The purpose of this study was to develop an online sensor calibration monitoring (OSCM) technique for detecting the drift of sensors and, furthermore, for preventing unnecessary maintenance, such as calibration in nuclear power plants. The Kalman filter was employed for the detection of the drift of multiple sensors. In addition, conventional methods, i.e., principal component analysis, independent component analysis, and the instrument calibration and monitoring program, were used for comparison. Simulation data with artificial drift was utilized to evaluate the OSCM techniques developed, and performance was evaluated in terms of accuracy, sensitivity, and drift threshold detection. In the results, it was observed that the Kalman filter enhances detection accuracy and reduces sensitivity. Moreover, the Kalman filter makes it possible to detect the drift threshold in an acceptable time period. According to the results, the Kalman filter is suitable for the OSCM technique with enhanced accuracy and precision.
