AI for Engineers & Technicians

1. Training Introduction

The AI for Engineers & Technicians programme is designed to introduce technical professionals to the essential concepts, tools, and practical applications of Artificial Intelligence in modern engineering environments.

The course provides a hands-on, skill-focused pathway for understanding how AI can support engineering design, predictive maintenance, troubleshooting, automation, quality control, manufacturing optimization, and operational efficiency.

This programme bridges the gap between traditional engineering skills and industry-ready AI competencies, enabling participants to work confidently with data, sensors, machine learning models, and automation systems commonly used in factories, utilities, transportation, energy, construction, telecoms, aviation, and industrial operations.

 

2. Training Objective

The programme aims to:

• Equip engineers and technicians with practical AI knowledge applicable to real engineering tasks.
• Teach participants to collect, process, and interpret engineering data for AI-driven decisions.
• Demonstrate how AI improves troubleshooting, reliability, quality, and safety.
• Train participants to use machine learning (ML) to predict failures, classify faults, and optimize engineering systems.
• Enable participants to work with digital tools such as sensors, robotics, IoT devices, and control systems enhanced with AI.
• Prepare participants for engineering workplaces transitioning to Industry 4.0 environments.

 

3. Targeted Group

This programme is ideal for:

• Engineers (mechanical, electrical, civil, industrial, chemical, telecom, mechatronics, etc.)
• Technicians in maintenance, instrumentation, manufacturing, or utilities
• Engineering assistants and technologists
• Machine operators and process control technicians
• Engineering students and interns seeking AI exposure
• Anyone interested in AI-enhanced engineering operations

 

4. Course Duration

15–16 Days (Standard Programme)
12 Days (Accelerated Version for Technical Experts)

 

5. Training Methodology

• Instructor-led lectures
• Hands-on practical labs (machine learning, sensors, troubleshooting models)
• Demonstrations with Python, MATLAB, PLC/SCADA data, and industrial datasets
• Group assignments and case-based exercises
• Real-life engineering examples (motors, pumps, turbines, circuits, production lines)
• Guided teamwork on predictive and diagnostic AI models
• Capstone project requiring a working AI-based engineering solution

 

6. Course Content

Module 1: Introduction to AI and Its Engineering Applications

• What AI is and what it is not
• AI applications in electrical, mechanical, civil, industrial, and ICT engineering
• Benefits and limitations

Module 2: Basics of Engineering Data

• Types of engineering data (sensor logs, vibration, temperature, images, voltage/current)
• Data collection from machines, PLCs, IoT sensors, SCADA
• Data cleaning and preparation

Module 3: Fundamentals of Machine Learning

• Supervised vs unsupervised learning
• Regression, classification, clustering for engineering tasks
• ML workflow for technicians and engineers

Module 4: Python for Engineers & Technicians

• Quick introduction to Python for technical users
• Libraries: NumPy, Pandas, Scikit-Learn, Matplotlib
• Running sample machine learning tasks

Module 5: AI in Predictive Maintenance

• Condition-based monitoring
• Vibration, acoustic, electrical signature analysis
• Building failure prediction models

Module 6: Fault Detection & Troubleshooting with AI

• Fault classification for motors, valves, circuits, bearings, pumps
• Diagnostics with ML models
• Using AI to reduce downtime

Module 7: AI for Quality Control and Production

• Image-based defect inspection
• Process optimization
• Automated measurement systems

Module 8: Sensors, IoT & Data Acquisition Systems

• Smart sensors
• IoT devices and communication protocols
• Connecting sensors to AI systems

Module 9: Robotics and Automation Basics

• Industrial robots
• PLCs, SCADA, HMIs
• How AI interacts with automation systems

Module 10: Digital Twins and Simulation for Technicians

• Virtual models of machines and equipment
• Offline troubleshooting and testing using simulations
• Real-time monitoring concept

Module 11: Energy Efficiency & Process Optimization with AI

• Detecting inefficiencies in motors, HVAC, pumps, production lines
• AI-driven optimization strategies
• Savings and performance improvements

Module 12: AI for Safety Monitoring

• Hazard detection
• Alarm prediction
• Wearable smart safety systems

Module 13: Computer Vision for Engineering

• Object detection
• Visual inspection of equipment
• Safety and monitoring systems

Module 14: Edge Computing & Embedded AI for Technicians

• Running AI on microcontrollers, sensors, machines
• Using Raspberry Pi, Arduino, ESP32
• Real-time engineering applications

Module 15: Hands-On Engineering AI Laboratory

• Build predictive maintenance model
• Construct a basic fault classifier
• Create sensor-based automation workflow
• Troubleshoot real datasets

Module 16: Capstone Project – AI Solution for an Engineering Problem

• Team project: select an engineering system (motor, pump, circuit, line, HVAC, etc.)
• Collect, process, or analyze data
• Build an AI solution (prediction, fault detection, control optimization)
• Present findings and deployment plan

 

7. Expected Learning Outcomes

Participants will be able to:

• Identify engineering tasks where AI and automation can be applied effectively.
• Collect, clean, and analyze engineering datasets.
• Build simple but useful machine learning models for engineering problems.
• Diagnose faults, predict failures, and optimize operations using AI.
• Integrate AI with automation systems (PLCs, SCADA, IoT, robots).
• Enhance engineering operations with increased efficiency, quality, and reliability.
• Work confidently in modern Industry 4.0 engineering environments.

 

8. Certificate of Completion

Participants who complete all modules, laboratory tasks, and the capstone project will receive:

Certificate of Completion

AI for Engineers & Technicians

Issued by FOTADE Training, Research and Resource Development Centre

The certificate validates the participant’s competence in AI fundamentals, engineering data analysis, predictive maintenance, automation integration, and practical technical AI applications.