Stop reacting to breakdowns. Our AI systems detect thermal anomalies, vibration patterns, and wear signatures days before failure — giving your maintenance team time to act.
VisionForge deploys thermal cameras, high-speed optical sensors, and vibration-imaging hardware at critical equipment points. AI models track degradation signatures continuously, learning what "normal" looks like and alerting when patterns deviate.
FLIR-integrated thermal cameras spot bearing overheating, motor hotspots, and electrical faults invisible in visible light.
High-speed vision captures micro-vibrations in rotating machinery, identifying imbalance, misalignment, and bearing wear.
Condition-based alerts sent to CMMS, mobile apps, and SCADA with severity scoring and recommended actions.
Our models build a unique baseline for every piece of equipment. Through continuous learning, they develop sensitivity to the earliest signs of degradation specific to each machine's age, load profile, and operating environment.
Health score trajectories plotted over weeks and months to forecast when equipment will require intervention.
AI correlates anomalies across upstream and downstream equipment to identify root causes in complex production systems.
Optimal maintenance windows suggested based on production schedules and predicted time-to-failure windows.
Vision-based sensing covers fault modes that traditional vibration sensors miss entirely.
Motor winding failures, bearing overheating, electrical connection issues, and cooling system blockages.
Thermal VisionFan blade wear, shaft misalignment, coupling degradation, and impeller fouling in pumps and compressors.
High-Speed OpticalChain elongation, gear tooth pitting, conveyor belt delamination, and surface wear progression.
Surface VisionOil seeps, coolant leaks, and hydraulic fluid contamination detected before catastrophic failure.
Spectral ImagingArc flash precursors, switchgear deterioration, and insulation breakdown detected thermally.
Thermal VisionThroughput reduction, cycle time drift, and output quality decline correlated to equipment condition.
Process Analytics