Model-based Dependability Analysis for Mechatronic Systems
LV MDA
Important:
This course will be in English. Most probably, all lectures will be online, the form and appointments will be discussed in the introductory meeting.
If you cannot attend the introductory meeting, please contact Dr. Andrey Morozov via E-Mail: andrey.morozov@tu-dresden.de
The goal of the course (Ziel des Lehrfaches)
Model-based System Engineering (MBSE) is widely accepted in a variety of safety-critical industrial domains including aerospace and industrial automation. Recent trends in technology, such as Industry 4.0, Cyber-Physical Systems, and Internet-of-Things, significantly increase the interest of this topic. MBSE implies an automated process of system development from a semi-formal system specification up to the final implementation. MBSE is supported by software for the formulation of system requirements, detailed design, and even automated implementation. This helps both to simplify and speed up system development and provide information for earlier system analysis. Modern standards for high-tech software and hardware systems demand a high level of dependability properties (such as reliability, safety, resilience) that cannot be achieved without the thorough comprehension of structural and behavioral aspects of these highly heterogeneous systems and their components. This course provides an overview of modern MBSE approaches (UML/SysML, Simulink, AADL), key dependability metrics (MTTF, FIT, Failure rate), classical reliability and safety evaluation methods (FTA, ETA, RBD, FMEA), as well as advanced methods based on stochastic models such as Markov Chains and Stochastic Petri Nets and Monte Carlo simulations.
Content of the course (Inhalt des Lehrfaches)
8 Lectures + 4 Exercises + Project
Lectures:
- Safety-critical mechatronic and Cyber-Physical Systems (CPS), model-based system engineering
- Dependability theory (reliability, safety, security, resilience)
- Metrics and method for reliability and safety analysis (RBD, ETA, FTA, FMEA)
- Fault tolerance and anomaly detection techniques
- Model checking and stochastic models (Markov Chains, Stochastic Petri Nets)
- Data error propagation analysis
- Timing analysis of distributed components
- Key challenges of analytical and simulative approaches
Exercises:
- Model-based design of a mechatronic system (SysML or AADL)
- Fault tolerance and reliability analysis (Static and Dynamic Fault Trees)
- Analysis of data errors propagation (ErrorPro)
- Analysis of timing errors (Stochastic Model Checking)
Project:
Each group (2-3 students) designs a model of a simplified mechatronic system and performs model-based dependability analysis using the methods introduced in the lectures and demonstrated in the exercises. Each group will make a 15 minutes' final presentation.
Prior knowledge:
Basics of system Design, Finite State Machines, Petri Nets, UML (recommended)
LV: Model-based Dependability Analysis for Mechatronic Systems
Modulname: |
Industrielle Automatisierungstechnik - Aufbaumodul |
|
Modulnummer: |
ET-12 01 11 |
WF 1/1/0 |
Lehrbeauftragte: |
Dr.-Ing. A. Morozov |
|
Important:
- This course in SS2020 will be in English. Most probably, all lectures will be online, the form and appointments will be discussed in the introductory meeting.
- Everyone who is interested, please attend the introductory meeting 27.04, 17:00 @ online.
- Lectures: Next meeting https://global.gotomeeting.com/join/143502733
- Exercises:
If you cannot attend the introductory meeting, please contact Dr. Andrey Morozov via E-Mail: andrey.morozov@tu-dresden.de
The goal of the course (Ziel des Lehrfaches)
Model-based System Engineering (MBSE) is widely accepted in a variety of safety-critical industrial domains including aerospace and industrial automation. Recent trends in technology, such as Industry 4.0, Cyber-Physical Systems, and Internet-of-Things, significantly increase the interest of this topic. MBSE implies an automated process of system development from a semi-formal system specification up to the final implementation. MBSE is supported by software for the formulation of system requirements, detailed design, and even automated implementation. This helps both to simplify and speed up system development and provide information for earlier system analysis. Modern standards for high-tech software and hardware systems demand a high level of dependability properties (such as reliability, safety, resilience) that cannot be achieved without the thorough comprehension of structural and behavioral aspects of these highly heterogeneous systems and their components. This course provides an overview of modern MBSE approaches (UML/SysML, Simulink, AADL), key dependability metrics (MTTF, FIT, Failure rate), classical reliability and safety evaluation methods (FTA, ETA, RBD, FMEA), as well as advanced methods based on stochastic models such as Markov Chains and Stochastic Petri Nets and Monte Carlo simulations.
Content of the course (Inhalt des Lehrfaches)
8 Lectures + 4 Exercises + Project
Lectures:
- Safety-critical mechatronic and Cyber-Physical Systems (CPS), model-based system engineering
- Dependability theory (reliability, safety, security, resilience)
- Metrics and method for reliability and safety analysis (RBD, ETA, FTA, FMEA)
- Fault tolerance and anomaly detection techniques
- Model checking and stochastic models (Markov Chains, Stochastic Petri Nets)
- Data error propagation analysis
- Timing analysis of distributed components
- Key challenges of analytical and simulative approaches
Exercises:
- Model-based design of a mechatronic system (SysML or AADL)
- Fault tolerance and reliability analysis (Static and Dynamic Fault Trees)
- Analysis of data errors propagation (ErrorPro)
- Analysis of timing errors (Stochastic Model Checking)
Project:
Each group (2-3 students) designs a model of a simplified mechatronic system and performs model-based dependability analysis using the methods introduced in the lectures and demonstrated in the exercises. Each group will make a 15 minutes' final presentation.
Prior knowledge:
Basics of system Design, Finite State Machines, Petri Nets, UML (recommended)