RAM&PHM 4.0: Advanced Methods for Reliability, Availability, Maintainability, Prognostics and Health Management of Industrial Equipment - Ed.25

Program Overview

This advanced program focuses on advanced methods for reliability, availability, and maintainability (RAM) analysis, as well as Prognostics and Health Management (PHM) for complex industrial systems. It equips participants with skills in data analytics, advanced RAM analysis, and PHM techniques for condition-based and predictive maintenance. The program prepares individuals for careers in fields related to RAM and PHM, enhancing safety, efficiency, and asset management in modern industries.

Program Outline

Objectives:

The course aims to equip participants with advanced methodological competences, analytical skills, and computational tools necessary to operate effectively in the areas of reliability, availability, maintainability, diagnostics, and prognostics of modern industrial equipment and systems. The program aims to improve safety, increase efficiency, manage equipment aging and obsolescence by setting up condition-based, predictive, and prescriptive maintenance and asset management strategies.

Outline:

Content:

• Advanced Methods for RAM Analysis: The course covers advanced methods for analyzing the availability, reliability, and maintainability of complex systems.
• Prognostics and Health Management (PHM): The program delves into PHM techniques for condition-based and predictive maintenance.
• Data Analytics: The course explores data analytics techniques, including Monte Carlo Simulation, nonlinear regression, Principal Component Analysis, Auto-Associative Kernel Regression, Artificial Neural Networks, Ensemble Systems, Deep Learning, Convolutional Neural Networks, Reservoir Computing, and Particle Filtering.
• Practical Case Studies: Participants engage in hands-on sessions applying the methods learned to practical case studies using MATLAB and/or PHYTON.

Structure:

The course is structured into three parts:

Lectures:

Lectures are delivered in English, covering advanced methods for RAM analysis and PHM.

Hands-on Sessions:

Participants engage in practical sessions applying the methods to case studies.

Real Applications:

The course showcases real-world applications of the methods.

Course Schedule:

• Start Date: 2024-11-18
• End Date: 2024-11-20

Teaching:

Teaching Methods:

• Lectures: Lectures are delivered in English.
• Hands-on Sessions: Participants engage in practical sessions applying the methods to case studies.
• Real Applications: The course showcases real-world applications of the methods.

Faculty:

• Director: ENRICO ZIO
• Co-Director: PIERO BARALDI

Unique Approaches:

The course emphasizes practical application through hands-on sessions and real-world case studies.

Careers:

Potential Career Paths:

• Control, process, quality, and maintenance engineers
• Asset managers
• Data scientists
• Data miners
• Researchers
• PhD students in the areas of RAM and PHM

Opportunities:

The program aims to equip participants with the skills and knowledge needed to excel in roles related to reliability, availability, maintainability, diagnostics, and prognostics of industrial equipment and systems.

Outcomes:

• Location: Campus Bovisa- Aula 0.12 - Via Lambruschini 4
• Department: DIPARTIMENTO DI ENERGIA
• Contact Person: GIULIA PERNICANO