Course Description
Predictive maintenance powered by machine learning shifts maintenance from scheduled and reactive approaches to condition-based, data-driven intervention — servicing equipment precisely when measurable indicators forecast degradation. This hands-on course develops the ability to design, build, and deploy predictive maintenance models.
Participants learn the full predictive maintenance workflow: from data acquisition through IoT sensors, feature engineering, model building, and validation, to deployment and integration with CMMS and SCADA systems. The course focuses on practical implementation, equipping engineers to launch and scale predictive maintenance initiatives aligned with Industry 4.0.
What you will achieve
By the end of this training course, participants will be able to:
- Explain the predictive maintenance workflow end to end
- Design IoT-enabled data collection for equipment monitoring
- Engineer features from sensor and operational data
- Build machine learning models for failure prediction
- Apply time-series analysis and anomaly detection
- Estimate remaining useful life and failure timing
- Integrate predictive models with CMMS, SCADA, and ERP systems
- Evaluate the ROI and business impact of predictive maintenance
How the course is delivered
The course combines the predictive maintenance workflow with hands-on model-building exercises using industrial sensor data. Participants work through feature engineering, model training, and validation on realistic datasets.
The programme emphasises practical implementation and integration, drawing on real industrial predictive maintenance deployments across manufacturing, energy, and transport.
Designed for
This training course is ideal for:
- Reliability and maintenance engineers
- Predictive maintenance program leaders
- Asset managers adopting Industry 4.0
- CMMS and SCADA analysts
- Engineers building data-driven maintenance capability
Daily programme
- Evolution to predictive maintenance
- The predictive maintenance workflow
- Data-driven versus scheduled maintenance
- Business case and ROI
- IoT sensors and data collection
- Industrial data sources and protocols
- Data quality and preparation
- Building the data pipeline
- Feature engineering from sensor data
- Machine learning models for failure prediction
- Time-series analysis
- Model training and validation
- Remaining useful life estimation
- Anomaly detection in production
- Deploying predictive models
- Integration with CMMS, SCADA, ERP
- Scaling predictive maintenance
- Measuring ROI and business impact
- Governance and continuous improvement
- Case study workshop and review
Certification & accreditation
BII Certificate of Completion
BII Certificate of Completion Upon successful completion, participants receive a BII Development Institute Certificate of Completion with a unique reference code that is independently verifiable. Our certificates are recognised internationally and reflect successful completion of your chosen programme.
