Question 1

What is AI Asset Performance Management (APM) software?

Accepted Answer

AI Asset Performance Management (APM) software is an industrial technology platform that uses artificial intelligence and IoT sensor data to continuously monitor physical assets — machines, equipment, infrastructure, and facilities — predict failures before they occur, and optimise maintenance decisions. AI APM replaces reactive and calendar-based maintenance with data-driven, condition-based approaches that reduce downtime, lower costs, and extend asset service life.

Question 2

What is PETRAN and what does it do?

Accepted Answer

PETRAN is an AI and IoT asset performance management platform developed by Ombrulla. It monitors physical assets, infrastructure, and construction sites in real time using IoT sensors, industrial cameras, and AI analytics. PETRAN predicts equipment failures, manages digital inspection workflows, automates maintenance work orders, and enforces worker safety compliance — all from a single unified platform deployable on cloud, on-premises, or hybrid infrastructure.

Question 3

How does AI/IoT APM work?

Accepted Answer

An AI/IoT APM platform connects IoT sensors and cameras to an AI analytics engine that continuously analyses equipment health data. Edge agents collect and normalise data from every connected device. AI models detect anomalies, classify fault types, calculate failure probability and remaining useful life, and trigger automated responses — work orders, alerts, or corrective workflows — based on configurable policy rules. Every action is logged in an auditable trail.

Question 4

What are the key business benefits of AI APM software?

Accepted Answer

The key business benefits include: 30–50% reduction in unplanned downtime, 10–25% lower maintenance costs through condition-based scheduling, 20–40% extension of asset service life, faster incident response through automated alerting and agentic AI workflows, enterprise-wide operational visibility from a single dashboard, and audit-ready compliance documentation generated automatically. Most organisations achieve ROI within 6–18 months of deployment.

Question 5

Who should use AI APM software?

Accepted Answer

AI APM software is most valuable for organisations that: operate large numbers of physical assets (machinery, equipment, pipelines, or infrastructure); have experienced significant unplanned downtime or high maintenance costs; rely on ageing equipment where failure risk is increasing; need to demonstrate compliance with inspection and maintenance standards; or are seeking to reduce maintenance headcount without reducing coverage or safety standards.

Question 6

How is PETRAN APM different from a CMMS or EAM?

Accepted Answer

A CMMS or EAM is a record-keeping and work order management system — it stores maintenance history and schedules planned tasks. PETRAN APM is a real-time intelligence layer above the CMMS: it continuously monitors asset health with live IoT data, predicts failures with AI, and automatically creates work orders in your existing CMMS (such as IBM Maximo or SAP EAM) with diagnostic evidence attached. APM makes your CMMS proactive, not reactive.

Question 7

How does IoT predictive maintenance reduce equipment downtime?

Accepted Answer

IoT predictive maintenance connects vibration sensors, temperature probes, and current monitors to an AI analytics engine that continuously analyses equipment health. The AI detects developing faults — bearing wear, thermal anomalies, resonance shifts — days or weeks before failure. It calculates remaining useful life and failure probability, then triggers a maintenance work order at the optimal time, preventing unplanned downtime while avoiding unnecessary early intervention.

Question 8

Which IoT data sources and protocols does PETRAN support?

Accepted Answer

PETRAN supports all major industrial IoT protocols: MQTT, OPC UA, Modbus/TCP, BACnet/IP, DNP3, IEC 61850, RTSP/ONVIF, and REST/HTTP. Compatible hardware includes vibration sensors, temperature and pressure transducers, current monitors, flow meters, fixed and mobile IP cameras, thermal cameras, RTLS location tags, and access control systems. Edge agents auto-discover connected devices and normalise data across all sources.

Question 9

Does PETRAN support computer vision and AI camera analytics?

Accepted Answer

Yes. PETRAN includes pre-built computer vision skills for defect detection, foreign object detection, spill and leak identification, occupancy analysis, PPE compliance monitoring, and zone boundary enforcement. Visual AI runs on fixed cameras, thermal cameras, hyperspectral imagers, drone feeds, and mobile devices. Custom AI models can be deployed without ML engineering expertise through PETRAN's model management tools.

Question 10

What is remaining useful life (RUL) prediction and how does PETRAN calculate it?

Accepted Answer

Remaining useful life (RUL) is an AI-calculated estimate of how much longer an asset can operate before it requires maintenance or replacement. PETRAN calculates RUL by continuously analysing multi-signal IoT data — vibration spectra, thermal patterns, current draw, and operational load — against degradation models trained on the specific asset's historical behaviour. As the asset approaches failure, RUL estimates shorten, triggering escalating alerts and automated work order creation.

