A Predictive Calibration Framework Based on the Degradation Model of Sensor Error

Sensor calibration is essential to ensure the accuracy and reliability of sensor measurements. However, existing calibration methods lack scientifically guided strategies and commonly rely on fixed-interval calibration schedules. Such approaches cannot adequately consider the inherent degradation characteristics of sensors, making them unsuitable for nonlinear degradation patterns and potentially causing resource waste or inadequate calibration. To address this issue, this paper proposes a predictive calibration framework based on the degradation model of sensor error. First, we establish deterministic degradation models under various temperature conditions. Subsequently, calibration schedules are derived based on the time required for degradation increments to reach a predefined threshold. A numerical case study demonstrates the application of the proposed method and provides a comparative analysis with traditional fixed-interval calibration strategies. The results show that fixed-interval schedules fail to meet performance requirements under nonlinear degradation scenarios, highlighting the effectiveness and superiority of the proposed predictive calibration framework.