FactoryMind AI — Model Trust Lab

FactoryMind AI — Model Trust Lab helps maintenance engineers, reliability teams, and factory operations managers evaluate machine-learning predictions before acting on them. It addresses a practical risk: a green “Healthy” prediction can still be wrong when operating cycles are correlated and accuracy overstates reliability. The prototype uses the UCI Condition Monitoring of Hydraulic Systems dataset: 2,205 one-minute hydraulic test-rig cycles and 30 engineered pressure, flow, temperature, and vibration features. A Random Forest predicts valve condition. Random holdout accuracy is 98.01%, while five-fold blocked validation with purging reports 82.86%. Across blocked folds, 260 non-optimal cycles were incorrectly classified as Optimal. FactoryMind prioritizes these false-green predictions for human review. At a 20% review budget, confidence ranking identifies 136 of the 260 errors by reviewing 266 of 1,344 predicted-healthy cycles. The Model Trust Lab also tests 50 hypothetical sensor-gain scenarios. Stable predictions can still be wrong, and the initial stress-based ranking did not outperform confidence-only review. This negative result is reported openly. For each selected cycle, an operator can inspect sensor evidence, review the prediction, request an AMD Token Factory Evidence Hearing using DeepSeek-V4-Flash, and approve or reject an inspection recommendation. The hosted model selects evidence and safe review checks from a bounded schema; measurements come from the recorded experiment. Decisions are stored in SQLite and exported as checksum-verified evidence records. No machine-control commands are issued. FactoryMind demonstrates evidence-first model review for industrial maintenance workflows. Results are retrospective test-rig measurements, not proof of factory safety, downtime reduction, or cost savings. Physical validation, independent-rig testing, calibration, security controls, and operator studies are required before real-plant deployment.