Course Description
This training course explores how artificial intelligence and machine learning are transforming maintenance — moving from scheduled and reactive approaches to data-driven predictive maintenance.
Participants learn how AI techniques analyse condition-monitoring data to predict equipment failure before it occurs, reducing downtime and maintenance cost. The course bridges maintenance engineering with data science, showing how predictive models are built, deployed, and integrated into a reliability program — directly relevant to the future of maintenance work.
What you will achieve
By the end of this training course, participants will be able to:
- Explain how AI and machine learning apply to predictive maintenance
- Understand the data required for predictive maintenance models
- Describe condition-monitoring data sources and preparation
- Understand common machine learning techniques for failure prediction
- Interpret predictive maintenance model outputs
- Integrate AI-driven predictions into a maintenance program
- Evaluate the business case for predictive maintenance
How the course is delivered
The course combines maintenance engineering context with practical explanation of AI and machine learning techniques, using case studies and demonstrations. It is designed to be accessible to maintenance professionals without a data science background.
On successful completion, an BII Research Certificate with CPE credits is awarded.
Designed for
This training course is ideal for:
- Maintenance and reliability engineers
- Condition monitoring specialists
- Asset managers exploring digital maintenance
- Engineers adopting Industry 4.0 practices
- Technical staff bridging maintenance and data
Daily programme
- Evolution of maintenance strategies
- The role of AI in predictive maintenance
- Industry 4.0 and digital maintenance
- Business case for predictive maintenance
- Condition monitoring data sources
- Sensors and the Industrial IoT
- Data quality and preparation
- Data infrastructure
- Introduction to machine learning
- Supervised and unsupervised learning
- Failure prediction techniques
- Anomaly detection
- Feature engineering from sensor data
- Model training and validation
- Remaining useful life estimation
- Interpreting model outputs
- Integrating predictions into maintenance
- Deploying and maintaining models
- Organisational change and adoption
- Course review and case studies
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.
