Adaptive Calibration of Soft Sensors Using Optimal Transportation Transfer Learning for Mass Production and Long‐Term Usage

Soft sensors suffer from high manufacturing tolerances and signal drift from long‐term usage, which degrades their practicality. Although deep learning has recently been proposed to address these issues, it is expensive in terms of data collection and processing. Therefore, an adaptive calibration method is proposed for soft sensors, suitable for mass production and long‐term usage. In addition to maintaining the original benefits of deep learning characterization, this method enables fast and accurate calibration by capturing the change in the characteristics of the sensor through domain adaptation, using optimal transportation. An offline calibration method is first described, which is for alleviating the difficulty in calibrating every single unit from mass produced soft sensors. The main advantage is that identically manufactured soft sensors in a large volume with variations can be calibrated with reduced time and effort for collecting and processing data. Online calibration is then discussed, which compensates for the parameter changes when a soft sensor is continuously used for an extended period of time. For a single sensor, even though the sensor shows signal drift from the long‐term usage, the calibrated network weights can be quickly adjusted online. Finally, the proposed adaptive calibration is experimentally evaluated using actual soft sensors. Herein, the use of transfer learning is proposed to overcome practical issues of using soft sensors in real‐world applications. Using optimal transportation theory, mass calibration of soft sensors is possible with reduced calibration time. In addition, sensors can be used for an extended period of time by online weight adaptation.