Objectives
- Explain the core principles, terminology and governing standards of Predictive Maintenance with AI
- Apply proven methods and tools for Predictive Maintenance with AI to real workplace situations
- Analyse performance data to find risks, gaps and improvement opportunities
- Compare your practice with international benchmarks and regional case studies
- Build a practical action plan to apply Predictive Maintenance with AI in your organisation
Who should attend
Maintenance managers, planners, reliability engineers and condition monitoring staff.
Course outline — 5 days
- Day 1Principles, terminology and governing standardsPredictive Maintenance with AI
- Day 2Methods, tools and calculationsPredictive Maintenance with AI
- Day 3Operation, inspection and performancePredictive Maintenance with AI
- Day 4Risk, troubleshooting and case studiesPredictive Maintenance with AI
- Day 5Best practice, assessment and action planPredictive Maintenance with AI
10-day outline
- Day 1Principles, terminology and governing standardsPredictive Maintenance with AI
- Day 2Methods, tools and calculationsPredictive Maintenance with AI
- Day 3Operation, inspection and performancePredictive Maintenance with AI
- Day 4Risk, troubleshooting and case studiesPredictive Maintenance with AI
- Day 5Best practice review and mid-course assessmentPredictive Maintenance with AI
- Day 6Advanced analysis techniquesPredictive Maintenance with AI
- Day 7Digital tools, data and softwarePredictive Maintenance with AI
- Day 8Practical labs and simulationsPredictive Maintenance with AI
- Day 9Regional benchmarks and lessons learnedPredictive Maintenance with AI
- Day 10Capstone project and final assessmentPredictive Maintenance with AI
Upcoming sessions 2027–2030
| City | Dates | Duration |
|---|---|---|
| Sharm El Sheikh | 7 Jan 2030 – 11 Jan 2030 | 5 days |
| Nairobi | 21 Oct 2030 – 1 Nov 2030 | 10 days |
No public session is listed yet. We can run this course in any city on your dates, online, or at your company.