Predictive maintenance has long been limited to Fortune 500 plants due to prohibitive sensor costs and complex data science infrastructure. Poldu’s Neural Downtime Predictor changes this by training compact anomaly detection models directly on raw machine electrical signatures.
Key Capabilities
- Tool Wear Drift Analysis: Detects subtle increases in cutting resistance before part dimensions fall out of tolerance.
- Thermal Equilibrium Tracking: Alerts operators when cold-start morning shifts run before hydraulic oil reaches optimal viscosity.
- Automated Root Cause Ranking: Classifies whether cycle slowdown is caused by material hardness variations or operator hesitation.