Predictive maintenance software helps you spot failure signs early using real equipment data. Our IoT predictive maintenance solution on Petran turns those signals into clear alerts and planned actions. See it on your assets.
Ombrulla's predictive maintenance solution (built on PETRAN) turns data from sensors, PLC/SCADA, historians, and inspections into:
Early warnings with evidence: what changed, when, and why it matters
Failure probability + RUL windows: you can plan around
Recommended actions: what to check first, likely causes, and what typically fixes it
Closed-loop execution: create work orders in your CMMS/EAM and track outcomes
From maintenance teams to multi-site operations leaders, predictive maintenance helps reduce firefighting and improve asset reliability.
Stop firefighting. Prioritize the assets most likely to fail soon and focus troubleshooting on probable failure modes (bearing wear, misalignment, imbalance, lubrication issues, cavitation, overheating).
Improve uptime and schedule maintenance around production without excessive time-based tasks that don't reflect actual asset condition.
Standardize condition monitoring and decision-making across plants with consistent rules, governance, and KPIs.
Portfolio-wide visibility into reliability risk, maintenance cost trends, and ROI from the predictive maintenance programme.
Teams usually start looking for predictive maintenance software when downtime keeps happening even with PM in place. These are the common reasons people evaluate AI + IoT predictive maintenance solutions.
Bring in PLC/SCADA tags, historian data, existing sensors, and inspection records. Add new sensors only where coverage gaps exist.
The system learns "normal" across operating modes (load, batch, environment), not just a single static threshold.
Spot subtle drift in vibration, temperature, current, pressure/flow, acoustics, and process behavior.
Go beyond "something changed" to "what it likely indicates" (e.g., imbalance vs. misalignment vs. bearing wear).
Provide a planning horizon with confidence so maintenance can align manpower, spares, and downtime windows.
Push actions into CMMS/EAM with evidence, recommended checks, and documentation for auditability.
Close the loop completed work and outcomes improve future alert accuracy and reduce false positives.
Shift from "replace just in case" to fix what's actually drifting, reducing emergency overtime, repeat repairs, and unplanned spares spend.
Repair during planned windows instead of surprise line stops stabilizing output and reducing last-minute decisions that impact quality.
Maintain an evidence trail: what was detected, who acted, what was done, and what improved critical for regulated and safety-sensitive operations.
Start with your top critical assets first. Use existing data sources where possible. Expand step-by-step across lines, areas, and plants.
Bring together signals from sensors, PLC/SCADA, historians, and inspections into a normalized view teams can trust.
Outcome
Unified data foundation for all asset monitoring
