MADE_3 - Case Studies in AI: Smart Maintenance (Pilot)

Type of course:

Digital learning, Lesson

Language:

Duration:

15 minutes

Proficiency:

Intermediate

Target:

Professionals

SUMMARY

This course provides an in-depth exploration of how AI-based smart maintenance strategies are transforming predictive maintenance. Using a real-world case study, learners will understand how AI, machine learning, and IoT sensors work together to predict equipment failures, optimize maintenance schedules, and reduce costly downtime. Designed for both technical and non-technical professionals, this course blends theoretical insights with practical applications, showing how smart maintenance contributes to operational efficiency, cost reduction, and increased asset reliability.

About The Author

Carlo Ongini is the Head of Innovation at the MADE Competence Center in Milan, Italy. He holds a PhD in System and Control Automation and a master’s degree in Computer Engineering from Politecnico di Milano. With a deep expertise in Computer Vision, AI, and Machine Learning, his research focuses on their application in Automation, Robotics, and Industry 4.0.

Ongini has authored over 10 publications and a patent application. He has extensive experience across various industry sectors, having led advanced technology initiatives at Electrolux, driving intelligent automation solutions in manufacturing, and at Vodafone Business, where he managed the development of 5G and IoT solutions for industry applications. He also played a key role in Vodafone’s 5G Trials in Milan and was instrumental in the growth of Smart Robots, an Italian startup specializing in robotic and visual automation systems.

MADE is a Competence Center for Industry 4.0 created to implement Orientation, Training, and Finalization activities for technology transfer projects with companies on Industry 4.0 issues. The ultimate goal of the Competence Center is to keep the profile of companies high, competitive, and sustainable. Moreover, MADE supports manufacturing companies, especially small and medium enterprises, on the path of digital transformation to factory 4.0: smart, connected, and sustainable, by providing a wide range of knowledge, methods and tools on digital technologies.

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Learning outcomes

Analyze and differentiate between historical and real-time data sources to apply effective predictive maintenance strategies that improve equipment uptime and reduce operational costs.
Evaluate and implement machine learning techniques, including supervised and unsupervised models, for predicting equipment failures and estimating remaining useful life (RUL) in industrial applications.
Develop a tailored predictive maintenance plan for their organization that integrates IoT, edge computing, and digital twin technology to enhance real-time monitoring and data-driven decision-making.