Maintenance has shifted from fixing what breaks to anticipating what will break. Sensors, historians and maintenance management systems now generate more data than reliability teams can review by eye, and the opportunity lies in turning that data into early, trustworthy warnings. Organisations that do this well reduce emergency repairs, protect safety and plan spares and labour more effectively.

This ten-day programme builds the full capability, beginning with reliability engineering fundamentals and the data foundations that predictive models require. Participants learn how to assemble and clean time series from sensors and work orders, define failure events and labels, and engineer features from vibration spectra, thermal and electrical signals. They then train and compare classification, anomaly detection, survival and remaining useful life models, paying close attention to imbalanced data, leakage and validation by asset rather than by random split. The later days address interpretability, alert thresholds, cost-benefit analysis, integration with a CMMS, deployment pipelines and monitoring for drift.

Instruction is laboratory-based, using Python notebooks and open industrial datasets. In the final days participants work in teams on a capstone that moves from problem framing to a deployed model and a rollout proposal for a management audience.

By the end of the programme, participants will be able to:

• Compare reactive, preventive, condition-based and predictive maintenance strategies and select the right one.
• Prepare sensor, SCADA and CMMS data for modelling, including labelling failure events.
• Engineer time-domain and frequency-domain features from vibration and process signals.
• Train and validate anomaly detection, failure classification and remaining useful life models.
• Handle imbalanced data, leakage and asset-level validation correctly.
• Explain model outputs to engineers and set alert thresholds based on cost.
• Deploy, monitor and retrain models using MLOps practices.
• Build a business case that quantifies savings from predictive maintenance.

The programme is suited to technical and managerial staff responsible for asset reliability and industrial data, including:

• Maintenance and reliability engineers
• Condition monitoring and vibration analysts
• Plant, asset and operations managers
• Data scientists and analysts working with industrial data
• IoT, automation and SCADA engineers
• Fleet and workshop managers in transport and logistics
• Engineers in power, water, mining and oil and gas facilities
• Digital transformation and Industry 4.0 leads
• Engineering consultants and technical trainers

Participants leave the programme with:

• Python notebooks for data preparation, feature engineering and model training.
• A trained remaining useful life or failure prediction model on an industrial dataset.
• A validation protocol that avoids leakage and overstated accuracy.
• An alert threshold method based on the cost of false alarms and missed failures.
• A deployment and monitoring blueprint for production use.
• A costed business case template for a predictive maintenance pilot.

The programme is laboratory-driven and anchored in industrial data. Delivery includes:

• Concept briefings linking reliability engineering with machine learning
• Daily coding labs in Python notebooks using open turbofan, bearing and pump datasets
• Pair work that combines engineering judgement with modelling skill
• A team capstone with peer review and a management-style presentation
• Take-home notebooks and reference templates for later use

Participants who attend the ten days and complete the laboratory work and capstone project receive a CPD-accredited Certificate of Completion issued by Vision Reach Global Consultancy.