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ADVANCED METHODS OF MONITORING AND DESIGN OF SYSTEMS

CODE 98218
ACADEMIC YEAR 2022/2023
CREDITS
  • 4 cfu during the 1st year of 10728 ENGINEERING TECHNOLOGY FOR STRATEGY (AND SECURITY)(LM/DS) - GENOVA
  • SCIENTIFIC DISCIPLINARY SECTOR ING-IND/09
    LANGUAGE English
    TEACHING LOCATION
  • GENOVA
  • SEMESTER 1° Semester
    TEACHING MATERIALS AULAWEB

    OVERVIEW

    The course provides the fundamentals to robust design of systems and related procedures for moitoring and diagnostics,  encompassing deterministic and stocastic approaches. Practical examples are derived from the mechanical engineering and energy system fields.

    AIMS AND CONTENT

    LEARNING OUTCOMES

    The course aims to illustrate how the design under uncertainty can help in modelling and design of the energy systems. The first part of the course will cover the necessary fundamentals of statistics. Then different uncertainty quantification methods will be presented, starting from sampling method like Monte Carlo and continuing with different approximated methods an overview of robust design will presented, focusing on the application of uncertainty quantification method in optimization problems. In the second part of the course, advanced techniques for Data Driven monitoring will be presented. Both methods will be applied to different case studies.

    AIMS AND LEARNING OUTCOMES

    At the end of the course the student will be able to:

    - approach and quantify the uncertainties impacting on a specific design problem

    - use mathematical methods for estimating the uncertainty in the design outputs

    - conduct a systematic design of experiment and treat design problems under uncertainty

    - apply robust design techniques to engineering problems

    - recognize the most common monitoring and diagnostic strategies

    - design a monitoring system selecting the most relevant parameters

    - select the most promising modelling approach on the basis of the available system information

    TEACHING METHODS

    Lectures and exercises on PC

    SYLLABUS/CONTENT

    Part A – Robust Design of Systems

    Lecture 1 – Introduction and motivation

    Course introduction, practical needs and applications of uncertainty quantification and robust design techniques.

    Introduction to statistics: statistical analysis and main parameters (average, variance, probability distribution functions, etc.).

    Lecture 2 – Uncertainty definition and treatment

    Source and types of uncertainties (aleatory, epistemic).  Uncertainty in measurements, recommended approach in scientific publications, ASME standards.

    Lecture 3 – Uncertainty estimation and Montecarlo simulation

    Overview of sampling and approximate methods for uncertainty quantification - UQ (Monte Carlo, response sensitivity analysis, polynomial chaos, design of experiment, response surface methodology), backward and forward UQ analysis. The reference sampling method: MonteCarlo simulation. The concept of Mean Square Pure Error (MSPE).

    Lecture 4 – Approximate methods

    Fundamentals and features of approximated methods for uncertainty quantification and propagation from inputs to outputs: the Response Sensitivity Analysis - RSA, the Polynomial Chaos - PC.

    Lecture 5 – Design of Experiment and Response Surface methods

    Fundamentals and features of approximated methods for uncertainty quantification and propagation from inputs to outputs: Response Surface Method – RSM, Design of Experiment – DOE.

    Lecture 6 – Robust design approach

    Robust design: fundamentals, optimization algorithm, combination with uncertainty quantification methods. Application examples: analytical function, cantilever beam, microgas turbine, gas-gas heat exchanger, advanced fuel cell energy system.

    Lecture 7 – Exercise on algebraic application

    MonteCarlo simulation and RSA method in Matlab environment: the Rosenbrock function.

    Lecture 8 – Exercise on engineering application

    Robust Design of a gas-gas heat exchanger: optimisation algorithm and uncertainty quantification.

     

    Part B – Monitoring and Diagnostics

    Lecture 1: Overview of the monitoring and diagnostics: objective, hurdles and methods

    General definitions, Maintenance Strategies, Monitoring application, Interaction between Monitoring and Control systems, Process Monitoring Design: Design of Experiment vs Monitoring

    Lecture 2: Industrial approach to monitoring and diagnostics

    Definitions by ISO 13372 2012: Condition monitoring and diagnostics of machines; Condition Monitoring; Diagnosis and Prognosis; Expert Systems for Monitoring and Diagnosis of Rotating Machinery
     

    Lecture 3: Dealing with measurement error: Data Reconciliation & Gross Error Detection

    Introduction to measurement errors; Comparison of data validation methods; DR and GED theory; problem formulation; Novel technique vs. traditional DR approach; VDI 2048 Exercise

    Lecture 4: Exercise on Data Reconciliation

    VDI 2048 Exercise solution: Linear solution with Lagrange Multiplier; Non-linear solution with Sequential Quadratic Programming.

