Our Services
Intelligence derived from data your mine already has.
Predictive Failure Analytics
We analyse your existing SCADA historian, CMMS work order history and operational data to build machine learning models that predict pump and motor failures 2 to 8 weeks before they occur. Your maintenance team receives clear actionable alerts telling them which asset needs attention, what failure is developing, and how much time they have to act.
What is included
- check_circle Data extraction and audit from your existing historian and CMMS
- check_circle Feature engineering specific to your assets and failure history
- check_circle ML model development trained on your specific equipment
- check_circle Validation against historical failures
- check_circle Alert delivery in a format your planners can act on immediately
- check_circle Initial deployment and handover to your maintenance team
Who it is for
Mining operations experiencing recurring unplanned pump and motor failures who want to convert emergency repairs into planned interventions.
Failure modes detected
Detection windows
- schedule Bearing failures — 4 to 8 weeks
- schedule Seal deterioration — 2 to 4 weeks
- schedule Impeller wear — 6 to 12 weeks
- schedule Winding insulation — 4 to 16 weeks
- schedule Rotor bar defects — 6 to 12 weeks
ML Model Development
Every mine is different. Your equipment, your failure history, your operational data and your process conditions are unique. Off-the-shelf platforms apply generic models built on other industries' data. OreNet builds custom machine learning models trained exclusively on your assets, your failures and your operational environment — producing predictions that are specific, validated and trusted by your team.
What is included
- check_circle Full data discovery and asset assessment
- check_circle Historical failure analysis using CMMS work order data
- check_circle Custom feature engineering for your specific equipment
- check_circle Model selection, training and validation
- check_circle Performance reporting — precision, recall, lead time achieved
- check_circle Model documentation and knowledge transfer
- check_circle Retraining schedule and ongoing model management plan
Who it is for
Mines with existing data infrastructure seeking a bespoke ML capability built around their specific assets and operational context.
Data sources supported
Asset Health Monitoring
Predictive analytics is not a once-off exercise. Equipment condition changes continuously, models require ongoing validation and new failure patterns emerge over time. OreNet's Asset Health Monitoring retainer keeps your critical pump and motor assets under continuous ML surveillance — with monthly health reports, real-time alerts on developing failures and regular model updates delivered to your maintenance team.
What is included
- check_circle Continuous ML monitoring of agreed critical assets
- check_circle Monthly asset health report covering condition status, trend analysis and upcoming risks
- check_circle Early warning alerts when developing failures are detected
- check_circle Quarterly model review and retraining with new failure data
- check_circle Direct access to OreNet team for alert interpretation and maintenance planning support
- check_circle Annual performance review covering failures prevented, lead times achieved and model accuracy
Who it is for
Mining operations who have completed an initial predictive analytics engagement and want ongoing intelligence without building an internal data science capability.
Why a retainer
A model trained today will drift as equipment ages, processes change and new assets come online. Ongoing monitoring ensures your predictions remain accurate, your alerts remain trusted and your maintenance team continues to benefit from improving model performance over time.
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