OVERVIEW

The course provides the fundamentals for robust design of engineering systems and related
procedures for monitoring and diagnostics, through deterministic and stochastic approaches.
Practical examples are derived from the fields of mechanical engineering and energy systems.

AIMS AND CONTENT

LEARNING OUTCOMES

The module 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

The course aims to illustrate methods for designing and monitoring performance under conditions of uncertainty, with specific case studies relating to mechanical and energy systems. The first part of the course will cover the necessary fundamentals of statistics. Various methods for quantifying uncertainty will then be presented, starting with a sampling method such as Monte Carlo and continuing with various approximate methods. An overview of so-called “robust design” will then be presented, focusing on the application of the uncertainty quantification method in optimization problems. In the second part of the course, advanced techniques for data-based monitoring will be presented. Both robust design methods and diagnostic and monitoring methods will be applied to various case studies.

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

PREREQUISITES

basic analytical skills, equation handling, basic computer coding skills

TEACHING METHODS

Lectures given by professors to explain theoretical concepts and demonstrate their application to engineering and statistical case studies.

Individual or group exercises on PCs using Microsoft Excel and MATLAB.

Attendance is not compulsory.

Students with valid certifications for Specific Learning Disorders (SLDs), disabilities or other educational needs are invited to contact the teacher and the School's contact person for disability at the beginning of teaching to agree on possible teaching arrangements that, while respecting the teaching objectives, take into account individual learning patterns. Contacts of the teacher and the School's disability contact person can be found at the following link Comitato di Ateneo per l’inclusione delle studentesse e degli studenti con disabilità o con DSA | UniGe | Università di Genova

SYLLABUS/CONTENT

Part A – Robust Design of Systems

Lecture 1-2

Course presentation: course structure and topics, examination procedures

Introduction to probability and statistics: definitions, set algebra, probability spaces, random variables (continuous and discrete), probability density function, probability mass function, expectation and variance, discrete (uniform) and continuous (uniform, normal, Weibull) distributions, multivariate concepts (random vectors, covariance, joint probability, Bayes’ theorem, independence).

Lecture 3

Uncertainty, Sensitivity and Analysis of Variance: definitions (verification and validation, errors, types of uncertainties), motivations, challenges and applications of uncertainty quantification (UQ), sensitivity analysis, analysis of variance (partitioning of the sum of squares, F-test).

Lecture 4

Uncertainty quantification and Monte Carlo simulation: forward and backward UQ analysis, intrusive and non-intrusive methods, sampling and approximate methods, Monte Carlo simulations, mean square pure error (MSPE) evolution analysis.

Lecture 5

Exercise on Monte Carlo simulation and MSPE analysis: application on cantilever beam in Matlab.

Lecture 6

Approximated Methods for UQ: Response sensitivity analysis (RSA), approximated RSA, numerical approximations of mean, variance and sensitivity, perturbation step choice, polynomial chaos.

Lecture 7

Exercise on approximated RSA and sensitivity analysis: application on cantilever beam in Matlab.

Lecture 8-9

Design of Experiment and robust design: Design of experiment (DoE), DoE techniques (full factorial, fractional factorial, central composite, latin hypercube), response surface methodology (RSM), deterministic optimization, deterministic optimization algorithms (generic line search, trust region, Newton, quasi‑Newton, simplex), constrained optimization, constrained optimization methods (Lagrange multipliers, penalty function, barrier function), solving techniques, stochastic optimization, multi-objective optimization, Pareto front, stochastic optimization algorithms (particle swarm, evolutionary and genetic), robust design analysis, multi-objective robust design optimization, reliability analysis.

Exercise on DoE and RSM: application on cantilever beam in Matlab.

Lecture 10

Real applications of UQ: application on cantilever beam in Matlab.

Presentation of assignments: Final assignments to be submitted for the examination.

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

LUCA MANTELLI (President)

ANDRIY VASYLYEV

ALESSANDRO SORCE (President Substitute)

LESSONS

LESSONS START

https://corsi.unige.it/en/node/190294

Class schedule

The timetable for this course is available here: Portale EasyAcademy

EXAMS

EXAM DESCRIPTION

The first part on robust design requires two reports describing analyses performed by the students with the aid of programming codes shared with them during the class.

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

Students with valid certifications for Specific Learning Disorders (SLDs), disabilities or other educational needs are invited to contact the teacher and the School's contact person for disability at the beginning of teaching to agree on possible teaching arrangements that, while respecting the teaching objectives, take into account individual learning patterns. Contacts of the teacher and the School's disability contact person can be found at the following link Comitato di Ateneo per l’inclusione delle studentesse e degli studenti con disabilità o con DSA | UniGe | Università di Genova

ASSESSMENT METHODS

Learning outcomes are assessed through the evaluation of written reports (for the robust design part) and the oral exam (for the monitoring and diagnostics part). Each learning outcome will be verified through specific questions and discussion of case studies, with particular attention to clarity of exposition, use of technical terminology, and the ability to apply the methods learned to real problems.

Exam schedule

FURTHER INFORMATION

Please contact the teacher for further information not included in the teaching unit description.