AI-enabled reliability intelligence for heavy mining and industrial assets
AuraMetrics helps heavy mining and industrial teams detect emerging degradation, investigate equipment events and identify likely causes using the telemetry their assets already produce.
No new hardware first. Vendor-neutral. Read-only. Advisory-only. Engineers remain in control.
Developed using more than 17.9 million governed telemetry records from an operational Eickhoff shearer.
The operational problem
Heavy industrial assets generate telemetry, alarms and event records across changing loads and operating conditions. Important warning signals can remain hidden by disconnected systems, persistent alarms, sensor problems and normal operational variation.
AuraMetrics creates a governed reliability layer over this information, helping teams separate genuine degradation from context, investigate failures faster and build a defensible path toward earlier intervention.
The objective is not more alarms. It is better evidence at the right time.
One foundation, two solutions
Begin with historical evidence. Progress toward monitoring only when the data, validation and operating environment support it.
Understand what happened - and build the evidence to explain why.
Analyse historical telemetry, events and operating context to reconstruct asset behaviour and rank plausible contributing causes.
Identify emerging degradation early enough to support better decisions.
Extend the governed foundation into validated health monitoring and evidence-supported advisory alerts.
A clear path
A five-stage journey keeps the engagement understandable, controlled and tied to the decision it must support.
Define the reliability question and test data readiness.
Use controlled exports, historical databases or approved read-only sources.
Map vendor-specific signals into a governed asset model.
Separate operating context, data quality and emerging degradation.
Present ranked evidence, limitations and a human-reviewed next step.
Mining-led. Industrially extensible.
Designed for high-value machinery where failures are costly, operating conditions vary and useful reliability evidence is difficult to extract manually.
Applicability is confirmed through assessment of the asset, reliability question and available data.
Underground and surface extraction, haulage, drilling and mobile equipment.
Load, vibration, current, temperature, pressure and process behaviour.
Drive, belt, transfer-point, load and operating-condition problems.
Interacting electrical, mechanical and hydraulic signals.
Fixed or mobile machinery with usable operating, condition, alarm or event data.
Initial industrial proof case
AuraMetrics developed and evaluated its initial reliability framework using an operational 15-year-old Eickhoff longwall shearer.
Initial proof case. Performance and applicability are validated separately for every asset and data environment.
Why AuraMetrics
Signals are interpreted in relation to load, machine state and process conditions.
Findings include contributing signals, context, candidate causes and stated limitations.
Obtain value from historical data before committing to live monitoring.
Forensic and predictive workflows share the same evidence spine.
Machine-specific signals map into a reusable asset structure.
AuraMetrics advises; authorised personnel decide and act.
Practical first step
Identify priority reliability use cases and determine whether your telemetry is ready to support credible forensic or predictive work.
No new-hardware commitment. No obligation to deploy live monitoring.
