Practical Training on CBM, IoT, AI, and ML to Reduce Industrial Failures
Predictive maintenance represents one of the main evolutions of the digital industry, allowing companies to monitor plant conditions in real-time and prevent anomalies and failures through the use of sensors, MES/CMMS systems, IoT, and Artificial Intelligence and Machine Learning algorithms. The course provides a practical and applied overview of the technologies and processes underlying Condition Based Maintenance (CBM), with a focus on the integration of industrial data and the use of predictive models to improve the reliability, availability, and efficiency of plants.
Upon completion of the course, participants will be able to:
Introduction to Predictive Maintenance
– Evolution of Maintenance: From TBM to CBM
– Benefits and objectives of predictive maintenance
Technologies and data acquisition
Industrial sensor technology and IoT
– Real-time data collection and monitoring
Industrial systems integration
– PLC, SCADA, MES, and CMMS
– Data management and data quality
– Artificial Intelligence and predictive models
Machine Learning and Predictive Algorithms
– Anomaly Analysis and Failure Prediction
Practical applications and use cases
– Examples of industrial platforms and solutions
– Operational and organizational benefits
Implementation Roadmap
The course is aimed at: Maintenance and Production Managers, Industrial Technicians and Maintenance Personnel, Process and Maintenance Engineers, Plant Managers and Industry 4.0 personnel, IT/OT staff involved in industrial data management.
8 in-person hours to be held at the Lean Factory School® or at the client company's facilities. The course includes a strong experiential component based on the "learning by doing" logic: through practice on real workstations, it is possible to consolidate learning and apply what has been learned during the course.
Register to download the Summary Course Sheet
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