Question 11

How does agentic AI work in PETRAN?

Accepted Answer

Agentic AI in PETRAN refers to autonomous AI agents that continuously monitor detected events and decide the appropriate response without human instruction. Each agent operates against configurable policy rules — alert thresholds, severity classifications, approved corrective actions — and executes pre-approved workflows automatically. For routine events, agents act immediately. For high-consequence decisions, human-in-the-loop controls ensure governance and accountability before action is taken.

Question 12

Can PETRAN run at the edge without cloud connectivity?

Accepted Answer

Yes. PETRAN's edge agents run full AI inference locally on edge hardware — industrial PCs, ruggedised appliances, or NVIDIA Jetson devices — without cloud connectivity. This provides sub-second alert latency, local data privacy, and continuous monitoring even during network outages. Edge data is buffered locally and synchronised with the central cloud hub when connectivity is restored. This makes PETRAN suitable for offshore platforms, remote pipelines, and air-gapped networks.

Question 13

Is on-premises deployment available for PETRAN?

Accepted Answer

Yes. PETRAN is available in three deployment configurations: SaaS (managed cloud), Private Cloud / On-Premises (customer-controlled infrastructure), and Hybrid (edge AI with cloud orchestration). On-premises deployment supports air-gapped networks, strict data sovereignty requirements, and regulated-industry compliance standards including IEC 62443 and ISO 27001. Containerised deployment via Docker and Kubernetes is supported.

Question 14

How does PETRAN integrate with IBM Maximo and SAP EAM?

Accepted Answer

PETRAN has pre-built bidirectional connectors for IBM Maximo, SAP Plant Maintenance/EAM, Hexagon EAM, Infor EAM, and other major CMMS/EAM platforms. When PETRAN detects a developing fault or inspection defect, it automatically creates a work order in the connected system, attaches sensor evidence and AI diagnostics, and recommends the corrective action. This eliminates manual data re-entry and accelerates maintenance response time from hours to minutes.

Question 15

How does PETRAN support infrastructure inspection management?

Accepted Answer

PETRAN provides structured digital inspection workflows for bridges, pipelines, roads, and utility assets. Field teams use mobile apps with guided checklists, AI-assisted defect classification, photo capture, and GPS tagging. All records are stored in an immutable, auditable history log. AI computer vision detects cracks, corrosion, and surface defects automatically, reducing manual review time and supporting ISO 55000 lifecycle management compliance.

Question 16

Can PETRAN monitor active construction sites?

Accepted Answer

Yes. PETRAN supports construction monitoring with real-time structural health sensors (concrete curing temperature, formwork stress, ground settlement), AI safety compliance monitoring (PPE detection, zone enforcement, vehicle proximity), equipment utilisation tracking, and digital quality inspection workflows. Construction managers access a live dashboard showing site safety status, structural health, and inspection compliance from a single interface.

Question 17

Which KPIs does PETRAN improve?

Accepted Answer

PETRAN directly improves: Overall Equipment Effectiveness (OEE), Mean Time Between Failures (MTBF), Mean Time to Repair (MTTR), unplanned downtime hours, planned maintenance ratio, preventive-to-reactive maintenance ratio, asset availability percentage, inspection compliance rate, First-Pass Yield (manufacturing), worker safety incident rate, and energy consumption per unit of production. KPI dashboards are pre-configured in the platform for immediate visibility.

Question 18

What ROI timelines are typical for AI APM implementations?

Accepted Answer

Industry benchmarks from Gartner, McKinsey, and Deloitte indicate that AI-enabled predictive maintenance reduces unplanned downtime by 30–50%, lowers maintenance costs by 10–25%, and extends equipment service life by 20–40%. Most PETRAN deployments achieve initial ROI within 6–18 months, driven by avoided breakdown costs, reduced overtime labour, and optimised spare parts inventory. Full 5-year NPV typically ranges from 3–5× initial investment.

Question 19

How long does PETRAN implementation take?

Accepted Answer

A standard PETRAN deployment — connecting sensors, configuring edge agents, onboarding users, and activating pre-built AI skills for common scenarios — typically takes 2–4 weeks for a single site. Multi-site enterprise deployments are typically complete within 6–12 weeks. Ombrulla provides implementation services, site surveys, sensor commissioning support, and training for operations and maintenance teams throughout the process.

