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This canvas implements an Autonomous Mechanical Health Monitor for detecting early-stage bearing degradation in CNC spindle units by fusing thermal and vibration sensor data into a "Friction Factor" baseline for anomaly detection and remaining useful life (RUL) prediction. The workflow generates synthetic sensor data, engineers a friction factor feature combining vibration and temperature, identifies critical system states using statistical control limits, and trains a Random Forest model to predict equipment failure timelines.