Please be informed that this site uses cookies.

+971 56 800 6744

+971 2 622 2605

Course Description

The "Applications of AI for Predictive Maintenance" training is beneficial because it equips participants with the knowledge and skills to leverage artificial intelligence (AI) technologies to optimize equipment maintenancestrategies. By analyzing real-time data from sensors and historical maintenance records, AI algorithms can accurately predict potential equipment failures before they occur. This proactive approach significantly reduces costly unplanned downtime, improves equipment lifespan, minimizes maintenance expenses, and enhances overall operational efficiency. Furthermore, the training provides practical insights into implementing AI-powered predictive maintenance solutions, including data collection, model development, and deployment strategies. By attending this training, individuals can gain a competitive advantage in their careers and contribute to the development of more resilient and cost-effective industrial operations.

Course Objectives

Upon the successful completion of this course, each participant will be able to:

  • Understand the fundamentals and benefits of predictive maintenance
  • Recognize how AI enhances maintenance through data-driven insights
  • Identify key data types and challenges in AI-based PdM systems
  • Describe common AI and ML models used for predicting equipment failures
  • Understand how AI models are integrated into real-world operations
  • Evaluate the business value and ROI of AI-driven maintenance
  • Be aware of challenges, ethical considerations, and future trends in AI for PdM

Who Should Attend?

This course is designed for Engineers, Maintenance Technicians, Data Scientists, and other professionals interested in applying AI for predictive maintenance.

Course Agenda

DAY 1

Registration, Welcome & Introduction

Pre-Test

Introduction to Predictive Maintenance and AI

  • Types of maintenance: Reactive, Preventive, Predictive
  • Benefits and business drivers for PdM
  • Introduction to AI and its relevance in maintenance
  • Overview of PdM use cases in different industries

DAY 2

Data in Predictive Maintenance

  • Sources of data: Sensors (vibration, temperature), logs, maintenance history
  • Time-series data basics
  • Data quality challenges: missing values, noise, drift
  • Role of IoT and data acquisition systems

DAY 3

AI & ML Models for Predictive Maintenance

  • Overview of Machine Learning models: Regression, Classification, Clustering
  • Common algorithms: Random Forests, k-NN, SVM
  • Deep Learning models: Autoencoders, LSTMs for time series
  • When to use which model

DAY 4

Deployment and Integration in Industrial Settings

  • System architecture: edge vs cloud computing
  • Model deployment: integration with SCADA, CMMS, ERP
  • Real-time vs batch processing
  • Maintenance alerts, dashboards, and decision support

DAY 5

Strategy, ROI, and Future Outlook

  • Measuring ROI and performance (e.g., downtime reduction, MTBF)
  • Organizational challenges: skills, change management, data silos
  • Ethical considerations and AI explainability
  • Emerging trends: digital twins, self-healing systems, federated learning

Post-Test

End of the Course

Assessment Methodology

All courses conducted by EdTech will begin with a Pre-evaluation and end with a Post-evaluation. The instructor will evaluate the knowledge and skills of the participants according to the feedback given by participants. This will help to recognize the benefits and the level of knowledge gained by participants through the course.

Training Methodology

Facilitated by a highly qualified specialist, who has extensive knowledge and experience; this program will be conducted using extensively interactive methods, encouraging participants to share their own experiences and apply the program material to real-life work situations in order to stimulate group discussions and improve the efficiency of the subject coverage.

Percentages of the total course hour classification are:

  • ​40% Theoretical lectures, Concepts and approach
  • 20% Motivation to develop individual skill and Techniques
  • 20% Case Studies and Practical Exercises
  • 20% Topic General Discussions and interaction

Course Manual

Participants will be provided with comprehensive presentation material as reference manual. This presentation material is a compilation of core valuable information, references, presentation methods and inspiring reading which will be used as a part of the material guide.

Course Certificate

At the completion of the course, all participants who successfully accomplished the required contact hours will receive an EdTech Training Participation Certificate as a testimony to their commitment to professional development and further education.

Why Edtech ?

  • Industry Experienced; Internationally Qualified Trainers
  • Hands-on Practical Sessions & Assignments
  • Intensive Study materials
  • Flexible Schedules
  • Realistic training methodology
  • High-Quality Training in Affordable Course Fees
  • Achievement Certificate, as approved by the Ministry of Education (Abu Dhabi Center for Technical and Vocational Education Training - ACTVET), HABC, AWS, IAOSHE, SHRM, etc.