The Graduate Certificate in AI-driven Predictive Maintenance provides students with the knowledge and tools to implement advanced predictive maintenance strategies using AI technologies. The core modules of the program include:
Foundations of Predictive Maintenance: This module introduces students to the fundamentals of predictive maintenance, including the principles of reliability engineering, failure analysis, and maintenance strategies.
Machine Learning for Predictive Maintenance: Students learn how to apply machine learning algorithms to analyze equipment sensor data, detect anomalies, and predict potential failures before they occur.
Data Analytics and Visualization: This module focuses on data preprocessing, feature engineering, and data visualization techniques to extract actionable insights from large-scale maintenance datasets.
IoT Sensors and Condition Monitoring: Students explore the role of IoT sensors in collecting real-time equipment data, monitoring asset health, and enabling proactive maintenance interventions.
Predictive Modeling and Optimization: This module covers advanced predictive modeling techniques, optimization algorithms, and decision-making frameworks to optimize maintenance schedules and resource allocation.
Throughout the program, students engage in hands-on projects and case studies, allowing them to apply theoretical concepts to real-world scenarios. By the end of the course, graduates will be equipped with the skills and expertise to implement AI-driven predictive maintenance solutions, enhance operational efficiency, and drive value for organizations across industries.