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The course is composed of 6 modules. These modules cover a range of aspects to provide you with maximum practical coverage in the field of plant maintenance strategies and AI-driven predictive technologies.

Module 1: Plant Maintenance Strategies & Asset Reliability

• Maintenance Philosophies: Corrective, Preventive, Predictive, Prescriptive & Asset Reliability
• Asset Lifecycle Management and Maintenance Economics
• Key performance indicators (MTBF, MTTR, OEE, availability)
• Integration of AI for asset health monitoring and predictive analytics

Module 2: Equipment Failure Analysis & Condition Monitoring

• Root Cause Failure Analysis (RCFA), FMEA
• Condition Monitoring Techniques: Vibration, Oil Analysis, Ultrasonic, Thermography
• AI-Supported Condition Monitoring: Automated Fault Detection and Diagnosis
• Real-time anomaly detection using machine learning

Module 3: AI & Machine Learning for Predictive Maintenance

• Introduction to Machine Learning (ML) and AI basics
• Predictive Analytics using ML Models (Classification, Regression, Clustering)
• Data Pre-Processing for Plant Data Analytics
• Practical examples: AI-based Predictions for Equipment Failures (Pumps, Conveyors, Mills, Motors)

 Module 4: Digital Twins & Intelligent Plant Operations

• Fundamentals of Digital Twin Technology
• AI-Driven Digital Twin for Predictive Maintenance
• Integrating Digital Twins with Plant SCADA, PLCs, CMMS
• Real-time Asset Performance Optimization through Digital Twins

Module 5: Maintenance Planning & AI Optimization

• Planning and Scheduling Maintenance Tasks (Routine, Shutdowns, Turnarounds)
• Leveraging AI for Optimal Scheduling and Resource Allocation 
• Using AI Analytics to Forecast Spares and Inventory Requirements
• Integrating Predictive Insights into Operational Decision-making

Module 6: Industry Applications & Case Studies

• Oceana Gold Mining and Processing Plant AI-driven Predictive Maintenance
• Broader Case Studies from Mining, Oil & Gas, Manufacturing, and Energy Sectors
• ROI and Economic Benefits of AI-enabled Predictive Maintenance
• Overcoming Practical Challenges in AI Implementation

To obtain a certificate of completion for EIT’s Professional Certificate of Competency, students must achieve a 65% attendance rate at the live, online fortnightly webinars.  Detailed summaries/notes can be submitted in lieu of attendance.  In addition, students must obtain a mark of 60% in the set assignments which could take the form of written assignments and practical assignments. Students must also obtain a mark of 100% in quizzes.  If a student does not achieve the required score, they will be given an opportunity to resubmit the assignment to obtain the required score.

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Our courses are delivered by experienced engineers and technical experts from around the world. Many have tackled real-world engineering challenges and bring practical, applied knowledge directly into the classroom.

We draw from a global pool of instructors and lecturers across our organisation. Explore our full team of expert educators on the Instructors & Lecturers page here.

Please note: Not all lecturers and instructors listed on this page will teach every course. The team teaching your course will be confirmed as part of your course enrolment.

You are expected to spend approximately 5-8 hours per week learning the course content. This includes attending fortnightly webinars that run for about 90 minutes to facilitate class discussion and allow you to ask questions.

This program has a 65% attendance requirement in the live webinars in order to graduate from the program. If you are unable to attend the live webinars, you have the option of watching the recording of completed webinars and sending a summary of what you have learnt from the webinar to the Learning Support officer. The summaries go towards your attendance requirement for the program.

This program is run online on an intensive part-time basis and has been designed to fit around full-time work. It will take six weeks to complete.

We understand that sometimes work commitments and personal circumstances can get in the way of your studies, so if at any point you feel that you are struggling with the pace of the course or finding a particular module challenging, you are encouraged to contact your designated Learning Support Officer for assistance.

Please ensure you book your place at least one week before the course start date.

If the course is not currently scheduled, please contact us. We can let you know when it will be offered next, or explore the possibility of creating a special intake if there is a group.