PERIODO: SETTEMBRE 2018 Objective of the course is to provide a presentation on the state-of-art of the discipline of Prognostics and Health Management (PHM), which is an integral part of the Condition Based Maintenance (CBM) approach to the systems maintenance. The course will thus present an overview of characteristics, methodologies and critical issues of the PHM technology and show a few application cases.

1 INTRODUCTION 1.1 Background and objectives 1.2 Definitions 1.3 Operational context 1.4 Contributing disciplines 2 FUNDAMENTALS 2.1 PHM systems architecture 2.2 Sensing and data processing 2.3 Steps for developing a PHM system 2.4 Fault monitoring, diagnosis and prognosis framework 3 METHODOLOGIES 3.1 Fault detection methods 3.1.1 Data driven fault classification and decision making 3.1.2 Physical model-based methods 3.1.3 Mathematical model-based methods 3.1.4 Case-based reasoning 3.1.5 Model-based reasoning 3.1.6 Rule-based systems 3.1.7 Statistical change detection 3.1.8 Artificial neural networks 3.1.9 Fuzzy logic 3.1.10 State-based feature recognition 3.1.11 Bayesian networks 3.1.12 Hidden Markov models 3.2 Fault prognosis 3.2.1 Critical issues 3.2.2 Model-based prognosis techniques 3.2.3 Probability-based prognosis techniques 3.2.4 Data-driven prognosis techniques 3.3 Performance metrics 4 INTEGRATED HEALTH MONITORING 4.1 Business value 4.1.1 Cost-benefit analysis 4.1.2 Maintenance scheduling and spare parts supply chain 4.2 Autonomic logistic support 4.3 Fielded systems 5 APPLICATIONS - Gearbox identification and faults detection 5.1 Basics 5.1.1 Problem statements 5.1.2 Symbols and definitions for gearboxes 5.1.3 Meshing Frequencies (also satellite gears) 5.1.4 Signal Splitting techniques (bearing vs gears) 5.1.5 Tacho and resampling techniques 5.1.6 Mesurements limitations in real world 5.2 Fault identification 5.2.1 Old fashion analysis: FFT, Cepstrum, Kurtosis, other indicators (NA4 etc..) 5.2.2 Order tracking analysis 5.2.3 Processing techniques and enhancement 5.2.4 Cyclostationarity 5.2.5 Spectral Kurtosis 5.2.6 Demodulation 5.2.7 EMD / EMD and SR 5.3 Case studies 5.3.1 Aircraft gearbox 5.3.2 Bearing fault detection

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Gli studenti e le studentesse con disabilità o con Disturbi Specifici di Apprendimento (DSA), oltre alla segnalazione tramite procedura informatizzata, sono invitati a comunicare anche direttamente al/la docente titolare dell'insegnamento, con un preavviso non inferiore ad una settimana dall'avvio della sessione d'esame, gli strumenti compensativi concordati con l'Unità Special Needs, al fine di permettere al/la docente la declinazione più idonea in riferimento alla specifica tipologia di esame.