The lay abstract featured today (for Novel Calibration Strategy for Kalman Filter-Based Measurement Fusion Operation to Enhance Aging Monitoring by Emre Genis and Duygu Bayram Kara) is from Quality and Reliability Engineering International with the full Open Access article now available to read here.

How to Cite

Genis, E. and Bayram Kara, D. (2025), Novel Calibration Strategy for Kalman Filter-Based Measurement Fusion Operation to Enhance Aging Monitoring. Qual Reliab Engng Int.. https://doi.org/10.1002/qre.3789

Lay Abstract

The early detection of component aging in industrial systems is crucial for ensuring operational
reliability and minimizing unplanned maintenance. In this context, various signal processing and
data fusion techniques have been employed to interpret sensor measurements and extract
meaningful indicators of degradation. Data fusion through Kalman filter is one of the widely used
approaches, which combines information from multiple sources to provide a cleaner, more
informative signal. While many recent studies have focused on advanced nonlinear variants of this
filter, these methods often introduce considerable complexity and computational cost.

In this study, a novel calibration strategy was developed for the Linear Kalman Filter (LKF),
offering a more efficient yet effective alternative for aging and fault detection. Rather than relying
on complex mathematical formulations, this approach optimizes the LKF to preserve critical
information across a broad frequency spectrum, allowing for more accurate monitoring of machine
health.

The proposed method was validated using real experimental data collected from electric motors
subjected to accelerated aging. By fusing current and vibration signals, the LKF was shown to
effectively retain both mechanical and electrical fault signatures. The strategy also preserved high-frequency features often lost in conventional filtering, which are essential for capturing early
indicators of degradation.

Beyond its technical performance, the approach offers practical advantages: it simplifies
computation, reduces data size, and enables real-time implementation in industrial settings. In
addition to capturing signs of wear, the fused signals also uncovered possible manufacturing
defects, demonstrating the method’s extended diagnostic capability. This work emphasizes the
value of well-calibrated, computationally efficient tools in modern condition monitoring and
predictive maintenance systems.