|SCIENTIFIC DISCIPLINARY SECTOR||ING-IND/09|
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.
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.
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
Lectures and exercises on PC
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.
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.
Office hours: contact by mail firstname.lastname@example.org
ALBERTO TRAVERSO (President)
ALESSANDRO SORCE (President Substitute)
All class schedules are posted on the EasyAcademy portal.
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.