Analyzes sensor telemetry to estimate failure probability and remaining useful life, scheduling maintenance proactively to minimize downtime and costs.
Stop reacting to breakdowns. Our predictive maintenance estimates failure probability and remaining useful life (RUL) for assets—so you schedule interventions before problems escalate. The result is higher uptime and lower maintenance costs.
How it works: sensor streams (vibration, temperature, current), logs, and maintenance history feed feature extraction and anomaly models. We combine physics-informed features with ML (survival analysis, gradient boosting) to predict failures. The dashboard shows asset health, risk scores, and recommended actions; work orders can auto-create in your EAM/CMMS. Post-maintenance feedback improves the model continuously.
Why Tagbin AI: we adapt to your machines and duty cycles instead of forcing generic thresholds. Our approach balances precision with practicality—fewer false alarms, clear next steps, and full auditability. Whether it’s plants, fleets, or utilities, you get predictable performance and planned downtime.
