| TSFS06 |
Diagnosis and Supervision, 6 ECTS credits.
/Diagnos och övervakning/
For:
D
I
Ii
IT
M
Y
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Prel. scheduled
hours: 54
Rec. self-study hours: 106
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Area of Education: Technology
Main field of studies: Electrical Engineering
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Advancement level
(G1, G2, A): A
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Aim:
To give both a theoretical and practical basis for how to design systems
that automatically detect and isolate faulty components in technical
processes.
After the course is finished, the student shall:
- know why diagnosis systems are used in different industrial application areas.
- know how to analyze which faults in a complex process that need to be supervised to achieve the overall goals.
- from a case description be able to structure the problem and develop principle and architechture for a complete implementation of a diagnosis system.
- given a formal model description be able to choose suitable mathemematical methods to solve the problem.
- know advantages and disadvantages of the different methods that are included in the course.
- be able to apply mathematical tools and methods from a variety of
previous courses to solve diagnosis problems.
- be able to value and verify functionality and performance of a complete diagnosis system.
- have a broad theoretical insight in the subject, deep enough to be able to understand and utilize new research results developed by the research community.
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Prerequisites: (valid for students admitted to programmes within which the course is offered)
Automatic Control, Probability
Note: Admission requirements for non-programme students usually also include admission requirements for the programme and threshhold requirements for progression within the programme, or corresponding.
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Organisation:
The course is organized in lectures, problem solving sessions, and laborations.
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Course contents:
- Introduction: history and overview, practical application examples.
- Principles for model based diagnosis: mathematical modelling of
faults, detection and isolation of faults by means of models, consistency relations, analytical redundancy, decisions with structured hypothesis tests.
- Control theory methods: linear and non-linear residual generation, observers and Kalman filters for diagnosis, residual evaluation, adaptive thresholding, statistical methods.
- Logic based AI methods: basic principles, fault isolation algorithms.
- Probability based diagnosis and Bayesian networks.
- Other: fault trees and FMEA, statistical methods/change detection.
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Course literature:
Compendium "Model Based Diagnosis of Technical Processes" by
Mattias Nyberg and Erik Frisk with companion exercise material.
Excerpt from the book "Detection of abrupt changes" by Michele Basseville and Igor Nikiforov.
Laboration material.
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Examination: |
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A written examination
Laboratory work
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4,5 ECTS
1,5 ECTS
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See also course homepage for further practical course information. |
Course language is Swedish.
Department offering the course: ISY.
Director of Studies: Johan Löfberg
Examiner: Erik Frisk
Link to the course homepage at the department
Course Syllabus in Swedish
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