Condition Monitoring · MINING · MANUFACTURING · OIL & GAS · POWER UTILITIES
Condition monitoring that starts with the bad actors and scales from there.
Most condition-monitoring programmes try to instrument everything at once and stall. The XMPro AO Platform takes a sharper path: prioritise the critical bad actors first, monitor them with a hybrid of engineering models and machine learning, then expand using configurable templates so each new asset class gets to value in weeks, not quarters.
What's getting in the way today.
Industrial condition-monitoring programmes share a common failure pattern: too broad, too late, too disconnected. Four pressures compound:
Where to start
Asset bases are huge and instrumentation budgets are not — without a way to rank critical bad actors, condition-monitoring programmes scatter their focus.
Pure-AI vs. pure-engineering
Black-box ML without engineering context produces false positives; rule-based monitoring alone misses subtle degradation patterns. The right answer is hybrid.
Slow time-to-value
Bespoke models per asset class drag deployment timelines, so condition-monitoring stays a pilot rather than scaling into production.
Insight without action
Detection without ranked, actionable recommendations leaves operators interpreting raw signals on their own.
Condition Monitoring — how it works.
A three-step path — prioritise bad actors first, monitor them with a hybrid model-based approach, then scale using pre-configured asset templates so condition-monitoring runs as a programme, not a pilot.
The platform integrates sensor data from IoT devices, operational systems and external sources continuously across the chosen asset base. A hybrid model-based approach pairs first-principles engineering models with machine-learning models for each asset class — catching both known failure modes and subtle, multi-variable degradation patterns. Pre-built templates accelerate deployment for common asset classes (conveyors, pumps, compressors, motors), and prescriptive recommendations combine business rules with AI logic to deliver ranked next actions when an event fires. Actions are monitored against their outcomes so models and rules improve continuously.
*Illustrative dashboards from the platform. Layout, signals and decision points are scoped per site.
Scope this for your operation.
Tell us about your fleet, your control maturity and the lever that matters most. We’ll map this use case to your starting point.
What this looks like in operation.
Focus before scale
Critical bad actors get attention first, so the programme delivers measurable value before broadening to the full asset base.
Hybrid models, fewer false positives
Engineering principles plus machine learning reduce alarm noise compared to either approach alone.
Templates over bespoke builds
Reusable asset-class templates compress deployment from quarters to weeks for each new fleet.
Built for these industries.
Other solutions you might explore.
Not a concept. In production.
XMPro is deployed at Tier 1 global operators across asset-intensive and mission-critical industries — delivering measurable results across predictive maintenance, process optimisation and operational intelligence.
Now pushing the frontier.
MAGS agents are achieving what no other industrial platform has demonstrated — sustained autonomous operations at enterprise scale.
