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Dettaglio Insegnamento

Contesto
Anno Accademico 2021/2022
Corso di Studi Dott. - MI (1383) Ingegneria Meccanica / Mechanical Engineering
Anno di Corso 1

Scheda Insegnamento
Codice Identificativo 057651
Denominazione Insegnamento METHODS FOR HEALTH MONITORING AND PROGNOSIS OF ENGINEERING SYSTEMS SUBJECT TO DEGRADATION
Tipo Insegnamento MONODISCIPLINARE
Crediti Formativi Universitari (CFU) 5.0
Programma sintetico In the last decades, many different methods have emerged for the health monitoring of engineering systems, including structural, mechanical, aerospace, energy and bio-inspired applications, just to mention a few. In most of the cases, this requires the acquisition of multiple sensor data, coupled with signal processing techniques for capturing any feature of interest, in the hope that the occurring degradation mechanism is correctly identified, then enabling system prognosis. However, one main obstacle in system state identification is the presence of confounding influences, either due to operational or environmental effects, hampering the feature extraction process. Within this framework, the course introduces different methods for the analysis of real-time data aiming the diagnosis and prognosis of systems subject to degradation in a realistic environment, including (i) outlier analysis for novelty detection, (ii) data normalisation for suppression of confounding influences, (iii) machine learning for diagnosis and surrogate modelling, (iv) sequential Monte-Carlo methods for state identification and prognosis. Follows the course programme: - Introduction and Design for SHM systems: Basic principles and definitions, Hardware components of the SHM, sensors, Design process, Motivation for SHM, Non-Destructive Evaluation vs SHM, Statistical pattern recognition paradigm, Historical overview - Feature selection and extraction: basic statistics, statistical moments/distributions, central limit theorem, principal component analysis - Novelty detection: features in the context of detection theory, feature classification, environmental/operational effects on SHM - Support Vector Machine for classification - Artificial Neural Network for classification and regression - Gaussian Processes - Graphical models and inference for SHM - Introduction to Monte-Carlo sampling and importance sampling theory - Bayesian inference - Model updating with Metropolis-Hastings MCMC algorithm - Sequential Monte-Carlo sampling theory the particle filter
Settori Scientifico Disciplinari (SSD) --

Dettaglio
Scaglione Docente Programma dettagliato
Da (compreso) A (escluso)
A ZZZZ Sbarufatti Claudio, Cadini Francesco, Worden Keith
manifestidott v. 1.7.0 / 1.7.0
Area Servizi ICT
12/08/2022