AI for Predictive Maintenance
Overview
Unplanned equipment failures are costly and dangerous. This course shows maintenance and reliability teams how AI uses sensor and maintenance data to predict failures before they happen, and how to plan, pilot and scale a predictive maintenance programme that delivers measurable reliability gains.
Who should attend
Maintenance and reliability engineers, maintenance planners, asset managers, and operations supervisors.
Training methodology
Interactive presentations, live demonstrations of AI tools, hands-on exercises using participants' own work examples, case studies, group discussion, and a personal action plan.
Objectives
By the end of this course, participants will be able to:
- Explain how AI predicts equipment failures
- Identify suitable assets and data sources
- Interpret predictive alerts and plan interventions
- Integrate predictions with maintenance planning systems
- Build a business case and pilot plan
Course outline
Day 1From Preventive to Predictive
- Maintenance strategies compared
- Failure patterns
- Role of AI
- Asset criticality review
- Maintenance KPIs
- Data readiness
Day 2Data and Sensors
- Vibration, temperature and other signals
- Maintenance history data
- Data quality and integration
- Exercise
- IoT and wireless sensors
- Edge versus cloud
Day 3AI Models for Prediction
- Anomaly detection
- Remaining useful life estimation
- Interpreting alerts
- Case study
- False alarms
- Engineer judgement
Day 4Acting on Predictions
- Linking to CMMS and work orders
- Planning and scheduling
- Spare parts decisions
- Workflow exercise
- Reliability analysis
- Feedback to models
Day 5Building the Programme
- Business case and KPIs
- Pilot design
- Scaling and skills
- Programme plan
- Vendor selection
- Skills and roles
