AI-powered condition monitoring and failure prediction systems that detect equipment degradation weeks before failure โ eliminating unplanned downtime and transforming maintenance from reactive to predictive.
Surya's predictive maintenance platform combines edge IoT sensors, machine learning models and maintenance workflow integration to keep critical infrastructure running reliably.
Industrial-grade wireless vibration, temperature, current and acoustic emission sensors that attach to critical assets โ motors, pumps, gearboxes, fans and bearings โ streaming data continuously to the edge analytics layer without wiring modifications to existing equipment.
ML models trained on asset-class-specific failure signatures โ bearing raceway defects, motor winding insulation degradation, gearbox tooth wear and pump cavitation โ issuing probability-ranked failure alerts with estimated remaining useful life calculations.
Single-pane-of-glass dashboard providing real-time asset health status across the entire fleet or plant โ colour-coded by health score, with drill-down to individual sensor readings, trend charts, historical events and scheduled maintenance actions.
Specialised predictive maintenance for railway assets โ monitoring loco traction motors, wheel bearing health, track geometry sensors and overhead equipment โ aligned with Indian Railways and metro maintenance workflows and RDSO inspection standards.
Start your predictive maintenance journey โ discuss sensor deployment, ML model development or CMMS integration with our reliability engineering team.