PETRAN fuses IoT sensing, edge intelligence, and cloud AI to predict equipment failure before it happens, automate CMMS/EAM workflows, and deliver measurable OEE improvement, across manufacturing, oil & gas, utilities, and infrastructure.
PETRAN is Ombrulla’s AI- and IoT-powered Asset Performance Management (APM) platform that helps industries monitor asset health in real time, predict failures before they happen, and automate maintenance actions to improve uptime, safety, and cost efficiency.
Gartner / McKinsey APM benchmark
Condition-based vs calendar-based
Optimised maintenance intervals
Multi-signal AI fusion vs single-sensor
PETRAN unifies data from legacy and modern industrial assets across all major protocols into one standardised stream. It connects to existing sensors, PLCs, and SCADA systems without requiring hardware replacement.
PETRAN’s edge agents analyse data directly at the asset, extracting key features and scoring anomalies before sending anything to the cloud. This enables sub-second alerts and reliable first-line triage even during network outages.
The cloud AI layer learns asset and fleet behaviour across thousands of operating cycles to detect subtle degradation and classify failure modes. It also forecasts Remaining Useful Life (RUL) for each monitored asset, with confidence intervals.
A continuously updated digital twin is maintained for each monitored asset, combining live sensor data with duty cycles, setpoints, process states, work history, and maintenance records. This operational context helps the AI separate true degradation from normal process changes, reducing false alarms.
When a developing fault is detected, a work order is automatically created in connected CMMS or EAM platforms such as IBM Maximo, SAP PM/EAM, Hexagon EAM, Infor EAM, or ServiceNow. Each work order includes AI-generated diagnostic evidence, including sensor readings, spectral charts, failure mode classification, recommended corrective action, and required parts.
Maintenance is prioritised by risk score, asset criticality, and Remaining Useful Life (RUL) instead of fixed preventive schedules. Reliability teams get a live priority queue showing which assets need attention first, how much time remains before failure, and the likely production impact.
AI models are version-controlled, approved through governed deployment workflows, monitored for drift, and can be rolled back safely if performance declines. Human-in-the-loop controls and change-control approvals ensure high-consequence actions and policy changes remain secure and accountable.
Open integrations connect historians, PLC/DCS systems, IoT platforms, data lakes, and enterprise software through standard industrial protocols and APIs. Pre-built connectors, along with REST, GraphQL, and Kafka/Azure Event Hub support, make it easy to integrate with virtually any standard interface.
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Inspect hard-to-access infrastructure, find defects early, without shutdowns.
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AI predictive maintenance ensures reliable power, prevents failures.
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AI predictive maintenance prevents failures, cuts downtime, boosts uptime.
Read UseCaseA single, normalised score per asset that blends vibration, thermal, electrical, and process context. Trended over time, benchmarked by asset class and criticality. Drill down to the specific sensor features driving degradation. AHI thresholds trigger automated workflow actions at configurable risk levels.
Measures true positives vs. noise to keep operators focused on genuine faults. Reports precision/recall, time-to-detect, and alert confidence intervals. Flags sensors or models that need recalibration. Tracks precision improvement as AI models are retrained on new operational data.
Quantifies reliability across lines, sites, and asset classes. Sliced by operating mode, workload, and environment to identify where design changes or PM tasks will yield the largest reliability improvement. Baseline MTBF established at deployment; improvement tracked monthly.
Exposes delays from first alert to asset restoration. Breaks MTTR into diagnosis time, parts wait, and execution time. Surfaces workflow bottlenecks and standardises fixes through guided CMMS procedures. MTTR reduction targets linked to PETRAN alert-to-work-order automation performance.
Risk-scored failure probability with Remaining Useful Life ranges and confidence intervals per asset. Prioritises maintenance interventions by risk severity and window width. Enables simulation of deferral or advancement decisions before committing resources.
Ties condition insights directly to production outcomes. Attributes OEE losses to specific assets and failure modes. Verifies OEE improvement as predictive maintenance tasks reduce unplanned downtime and micro-stops. Supports production planning and capacity commitments.