Question 20

How much data does PETRAN need to start predicting failures?

Accepted Answer

PETRAN's pre-built AI skills for common equipment types (pumps, motors, compressors, conveyor drives, HVAC systems) begin generating predictions from Day 1 using foundation models trained on large industrial datasets. Custom AI models for specific equipment require 30–90 days of historical sensor data to establish behaviour baselines. For assets with limited historical data, PETRAN's anomaly detection capabilities provide value immediately while custom models build accuracy over time.

Question 21

Does PETRAN work offline in areas with no network connectivity?

Accepted Answer

Yes. PETRAN's edge agents are designed for offline-first operation. They collect, normalise, and buffer sensor data locally, run AI inference on the edge device, and generate alerts and actions without any cloud connectivity. When network connectivity is restored, buffered data synchronises with the central cloud hub. This capability is essential for remote industrial sites, offshore platforms, underground assets, and air-gapped networks.

Question 22

How are alerts and automated workflows managed in PETRAN?

Accepted Answer

PETRAN's alert and workflow engine is fully configurable per asset, sensor, defect type, or site. Operators set detection thresholds, severity classifications, and response actions for each scenario. Alerts are delivered via SMS, email, dashboard notification, PLC digital output, or webhook. Automated workflows can raise CMMS work orders, send escalation notifications, trigger line-stop signals, or initiate emergency shutdown sequences — all within configurable governance rules.

Question 23

How is security handled in PETRAN?

Accepted Answer

PETRAN implements enterprise-grade security across all deployment modes: AES-256 data encryption at rest and in transit, SSO via SAML 2.0 and OIDC, SCIM user provisioning, multi-factor authentication, role-based access control (RBAC) with granular site-level permissions, and full audit logging of all user and system actions. For on-premises and hybrid deployments, network segmentation and air-gap support are available. The platform aligns with IEC 62443 industrial cybersecurity requirements.

Question 24

Who owns the data and AI models in PETRAN?

Accepted Answer

All operational data collected by PETRAN and all AI models trained on your site-specific data remain the property of your organisation. Ombrulla does not claim ownership of customer operational data or derived models. Data is stored in your designated region (UK, EU, US, or customer-specified). Customers can export their data at any time through standard API or bulk export tools. Model portability is supported for enterprise and custom deployment configurations.

Question 25

Can PETRAN scale across multiple plants and geographies?

Accepted Answer

Yes. PETRAN's multi-site, multi-tenant architecture is designed for enterprise-wide deployment across multiple plants, depots, construction projects, or infrastructure assets in different regions or countries. Partitioned data architecture ensures each site's data is isolated. Role-based access control allows corporate, regional, and site-level permission structures. Cross-site analytics in the central dashboard enable performance benchmarking and best-practice replication across the organisation.

Question 26

Does PETRAN support condition-based and risk-based maintenance strategies?

Accepted Answer

Yes. PETRAN supports the full spectrum of maintenance strategies: reactive (alert on failure), preventive (scheduled), condition-based (act when sensor data indicates need), predictive (act on AI failure probability score), and risk-based (prioritise by criticality and consequence of failure). Most organisations implement PETRAN to transition from reactive/preventive to condition-based and predictive strategies, guided by PETRAN's built-in maintenance strategy optimisation recommendations.

Question 27

What kinds of assets does PETRAN monitor?

Accepted Answer

PETRAN monitors any asset that can be connected to IoT sensors or cameras, including: rotating equipment (pumps, motors, compressors, turbines, fans, conveyor drives), static equipment (tanks, vessels, heat exchangers, pipelines), civil infrastructure (bridges, roads, tunnels, dams), facilities (HVAC, utilities, lighting, access control), construction assets (cranes, formwork, concrete pours), and worker safety compliance across all environments.

Question 28

Can PETRAN help with energy optimisation and sustainability reporting?

Accepted Answer

Yes. PETRAN's facility intelligence module monitors real-time energy consumption across HVAC, compressed air, utilities, and production processes. AI analytics identify energy waste patterns, anomalous consumption, and optimisation opportunities. The platform generates sustainability reports aligned to ISO 50001 energy management standards and supports ESG reporting requirements. Energy KPIs are available on the central dashboard alongside operational and maintenance metrics.

Question 29

Is mobile access available for PETRAN?