    Lecture 5: Selection of monitoring and observation + detection strategy:

    First principles models and Data-Driven Models; Example of Fault detection and identification approaches; Fault simulation.

    Lecture 6: Dealing with high dimension Data Base: Principal Component Analysis

    PCA Theory; PCA for Dimensionality Reduction; Examples of PCA as regression model.

    Lecture 7: Selection of Regression Models

    Example of Regression Models; Model and Feature Selections, Technique for Data-Driven application; Evaluating the goodness of fit; Underfitting vs Overfitting;  Exercise on Data sets

    Lecture 8: Real-Time Risk Management enabled by Industry 4.0

    Industry 4.0; Industrial Internet of Things; Digital twin; Digital Factory; Risk Management in the Age of IIoT; State of the art of safety controls; Risk Assessment methods; Real-Time Risk Management.

    RECOMMENDED READING/BIBLIOGRAPHY

    • Spiegel, M.R., Schiller, L.J.(1999), Statistics, McGraw Hill, NYC
    • Montgomery, D.C. (2000), Design and Analysis of Experiments, John Wiley & Sons, New York
    • Ralph C. Smith (2013), Uncertainty Quantification: Theory, Implementation, and Applications
    • T.J. Sullivan (2015), Introduction to Uncertainty Quantification”, Springer
    • Ghanem, Higdon, Owhadi, (2017), Handbook of Uncertainty Quantification, Springer
    • Souza de Cursi, Sampaio, (2015), Uncertainty Quantification and Stochastic Modeling with Matlab, ISTE Press – Elsevier
    • L. Eriksson, E. Johansson, N. Kettaneh-Wold, C. Wikström, and S. Wold (2001), Design of Experiments
    • Marler, R. T., and Arora, J. S., 2004 “Survey of Multi-Objective Optimization Methods for Engineering” Structural and Multidisciplinary Optimization, 26(6), pp.269.295
    • Myers, R. H., and Montgomery, D. C., 2002, “Response Surface Methodology: Process and Product Optimization Using Designed Experiments” John Wiley & Sons Inc, USA.
    • Law, A.l., Kelton, W. D., 1991, “Simulation Modeling and Analysis”, Mc Graw Hill
    • Mäkelä, M., 2017, “Experimental Design and Response Surface Methodology in Energy Applications: A Tutorial Review” Energy Conversion and Management, 151(May), pp. 630–640.
    • Kleijnen, J. P. C., 2005, “An Overview of the Design and Analysis of Simulation Experiments for Sensitivity Analysis” Eur. J. Oper. Res., 164(2), pp. 287–300.
    • Tagushi, G., Yokoyama, Y., and Wu, Y., 1993, “Taguchi Methods: Design of Experiments”
    • K. Kim, M.R. von Spakovsky, M. Wang, D.J. Nelson, 2012. “Dynamic optimization under uncertainty of the synthesis/design and operation/control of a proton exchange membrane fuel cell system, Journal of Power Sources, 205, pp. 252–263
    • Navarro, M., Witteveen, J., Blom, J., 2014. “Polynomial chaos expansion for general multivariate distributions with correlated variables”
    • Seshadri, P., Narayan, A., Mahadevan, S., 2016. “Effectively Subsampled Quadratures For Least Squares Polynomial Approximations”. ArXiv e-prints
    • Giunta, A.A., Eldred, M.S., Castro, J.P., 2004. “Uncertainty quantification using response surface approximations”, 9th ASCE Specialty Conference on Probabilistic Mechanics and Structural Reliability
    • M. Cavazzuti, Optimization Methods: From Theory to Design, DOI: 10.1007/978-3-642-31187-1_3, Springer-Verlag Berlin Heidelberg, 2013.

    TEACHERS AND EXAM BOARD

    Exam Board

    ALBERTO TRAVERSO (President)

    LUCA MANTELLI

    ALESSANDRO SORCE (President Substitute)

    LESSONS

    Class schedule

    All class schedules are posted on the EasyAcademy portal.

    EXAMS

    EXAM DESCRIPTION

    The first part on robust design requires a report containing: one chapter on theoretical topic of the course, one exercise on algebric equation, one exercise on engineering application.

    The second part on monitoring and diagnostics requires an oral exam.

    ASSESSMENT METHODS

    Written and oral exam

    Exam schedule

    Date Time Location Type Notes
    10/01/2023 09:30 SAVONA Orale
    12/01/2023 09:30 GENOVA Orale
    23/01/2023 09:30 SAVONA Orale
    26/01/2023 09:30 GENOVA Orale
    16/02/2023 09:30 GENOVA Orale
    08/06/2023 09:30 GENOVA Orale
    22/06/2023 09:30 GENOVA Orale
    07/07/2023 09:30 GENOVA Orale
    20/07/2023 09:30 GENOVA Orale
    07/09/2023 09:30 GENOVA Orale