RAM & PHM 4.0

RAM & PHM 4.0

Advanced methods for Reliability, Availability, Maintainability, Prognostics and Health Management of industrial equipment

Course presentation

In recent years, the volume of data and information collected by the industry has been growing exponentially, and more sophisticated and performing analytics have been developed to exploit their content.

This offers great opportunities for optimized, safe and reliable productions and products, including optimal predictive maintenance for “zero-defect” production with reduced warehouse costs,  and improved system availability, with “zero unexpected shutdowns”. To grasp these opportunities, new system analysis capabilities and data analytics skills are needed.

The goal of this course is to provide participants with advanced methodological competences, analytical skills and computational tools necessary to effectively operate in the areas of reliability, availability, maintainability, diagnostics and prognostics of modern industrial equipment. The course presents advanced techniques and analytics to improve safety, increase efficiency, manage equipment aging and obsolescence, set up condition-based and predictive maintenance.


Laboratory of Analysis of Signals and Analysis of Risk (LASAR) www. lasar.polimi.it

Energy Data and Information Lab (EDILAB)


ESRA (European Safety and Reliability Association) www.esrahomepage.eu


ARAMIS Srl, Milano, Italy www.aramis3d.com

Cluster S2D2 (Cluster Security, Safety, Defense, Disaster Management and Recovery) of Politecnico di Milano

CRESCI (Center for Reliability and Safety of Critical Infrastructures), Beihang University, Beijing, China

IEEE – Reliability Society, Italian Chapter

Kyung Hee University, Department of Nuclear Engineering, College of Engineering, Republic of Korea

MINES ParisTech, PSL Research University, CRC, Sophia Antipolis, France 

Lasar Group Presentation

The LASAR (Laboratory of Signal Analysis and Risk Analysis) within the department of Energy of the Politecnico di Milano brings has expertise on Probabilistic Reliability and Safety Analysis of complex systems operating in stationary or dynamic conditions, Monte Carlo simulation modelling of the processes of failure, repair, ageing, maintenance, renovation of components, within the Reliability Availability Maintenance Safety (RAMS), dependability and resilience analysis of mechanical, aerospace, chemical, energy systems, Computational Intelligence (neural networks, genetic algorithms and fuzzy logic) for the multi-objective optimal design, operation and logistic management of complex systems, and for the identification, simulation and control of physical, chemical and thermal-hydraulic processes, with application in early fault detection, diagnostics and prognostics.