Solutions — Asset Reliability & Availability
Every unplanned outage is a decision you didn't get to make.
Unplanned downtime is not an asset problem. It's an information problem. Assets degrade on a curve — the fault that trips a conveyor, trips a substation, or forces a pipeline shutdown was visible weeks or months before the failure event. The information just wasn't captured, processed, or acted on in time.
Unleash Live closes that gap. Continuous visual monitoring via Prism and structured autonomous inspection via Orb put condition intelligence into operations teams' hands before degradation becomes disruption.
The metrics this moves
| KPI | What changes | How |
|---|---|---|
| Unplanned Downtime Hours | Reduced | Anomaly detection upstream of failure threshold events |
| OEE (Overall Equipment Effectiveness) | Improved | Higher asset availability through earlier intervention |
| MTBF (Mean Time Between Failures) | Extended | Condition-led maintenance replaces time-based schedules |
| MTTR (Mean Time To Repair) | Reduced | Visual evidence and fault classification accelerate diagnosis |
| Asset Availability Rate | Improved | Fewer reactive shutdowns, more planned maintenance windows |
Where the problem lives across your operations
Conveyors and process plant
Belt mistracking, idler degradation, transfer point blockage, and carryback accumulation are the leading causes of unplanned conveyor shutdowns. These conditions are visible before they become failures. Prism's Conveyor Belt Analytics monitors continuously — generating pre-trip warnings for belt centreline deviation, thermal signatures, and material spillage before BSS threshold events are reached. Outputs integrate directly into SCADA historians as additional process tags.
Transmission and distribution infrastructure
Conductor faults, insulator degradation, and vegetation encroachment don't announce themselves. They accumulate. Orb's automated aerial inspection finds faults at the degradation stage, not the failure stage, with AI detection at 91% F1-Score across the full network asset portfolio. Operators that have deployed report a 300% increase in defect identification in year one versus prior helicopter-based programs.
Process facilities and high-consequence zones
Fixed cameras via Prism monitor substations, switchgear, tank farms, and process plant continuously, detecting anomalies in real time and routing alerts with visual evidence into operations teams and maintenance scheduling systems. No manual patrol required. No detection gap between inspection cycles.
How it works
Continuous monitoring (Prism)
Fixed cameras stream visual data into the Unleash Live platform from conveyors, substations, process plant, and high-consequence zones. Trained AI detection models run continuously against the feed, identifying anomaly conditions and generating alerts before failure thresholds are reached. Outputs route into SCADA, Teams, email, and maintenance scheduling systems with visual evidence attached. No additional headcount required.
Structured inspection (Orb)
Autofly executes autonomous, repeatable inspection missions across distributed infrastructure — T&D networks, pipeline corridors, mine site assets. AI models process captured visual data and classify faults by severity. Risk-prioritized outputs surface in Cloud Insights and integrate into asset management and work scheduling systems via API. Critical faults identified in under 24 hours from capture.
From detection to action
Every anomaly or fault finding includes the originating visual evidence, classification, severity score, and recommended action. Findings route directly into work order creation and maintenance scheduling, reducing the time from detection to remediation decision.
Proven in deployment
| Outcome | Result | Context |
|---|---|---|
| Defect identification rate | 300% increase in year one | vs. helicopter-based inspection across a utility T&D network |
| AI detection performance | 91% F1-Score | Precision and recall across fault classification at network scale |
| Fault detection speed | Critical faults identified in under 24 hours | Overnight processing and next-day risk-prioritized output |
| Inspection cycle time | 40–60% reduction | vs. manual inspection methods across comparable network assets |
| Truck rolls | 10–15% reduction | Fewer repeat site visits from higher-quality capture and earlier fault identification |
Who this is for
COO / VP Operations
Accountable for OEE, availability, and unplanned downtime hours. This is the conversation about moving from reactive to predictive operations without adding headcount or changing operational systems.
Asset Manager / Reliability Engineer
Accountable for MTBF, MTTR, and maintenance cost ratios. This is the conversation about condition-led maintenance intelligence replacing time-based inspection schedules.
Head of Maintenance
Accountable for work order backlog and planned vs. unplanned maintenance ratio. This is the conversation about getting earlier, better fault data into the maintenance scheduling system.
The structural difference
Most monitoring deployments generate alerts. Alert fatigue is already a documented problem in industrial operations — too many signals, too little context, too slow a path from detection to action. Unleash Live is built around the full detection-to-decision cycle: AI classification with evidence attached, risk prioritization, and direct integration into the systems maintenance and operations teams already use. The output is not a dashboard. It is a work order trigger.
Request a Technical Briefing
See how your current downtime profile and inspection program compare against deployed Unleash live operators.
