# AI for Maintenance and Reliability Engineering

> Use sensor data, machine learning and digital twins to predict failures and optimise maintenance across the asset life cycle.

- **Provider:** Afriskora Training Solutions
- **Category:** Engineering & Maintenance
- **Duration:** 3 days
- **Formats:** classroom (cities across Africa), live virtual (Zoom/Teams), in-house for teams
- **Certificate:** Afriskora Certificate of Completion, verifiable online
- **Booking:** request dates and a quote; invoice and EFT payment
- **Web page:** https://afriskora.co.za/courses/engineering-maintenance/ai-for-maintenance-and-reliability-engineering
- **Request dates:** https://afriskora.co.za/request-training?course=ai-for-maintenance-and-reliability-engineering

## Overview

Predictive maintenance promises fewer breakdowns and lower costs, but many pilots never scale. This course explains how condition data, machine learning and digital twins can predict failures, estimate remaining useful life and optimise maintenance and renewal decisions. Participants learn what data is needed, how to assess vendors and how to build a business case that operations will support.

## Who should attend

- Reliability and maintenance engineers
- Asset and maintenance managers
- Data analysts in industrial organisations
- Operations and engineering leaders

## Learning outcomes

- Explain predictive and prescriptive maintenance approaches
- Identify data requirements and sources
- Understand anomaly detection and remaining-useful-life models
- Use digital twins for asset life-cycle decisions
- Build a business case and scale predictive maintenance

## Course outline

### Module 1: From preventive to predictive

- Maintenance maturity
- Condition data and sensors
- CMMS and historian data
- Data quality

### Module 2: AI techniques

- Anomaly detection
- Remaining useful life
- Digital twins
- Generative AI for maintenance knowledge

### Module 3: Scaling up

- Vendor and platform selection
- Business case
- Change management with technicians
- Roadmap