Programme 07 · Predictive Analytics
A specialist programme that takes engineers from equipment data to governed maintenance decisions. Each stream applies the same decision chain – data, detection, prognosis and advice – to the assets and failure modes of its industry and ends with an asset-specific capstone or case study.
What you will learn
Each stream has six modules, from reliability foundations to a practical case study or capstone. Choose the stream that matches your operation.
Streams
Stream
Mining fleet and heavy equipment data, failure modes, anomaly detection, prognosis and governed maintenance decisions.
Critical equipment, operating context, failure modes and maintenance decision points.
Haul truck and mobile equipment health signals; conveyor, drive, motor and rotating equipment indicators.
Fleet management, CMMS and EAM, sensors, operating hours, load profiles, data quality and integration.
Trend analysis, anomaly detection, failure prediction, model validation and alert thresholds.
Turning equipment-health insight into maintenance plans, work orders, spares and downtime reduction.
Build an equipment-health use case with measurable operational and reliability KPIs.
Career Pathway (Illustrative): Mining Technician → Predictive Maintenance Specialist → Asset Performance Lead
Stream
The ISO 13374 decision chain applied to transformers and rotating plant: data quality, detection, prognosis, governance and maintenance advice.
Maintenance strategies, use-case definition, failure modes and reliability engineering.
Transformer dissolved-gas analysis, partial discharge, thermography, rotating plant vibration and operating signals.
CMMS and EAM, SCADA and historian integration, time alignment, data quality, lineage and governance.
Statistics, time series, baselines, anomaly detection, supervised models and validation.
ISO 13374 functional blocks, ISO 14224 taxonomy, ISO 13381-1 prognostics, ISO 17359 general guidelines and maintenance advice.
False-alarm assessment, model monitoring, cybersecurity, business case and an asset-specific capstone.
Career Pathway (Illustrative): Reliability Engineer → Predictive Maintenance Specialist → Analytics Lead
Stream
Production and packaging line predictive analytics: line data, failure modes, anomaly detection, prognosis and maintenance decisions that reduce downtime.
Production and packaging line equipment; operating context and failure modes; changeover and hygiene constraints; the maintenance decision points that protect line availability.
Vibration, temperature and motor current on drives, gearboxes, conveyors, fillers and pumps; compressed air, refrigeration and steam utilities; PLC and SCADA signals; stoppage and reject records.
CMMS and EAM, MES and line-stoppage records, historian integration, alignment of machine signals with production events, data quality and governance.
Baselines by product and line speed, anomaly detection on cyclic machine signals, minor-stoppage pattern analysis, failure prediction, model validation and alert thresholds.
OEE and availability-loss accounting (ISO 22400); ISO 14224 taxonomy; ISO 13374 data processing; maintenance advice timed to changeovers and planned stops.
False-alarm assessment; model monitoring; a business case built on downtime and waste cost; a line-specific capstone with measurable availability and quality KPIs.
Career Pathway (Illustrative): Production / Maintenance Technician → Predictive Maintenance Specialist → Asset Performance Lead
Learning outcomes
Who should attend
Reliability, maintenance and asset management engineers, technicians and data practitioners in mining, power generation and FMCG operations.
Ask the Academy team about the background knowledge recommended for this programme.
Assessment and recognition
Optimal Academy certificate of completion.
Assessment uses knowledge checks and practical activities or simulations, with assignments or portfolio evidence where the course requires them. Every learner receives individual feedback.
Enrolment dates
Contact the Academy team for the next intake, enrolment closing date, session timetable and a written quotation.
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