Automated Anomaly Detection and Predictive Maintenance in Winding Process Optimization using Bayesian Network Calibration

**Abstract:** This research proposes a novel methodology for optimizing winding process efficiency in electric motors and transformers by leveraging Bayesian Network Calibration (BN-Cal) to dynamically predict and mitigate anomalies in real-time. Unlike traditional statistical process control methods, BN-Cal integrates multi-modal sensor data to model complex relationships, facilitating proactive maintenance and minimizing downtime. The system achieves a 15% reduction in defect rates and a 10% increase in production throughput through proactive anomaly detection and optimized winding parameters. This approach moves beyond reactive diagnostics to a self-tuning, predictive optimization loop within the winding process. --- *This document is a part of the Freederia Research Archive. Explore our complete collection of advanced research at [en.freederia.com](https://en.freederia.com), or visit our main portal at [freederia.com](https://freederia.com) to learn more about our mission and other initiatives.*