Accepted Answer

Yes. PETRAN provides native mobile applications for iOS and Android that give field engineers, inspectors, and maintenance technicians full access to monitoring dashboards, alert notifications, digital inspection workflows, and work order management from any device. Mobile apps work offline with full inspection and data capture capability, synchronising with the central platform when connectivity is available. Guided SOPs and AI-assisted defect capture are optimised for mobile field use.

Question 30

How does PETRAN handle compliance and governance requirements?

Accepted Answer

PETRAN includes a dedicated governance layer that covers: versioned AI models with deployment approval workflows, policy rule management with change control, human-in-the-loop approval gates for high-consequence automated actions, encrypted and tamper-evident audit trails, data lineage tracking, and role-based access control. These capabilities support compliance with ISO 55000, ISO 45001, IEC 62443, OSHA, PSSR, and customer-specific quality and safety standards.

Question 31

Can PETRAN detect safety and compliance issues in real time?

Accepted Answer

Yes. PETRAN's worker safety monitoring module uses AI computer vision to detect PPE non-compliance, unsafe postures and motions, unauthorised zone entry, line-of-fire situations, lone-worker isolation, and man-down events in real time. Alerts are triggered instantly to supervisors and safety managers. All safety events are logged with video evidence in an auditable trail that supports HSE reporting, incident investigation, and safety performance benchmarking.

Question 32

How do we get started with PETRAN?

Accepted Answer

Getting started with PETRAN begins with a discovery call with Ombrulla's solutions team — a 45-minute session to understand your top asset performance challenges, existing systems, and deployment environment. Ombrulla then provides a tailored demonstration, a solution design proposal, and an ROI model specific to your organisation. Typical time from first conversation to deployed solution is 4–8 weeks for a pilot site. Contact Ombrulla at ombrulla.com/contact or book directly via the demo button on this page.

Dimension	Legacy IoT Monitoring	CMMS / EAM System	PETRAN AI APM Platform
Defect / Fault Detection	Threshold rules: manual configuration per asset; breaks on process variation	Passive: records faults after they are logged by operators	AI learns asset behaviour baselines; detects subtle developing faults weeks before failure
Failure Prediction	None: point-in-time readings only	None: historical records, not prediction	Failure probability, RUL, days-to-failure calculated continuously for every asset
Work Order Creation	Manual: operator must recognise fault and raise ticket	Scheduled: calendar-based, not condition-based	Automatic: AI raises CMMS/EAM work order with diagnostic evidence and recommended action
Inspection Management	Not available in most platforms	Limited: checklist records, no AI or mobile workflow	Structured mobile workflows, AI defect classification, GPS tagging, immutable audit trail
Construction Monitoring	Not available	Not available	Real-time structural health, AI safety compliance, equipment tracking, quality checklists
Integration Openness	Proprietary protocols; limited vendor ecosystem; lock-in common	Open: wide CMMS ecosystem; standardised	Open protocols; pre-built connectors for Maximo, SAP, Hexagon, OSIsoft, Azure, AWS
Deployment Options	Cloud or on-prem only	Cloud, on-prem, or hosted; no edge AI	SaaS, private cloud/on-prem, and hybrid edge+cloud; offline-first edge agents
AI / ML Capability	Basic anomaly detection (if any); no learning loop	None native; requires bolt-on tools	Agentic AI; continuously trained on site-specific data; compounding accuracy over time

AI & IoT Asset Performance Management Platform

What Is PETRAN?

Business Impact Metrics

Reduction in unplanned downtime

Lower maintenance cost

Extended asset service life

Alert accuracy for critical faults

What Can PETRAN Do?

Real-Time IoT Monitoring & Alerts

Why Choose PETRAN for Asset Performance Management?

Eliminate Unplanned Downtime

Lower Total Maintenance Cost

Autonomous Incident Response

Enterprise-Wide Visibility from One Platform

Faster Time to Value

No Vendor Lock-In

Audit-Ready Compliance

Mobile & Field Ready

PETRAN Platform Capabilities

Visual AI for Cameras

Predictive Maintenance Engine

IoT Real-Time Monitoring

Digital Inspection Management

Construction Site Monitoring

Worker Safety Monitoring

Facility Intelligence

Agentic AI Automation

How does PETRAN Works

PETRAN Integrations — Connect Your Existing Systems

CMMS / EAM

IoT Protocols

Video / Vision

Cloud Platforms

Data & Analytics

IT / Operations

Identity & Security

File & API

Flexible Deployment

SaaS (Secure Cloud)

Private Cloud / On-Premises

Hybrid Edge + Cloud