Reliability intelligence built for engineering trust

Industrial AI Should Make Evidence Clearer - Not Decisions More Opaque

AuraMetrics combines disciplined data engineering, asset context and explainable analysis to help heavy mining and industrial teams make better-informed maintenance decisions.

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What makes AuraMetrics different

Engineering Trust Is a Design Requirement

Operating-context aware

Signals are interpreted in relation to load, machine state and process conditions.

Explainable by design

Findings include contributing signals, context, candidate causes and stated limitations.

Forensic first

Obtain value from historical data before committing to live monitoring.

One governed foundation

Forensic and predictive workflows share the same evidence spine.

Vendor-neutral architecture

Machine-specific signals map into a reusable asset structure.

Engineers remain in control

AuraMetrics advises; authorised personnel decide and act.

Practical distinction

More Than Conventional Monitoring

Conventional approachAuraMetrics approach

Fixed limits across changing conditionsLoad- and operating-context-aware analysis

Machine-specific data silosGoverned, vendor-neutral asset model

Alarm shows something changedEvidence helps explain what changed and why

Sensor faults can resemble asset faultsData-quality diagnostics form part of the analysis

Separate forensic and monitoring toolsOne foundation supports both workflows

Results may be difficult to challengeTraceable evidence and stated limitations

Evidence hierarchy

Clear About What the Evidence Supports

AuraMetrics distinguishes data-quality observation, descriptive evidence, forensic diagnosis and predictive advisory. Forensic Intelligence may be the correct long-term answer; not every client must progress to monitoring.

Data-quality observation

Descriptive evidence

Forensic diagnosis

Predictive advisory

Initial industrial proof case

Developed Against the Complexity of a Real Mining Asset

AuraMetrics developed and evaluated its initial reliability framework using an operational 15-year-old longwall shearer.

17.9M+governed telemetry records

30 minpredictive horizon evaluated

Controlledforensic, predictive and health-monitor progression

Initial proof case. Performance and applicability are validated separately for every asset and data environment.

Business value

Better Evidence Supports Better Maintenance Decisions

Reduce investigation time

Bring telemetry, events and context into a structured investigative view.

Improve prioritisation

Focus attention on the assets and conditions with the strongest evidence.

Surface monitoring gaps

Identify chronic sensor problems, missing context and weak alarm coverage.

Reduce deployment risk

Validate data, use cases and predictive performance before rollout.

Build reusable capability

Create governed mappings, evidence workflows and model packs.

Strengthen knowledge

Capture confirmed findings and corrections for future work.

Industry benchmark percentages belong in a sourced business case - not as AuraMetrics performance promises.

Assessment value

A Stronger Basis for Investment Decisions

Identify priority reliability problems

Evaluate data and telemetry readiness

Select credible forensic or predictive opportunities

Decide whether further investment is justified

Practical first step

Trust Starts with Evidence

Before recommending a model, integration or monitoring program, AuraMetrics examines the reliability question and quality of the supporting evidence.

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No new-hardware commitment. No obligation to deploy live monitoring.