermore, with the other three temperature data points away from the heat source, the score increased by ∼11.6% (∼79.9 to ∼89.2%) after the hyperparameter tuning processes. These results support the precise noncontact virtual sensing technology of temperature monitoring methods for various industries, such as electric vehicles, cold-chain warehouses, and robotics.