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Fault detection and diagnosis of technical systems, 6 credits
Feldetektion och diagnos av tekniska system, 6 hp
TSFS22
Main field of study
Electrical EngineeringCourse level
Second cycleCourse type
Programme courseExaminer
Daniel JungDirector of studies or equivalent
Johan LöfbergEducation components
Preliminary scheduled hours: 52 hRecommended self-study hours: 108 h
Available for exchange students
YesECV = Elective / Compulsory / Voluntary
The course syllabus is preliminary
Main field of study
Electrical EngineeringCourse level
Second cycleAdvancement level
A1XCourse offered for
- Master of Science in Information Technology
- Master of Science in Computer Science and Software Engineering
- Master of Science in Computer Science and Engineering
- Master of Science in Applied Physics and Electrical Engineering - International
- Master of Science in Mechanical Engineering
- Master of Science in Applied Physics and Electrical Engineering
Prerequisites
Automatic Control, Probability theory
Intended learning outcomes
To give both a theoretical and practical basis for how to design systems that automatically detect and isolate faulty components in technical processes.
After completing the course, the student shall be able to:
- Based on a mathematical model of a technical process, apply model-based methods to analyze diagnostic performance and to detect and isolate faults.
- Based on historical data from a technical process apply data-driven methods to detect and classify faults.
Course content
1. Introduction to fault diagnosis, design of diagnostic systems, examples of industrial applications.
2. Mathematical modeling for fault detection and fault isolation using models, consistency relations, analytical redundancy.
3. Structural methods for fault diagnosis, bipartite graphs, modeling for structural analysis, matching, analysis of structural diagnosis properties, algorithms for finding overdetermined equation sets for residual generation.
4. Linear and nonlinear residual generation, observers and Kalman filters for diagnosis.
5. Statistical methods for fault detection.
6. Fault isolation, decisions with structured hypothesis tests, minimal hitting set.
7. Data-driven fault diagnosis, anomaly detection, classification.
8. Hybrid fault diagnosis combining model-based and data-driven diagnosis methods.
Teaching and working methods
The course is organized in lectures, problem solving sessions, and laborations.
Examination
| LAB1 | Laboratory work | 2 credits | U, G |
| DAT1 | Computer exam | 4 credits | U, 3, 4, 5 |
Grades
Four-grade scale, LiU, U, 3, 4, 5Department
Institutionen för systemteknikCourse literature
Compendia
- Mattias Nyberg och Erik Frisk, Model Based Diagnosis of Technical Processes
| Code | Name | Scope | Grading scale |
|---|---|---|---|
| LAB1 | Laboratory work | 2 credits | U, G |
| DAT1 | Computer exam | 4 credits | U, 3, 4, 5 |
Compendia
Mattias Nyberg och Erik Frisk, Model Based Diagnosis of Technical Processes
Note: The course matrix might contain more information in Swedish.
I = Introduce, U = Teach, A = Utilize
| I | U | A | Modules | Comment | ||
|---|---|---|---|---|---|---|
| 1. DISCIPLINARY KNOWLEDGE AND REASONING | ||||||
| 1.1 Knowledge of underlying mathematics and science (G1X level) |
|
|
X
|
DAT1
|
calculus, algebra, probability theory |
|
| 1.2 Fundamental engineering knowledge (G1X level) |
|
|
X
|
DAT1
|
signal processing, logics |
|
| 1.3 Further knowledge, methods, and tools in one or several subjects in engineering or natural science (G2X level) |
|
|
X
|
LAB1
DAT1
|
automatic control, modeling |
|
| 1.4 Advanced knowledge, methods, and tools in one or several subjects in engineering or natural sciences (A1X level) |
|
X
|
|
LAB1
DAT1
|
fault diagnosis, machine learning |
|
| 1.5 Insight into current research and development work |
X
|
|
|
Information on current research activities |
||
| 2. PERSONAL AND PROFESSIONAL SKILLS AND ATTRIBUTES | ||||||
| 2.1 Analytical reasoning and problem solving |
|
X
|
|
LAB1
DAT1
|
Engineering trade-offs during design of diagnosis systems |
|
| 2.2 Experimentation, investigation, and knowledge discovery |
|
X
|
|
LAB1
DAT1
|
Laborations using real data and realistic models. Comparing different methods for fault diagnosis |
|
| 2.3 System thinking |
|
X
|
|
comprehensive view on design of diagnosis systems |
||
| 2.4 Attitudes, thought, and learning |
|
|
X
|
LAB1
DAT1
|
Work during exercise sessions and laborations |
|
| 2.5 Ethics, equity, and other responsibilities |
|
|
|
|||
| 3. INTERPERSONAL SKILLS: TEAMWORK AND COMMUNICATION | ||||||
| 3.1 Teamwork |
|
|
X
|
group work |
||
| 3.2 Communications |
|
|
X
|
LAB1
|
written lab reports |
|
| 3.3 Communication in foreign languages |
|
|
|
|||
| 4. CONCEIVING, DESIGNING, IMPLEMENTING AND OPERATING SYSTEMS IN THE ENTERPRISE, SOCIETAL AND ENVIRONMENTAL CONTEXT | ||||||
| 4.1 External, societal, and environmental context |
X
|
|
|
Examples of industrial applications |
||
| 4.2 Enterprise and business context |
|
|
|
|||
| 4.3 Conceiving, system engineering and management |
|
X
|
|
LAB1
DAT1
|
Modeling, analysis of diagnosis properties |
|
| 4.4 Designing |
|
X
|
|
LAB1
DAT1
|
Design of diagnosis systems |
|
| 4.5 Implementing |
|
X
|
|
LAB1
|
Implementation of algorithms |
|
| 4.6 Operating |
X
|
|
|
Laborations using data from realistic problem scenarios |
||
| 5. PLANNING, EXECUTION AND PRESENTATION OF RESEARCH DEVELOPMENT PROJECTS WITH RESPECT TO SCIENTIFIC AND SOCIETAL NEEDS AND REQUIREMENTS | ||||||
| 5.1 Societal conditions, including economic, social, and ecological aspects of sustainable development for knowledge development |
|
|
|
|||
| 5.2 Economic conditions for knowledge development |
|
|
|
|||
| 5.3 Identification of needs, structuring and planning of research or development projects |
|
|
|
|||
| 5.4 Execution of research or development projects |
|
|
|
|||
| 5.5 Presentation and evaluation of research or development projects |
|
|
|
|||
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