Tracks maintenance spend normalised by throughput (£/unit, $/tonne, $/MWh). Compares planned vs. unplanned cost split, labour vs. parts mix. Demonstrates ROI as predictive interventions shift cost from reactive to planned. Reported by asset class, line, site, and region.
Monitors work order completion rates, SLA adherence, and technician feedback quality. Scores work orders on evidence quality (photos, vibration captures, observations). Loops high-quality technician inputs back into model retraining and SOPs. Tracks the AI accuracy improvement driven by feedback.
BLE, LoRaWAN, and LTE-M vibration nodes make it easy to monitor pumps, motors, fans, and conveyors without heavy wiring or complex retrofits.
Tri-axial MEMS and IEPE sensors capture high-frequency machine data with on-node FFT, enveloping, edge compression, and precise hardware time sync for faster fault detection.
Radiometric IR arrays and pyrometers detect abnormal heat patterns across electrical panels, bearings, furnaces, and process equipment with calibrated, zone-based monitoring.
Class A power-quality meters track RMS, THD, harmonics, flicker, imbalance, inrush, and transients, helping teams link electrical disturbances to trips, process issues, and mechanical faults.
Smart HART and Modbus instruments deliver accurate pressure and flow data, along with cavitation and clogging indicators, temperature compensation, and sensor health diagnostics.
Inline oil sensors track viscosity, moisture, ferrous particles, dielectric change, and ISO code trends to detect lubrication issues before they turn into mechanical damage.
Industrial cameras (global shutter) with inference at the edge (ONNX/TensorRT runtimes) for belt tracking, steam/leak detection, and surface defect classification. Lens distortion correction and lighting normalisation. Integrates with TRITVA visual AI skills library.
By combining connected hardware with AI-driven diagnostics, PETRAN helps cut maintenance costs, reduce unplanned downtime, extend asset life, improve OEE, accelerate planning, and strengthen compliance readiness.
Condition-based maintenance uses sensor data to service assets only when needed, reducing costs by 10–25% within a year.
AI anomaly detection finds early faults, enabling planned maintenance and reducing unplanned downtime by 30–50%.
Operating assets within optimal conditions extends component life by 20–40%, driven by condition-based scheduling and optimisation.
Stable assets reduce micro-stops and losses. PETRAN links asset health to OEE, helping teams trace losses and verify improvements.
| Dimension | Traditional APM / Threshold Monitoring | PETRAN AI APM Platform |
|---|---|---|
| Failure Detection Method | Threshold rules: triggers on current sensor value exceeding a fixed limit, reactive by design | AI analyses multi-signal patterns over time; detects developing faults weeks before threshold breach |
| Failure Prediction | None: no prediction capability; alert fires when fault is already present | Calculates failure probability, days-to-failure, and RUL with confidence intervals per asset |
| False Alarm Rate | High: single-sensor threshold rules generate high noise; alert fatigue common | Low: multi-signal AI fusion reduces false positives 60–80%; severity-scored alert routing |
| Work Order Creation | Manual: operator must recognise fault, transcribe data, and raise ticket in CMMS | Automatic: AI raises CMMS/EAM work order with diagnostic evidence and recommended action attached |
| Fleet Learning | None: each asset monitored in isolation; no cross-fleet intelligence | Cloud-scale AI learns across entire asset fleet; transfers learnings to similar assets |
| Model Accuracy Over Time | Static: model performance does not improve after deployment | Continuously trained on site-specific data; technician feedback loops compound accuracy over time |
| Open Integration | Proprietary protocols common; EAM integration requires custom development | Open APIs; pre-built CMMS/EAM connectors (Maximo, SAP, Hexagon, Infor, ServiceNow) |
| Deployment Options | Typically cloud-only or on-premises only; no edge AI | SaaS, Private Cloud / On-Premises, Hybrid Edge + Cloud, offline-first edge agents |
| Governance & Explainability | Limited: few platforms offer model versioning, drift monitoring, or explainable AI outputs | Full MLOps: version control, drift detection, rollback, human-in-the-loop, explainable AI |
Pipeline integrity, rotating equipment health, flare stack inspection, HSE compliance, and remote site surveillance. PETRAN supports offshore and remote operations with offline-first edge agents, lone worker safety workflows, and compliance readiness for IEC 62443 and PSSR.
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