Novel Calibration Strategy for Kalman Filter-Based Measurement Fusion Operation to Enhance Aging Monitoring

This study introduces a novel calibration strategy for the linear Kalman filter (LKF) fusion application in condition monitoring, specifically utilizing accelerated aging data. Unlike the existing literature that often focuses on complex nonlinear Kalman filter variants, this research delves into the calibration of LKF. By using experimental data comprising current and vibration signals that represent various aging stages of an induction motor (IM), the study evaluates LKF's feature combination capabilities. The accelerated aging data provide a solid foundation for analyzing degradation patterns and reliability. Emphasis is placed on the importance of measurement normalization and the configuration of the observation matrix to achieve an optimally representative fused signal reflecting the IM's condition. The parameters are fine-tuned based on the spectral properties of the measured signals, ensuring filter stability. Ultimately, the study demonstrates that a well-calibrated LKF accurately captures the essential features in the measured signals, successfully integrating indications of aging and manufacturing imperfections from both current and vibration signals. These findings offer valuable insights into the application of LKF in degradation-based reliability analysis, particularly in condition monitoring, remaining useful life (RUL) prediction, and preventive maintenance. This research contributes to the emerging topics in modern degradation-based reliability analysis by providing a practical application of LKF in accelerated degradation tests, degradation modeling, and maintenance optimization based on degradation data.