PETRAN unifies asset health, risk, cost, and lifecycle data across manufacturing, oil & gas, utilities, and infrastructure portfolios into one reliability program - connecting condition monitoring, maintenance execution, and capital planning so every asset decision is backed by evidence, not guesswork.
Availability, performance, and quality gains realised across the full asset portfolio, not one machine.
Condition-based vs calendar-based spend, aggregated portfolio-wide.
Deferred capex from extended, evidence-based asset life - a capital-planning outcome PdM alone doesn't claim.
Combined reliability, cost, and capital-planning gains across the program, not a single pilot asset.
See how PETRAN turns real-time asset data into early warnings, RUL predictions, and evidence-backed maintenance actions.
Edge Sensing - Capture high-frequency asset condition data in real time.
PETRAN starts at the source, pulling continuous condition signals straight from equipment before anything is lost to sampling gaps or delayed reporting.
Combines sensor data across vibration, thermal, electrical, process, visual, and environmental sources into one consistent digital fingerprint, regardless of protocol.
Detects anomalies and triggers critical alerts in under a second at the machine, even when cloud connectivity is unavailable.
Learns across the entire asset fleet to detect performance drift, benchmark similar assets against each other, and rank where risk is concentrated.
Each asset's digital twin understands its operating states, duty cycles, setpoints, and history to improve diagnosis and reduce false alarms.
Converts risk-scored findings into actionable work orders and capital-planning inputs, using technician and finance feedback to continuously improve.
Prioritises maintenance and replacement spend based on asset criticality, risk, and remaining useful life, so labour, parts, and capital are focused where they matter most.
Combines AI governance, role-based access, SSO, audit trails, encryption, drift monitoring, and safe rollback for secure, trustworthy operations.
Open APIs connect PETRAN with historians, PLCs, DCS platforms, IoT systems, EAM/CMMS solutions, and enterprise data lakes without replacing existing investments.
A multi-plant manufacturer replaces inconsistent, site-by-site thresholds and reporting with one Asset Health Index and one escalation standard, so a risk ranking from Plant A means the same thing as a risk ranking from Plant C when capital and labour get allocated.
Operators combine asset-health evidence with remaining-life forecasts to decide which units get refurbished versus replaced this budget cycle - turning a capex conversation that used to run on inspection anecdotes into one backed by fleet-wide condition data.
Utilities rank transformers, switchgear, and substation equipment by combined criticality and risk score, so limited crews and budget go to the assets whose failure would matter most - not simply the ones due next on a calendar.
Infrastructure operators connect asset condition directly to availability and performance losses, giving leadership one dashboard that shows exactly where a reliability investment would move OEE, instead of a maintenance log disconnected from production numbers.
A normalised Asset Health Index combines vibration, thermal, electrical, and process data into a single score for fast fleet-wide condition monitoring.
Track precision, recall, detection time, and confidence to reduce false alarms, identify weak sensors or models, and continuously improve alert quality.
Measure failure frequency across assets, lines, and sites to identify where maintenance or operational changes can deliver the greatest reliability gains.
Break repair time into diagnosis, parts delay, and execution to expose bottlenecks and accelerate recovery through automated, guided maintenance workflows.
Combine failure probability with Remaining Useful Life ranges and confidence levels so maintenance teams can plan interventions at the right time.
Connect asset health and failure modes directly to availability, performance, and quality losses to show where reliability improvements will increase production.
Measure maintenance spend against production output to quantify savings as asset strategy shifts work from costly reactive repairs to planned, risk-prioritised interventions.
Track work-order completion, SLA performance, and technician evidence quality while feeding high-value field feedback back into models and maintenance procedures.
| 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 |
| Fleet Learning | None: each asset monitored in isolation; no cross-fleet intelligence | Cloud-scale AI learns across the entire asset fleet; transfers learnings to similar assets |
| Capital Planning Input | None: condition data rarely reaches finance or planning teams | Asset-health and RUL evidence feed directly into replacement vs refurbish decisions |
| Open Integration | Proprietary protocols common; EAM integration requires custom development | Open APIs; pre-built CMMS/EAM connectors (Maximo, SAP, Hexagon, Infor, ServiceNow) |
| 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 |
Tri-axial MEMS and IEPE sensors capture high-frequency vibration with edge FFT, enveloping, time synchronisation, and compression for scalable fault detection on rotating equipment.
Radiometric IR arrays and pyrometers use calibrated zone-based measurements to distinguish genuine thermal degradation from normal process variation across electrical and process equipment.
Class A power-quality meters monitor harmonics, flicker, imbalance, inrush, and transients to correlate electrical disturbances with trips, process anomalies, and mechanical faults.
Smart HART and Modbus instruments measure pressure and flow with cavitation, clogging, temperature compensation, and sensor-health diagnostics for reliable process monitoring.
Inline oil sensors track viscosity, moisture, ferrous particles, dielectric change, and ISO cleanliness trends to detect lubrication-related degradation before mechanical damage occurs.
Industrial cameras run ONNX/TensorRT inference for tracking, leak detection, and defect classification, supported by secure multi-protocol I/O, TPM, and containerised ML runtimes.
Explore how PETRAN can move from a focused reliability pilot to an enterprise-wide Asset Performance Management program.