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Scheda Riassuntiva
Anno Accademico 2016/2017
Tipo incarico Dottorato
Insegnamento 099088 - ADVANCED TOPICS IN RISK AND RELIABILITY ANALYSIS OF ENERGY SYSTEMS
Docente Cadini Francesco
Cfu 5.00 Tipo insegnamento Monodisciplinare

Corso di Dottorato Da (compreso) A (escluso) Insegnamento
MI (1372) - SCIENZE E TECNOLOGIE ENERGETICHE E NUCLEARI / ENERGY AND NUCLEAR SCIENCE AND TECHNOLOGYAZZZZ099088 - ADVANCED TOPICS IN RISK AND RELIABILITY ANALYSIS OF ENERGY SYSTEMS

Programma dettagliato e risultati di apprendimento attesi

Mission and goals:

The goal of the course is to provide participants with the methodological competences and the computational tools necessary to tackle critical problems in the areas of reliability, availability, maintainability, diagnostics and prognostics, with application to energy systems. To this purpose, the course presents proven methods to improve safety, increase efficiency, manage equipment aging and obsolescence, automate maintenance and reduce maintenance costs of industrial systems.

 

Contents:

The first part of the course is devoted to the presentation of advanced methods for the availability, reliability and maintainability analysis of complex systems and for the development of Prognostics and Health Management (PHM) and Condition Based Maintenance (CBM) approaches.

In this respect, the basics and more recent advancements of Markov modeling, nonlinear regression and filter models (Artificial Neural Networks, Principal Component Analysis, Auto Associative Kernel Regression, Ensemble Systems) and evolutionary optimization methods (Genetic Algorithms) are illustrated.

In the second part of the course, exercise sessions on Markov modeling, Artificial Neural Networks and Genetic Algorithms provide the participants with the opportunity of directly applying the methods to practical case studies.

Finally, in the last part of the course, real applications of the advanced methods are illustrated. The applications range from the evaluation of maintenance costs taking into account the reliability and availability of equipment, to the application of advanced Markov modeling for availability analysis and condition-based maintenance management and of regression and classification techniques to fault detection, classification and prognosis in complex industrial plants.


Note Sulla Modalità di valutazione

Application of one of the techniques presented during the course to some relevant case studies. Presentation of the results.


Intervallo di svolgimento dell'attività didattica
Data inizio
Data termine

Calendario testuale dell'attività didattica


Schedule:

Monday: 9 January 2017

9.00 - 11.00: Introduction to Risk (Cadini)

11.00 - 13.00: Dynamic Probabilistic Risk Assessment (Di Maio)

14.00 - 17.00: Evolutionary Algorithms forfailure scenarios identification (Di Maio)                               

Tuesday: 10 January 2017

9.00 - 11.00: Introduction to reliability, maintenance and PHM (Baraldi)

11:00 - 13.00: Fault Detection: Principal Component Analysis (Baraldi)

14:00 - 16:00: Fault Detection: Exercises (Baraldi)

Wednesday: 11 January 2017

9:00 – 13:00: Model Based Prognostics and Heath Management (Cadini)

14.00 -18.00: Model Based Prognostics and Health Management: Exercises (Cadini)

Thursday: 12 January 2017

9.00 - 11.00: Data-driven prognostics: Artificial Neural Networks (Baraldi)

11:00-13.00: Data-driven prognostics: Exercises (Baraldi)


Bibliografia

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Mix Forme Didattiche
Tipo Forma Didattica Ore didattiche
lezione
25.0
esercitazione
0.0
laboratorio informatico
0.0
laboratorio sperimentale
0.0
progetto
0.0
laboratorio di progetto
0.0

Informazioni in lingua inglese a supporto dell'internazionalizzazione
Insegnamento erogato in lingua Inglese
Disponibilità di materiale didattico/slides in lingua inglese
Disponibilità di libri di testo/bibliografia in lingua inglese
Possibilità di sostenere l'esame in lingua inglese
Disponibilità di supporto didattico in lingua inglese

Note Docente
schedaincarico v. 1.6.9 / 1.6.9
Area Servizi ICT
21/01/2022