R&D Division
Digital Twin Technology & Advanced Compliance
Real-time digital twin representations, AR-assisted inspection, predictive maintenance modelling, and automated compliance workflows transforming fire system lifecycle management.
Research Overview
The Digital Twin Technology division develops real-time virtual representations of building fire systems that mirror the state of physical assets with sub-second fidelity. By integrating live sensor telemetry from fire panels, IoT environmental monitors, and BMS data feeds, our digital twins provide unprecedented situational awareness for facility managers and emergency responders.
Augmented reality (AR) assisted field inspection overlays fire system schematics, maintenance history, and compliance status directly onto the physical environment through technician headsets — dramatically reducing inspection time and human error rates. BIM (Building Information Modelling) integration ensures the digital twin remains synchronised with the as-built architectural model throughout the building lifecycle.
Our compliance automation research applies machine learning to AS 1851 scheduling optimisation, routing technicians efficiently across large property portfolios while generating automated inspection reports and regulatory submissions. Predictive maintenance models trained on sensor telemetry and component failure history enable condition-based maintenance strategies that preempt system failures.
Technical Methodology
From BIM integration through automated compliance reporting.
BIM Integration & Data Ingestion
Building Information Models (BIM) are ingested and enriched with fire system asset data — panel locations, detector types, sprinkler coverage, cable routes — creating a comprehensive digital foundation for twin construction.
Digital Twin Construction & Sensor Binding
Real-time digital twin representations are built by binding live sensor telemetry from fire panels, IoT environmental sensors, and BMS data points to their corresponding digital model elements.
Predictive Maintenance Modelling
Machine learning models trained on sensor telemetry and historical failure data predict component degradation, enabling condition-based maintenance that preempts failures before they impact system availability.
AR-Assisted Field Inspection
Augmented reality overlays project fire system schematics, maintenance history, and compliance status onto field technician headsets during inspections, reducing human error and accelerating assessment cycles.
Automated Compliance & Reporting
ML-optimised technician routing for AS 1851 compliance scheduling, automated report generation from inspection data, and regulatory submission workflows that eliminate manual documentation bottlenecks.
Key Research Outcomes
Core capabilities from our digital twin and compliance research programme.
Real-Time Digital Twins
Live digital representations of building fire systems reflecting current sensor states, alarm conditions, and system health with sub-second synchronisation latency.
AR Field Inspection
Augmented reality interfaces overlaying fire system schematics, maintenance records, and compliance data onto the physical environment during technician inspections.
Predictive Maintenance
ML-driven failure prediction models using sensor telemetry time-series analysis and component lifecycle data to schedule maintenance before degradation causes faults.
Compliance Automation
Automated AS 1851 scheduling, ML-optimised technician routing, digital report generation, and regulatory submission workflows eliminating manual compliance overhead.
Standards & Publications Referenced
BIM, asset management, and compliance standards underpinning our digital twin research.
- AS 1851 — Routine service of fire protection systems and equipment
- ISO 19650 — Organization and digitization of information about buildings and civil engineering works using BIM
- IFC (Industry Foundation Classes) — buildingSMART International
- ISO 23247 — Digital twin framework for manufacturing
- NCC / BCA — National Construction Code maintenance provisions
- ASHRAE 223P — Designation and Classification of Refrigerants (BACnet for digital twins)
- ISO 55000 — Asset management
Explore Digital Twin R&D
Ready to transform fire system management with digital twin technology, predictive maintenance, or compliance automation? Connect with our team.
Explore Digital Twin R&D