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 and systems.
The course presents advanced techniques and analytics to improve safety, increase efficiency, manage equipment aging and obsolescence by setting up condition-based, predictive and prescriptive maintenance and asset management strategies.
ORGANIZERS
Department of Energy – Politecnico di Milano www.energia.polimi.it
Laboratory of Analysis of Signals and Analysis of Risk (LASAR) www. lasar.polimi.it
Energy Data and Information Lab (EDILAB)
SPONSORSHIP
ESRA (European Safety and Reliability Association) www.esrahomepage.eu
SUPPORT
ARAMIS Srl, Milano, Italy www.aramis3d.com
IEEE – Reliability Society, Italian Chapter, Italy
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.