Prerequisite/Recommended prerequisite for
participation in the module
Probability, statistics and stochastic processes, system
identification and estimation
Content, progress and pedagogy of the
module
Purpose
- To contribute to students’ attainment of comprehension of some
typical fault detection and diagnosis techniques.
Content
- Fundamental concepts, terms and principles of FDD
- Fault modelling and analysis
- Fault types and classification
- Fault modelling
- Fault delectability
- Fault diagnosability
- Residual generation (I): Observer based FDD methods for
deterministic systems
- Review of observer theory
- Fault detection using single observer
- Fault diagnosis using a bank of observers
- Residual generation (II): Kalman filter based FDD methods for
stochastic systems
- Review of probability and stochastic processes
- Kalman filter theory
- Extended Kalman filter
- Fault detection using single Kalman filter
- Fault diagnosis using a bank of Kalman filters (Multiple Model
(MM) method)
- Fault diagnosis using a bank interactive Kalman filters
(Interactive Multiple Model (IMM) method)
- Fault diagnosis using a two-stage Kalman filter for additive
and multiplicative faults
- Robust residual generation (I): Unknown Input Observer (UIO)
method
- (complete) Disturbance decoupling principle
- UIO theory
- Robust FDD using UIO method
- Robust residual generation (II): Robust filtering method
- Disturbance attenuation principle
- Modelling uncertainties
- Introduction to robust filtering theory (H_infty optimal
control theory)
- Robust FDD using H_infty filtering method
- Residual evaluation
- Simple voting techniques
- Statistical testing approaches
- Likelihood function methods
- Probabilities of false alarm and miss
- FDD using Parity space approaches
- Delectability and diagnosability
- Parity space methods for FDD
- Parameter estimation based FDD methods
- Parametric fault characteristics
- FDD using parameter estimation (least-square methods)
- FDD using recursive system identification
methods
- Signal-based (model-free) FDD methods
- FDD using spectrum analysis
- FDD using short-timed Fourier transform and wavelet
transform
- FDD using some artificial intelligence
methods
Learning objectives
Knowledge
- Have comprehension of some typical model-free fault detection
and diagnosis methods
- Have comprehension of some typical model-based fault detection
and diagnosis methods
Skills
- Are able to apply the learned knowledge to handle some fault
detection and diagnosis problems.
- Are able to judge the usefulness of the set up methods
- Are able to relate the methods to applications in the
industry
Competences
- Independently be able to define and analyze scientific problems
within the area of fault detection and diagnosis.
- Independently be able to be a part of professional and
interdisciplinary development work within the area of fault
detection and diagnosis.
Type of instruction
The program is based on a combination of academic, problem-oriented
and interdisciplinary approaches and organized based on the
following work and evaluation methods that combine skills and
reflection: • Lectures • Classroom instruction • Project work •
Workshops • Exercises (individually and in groups) • Teacher
feedback • Reflection • Portfolio work
Extent and expected workload
Since it is a 5 ECTS course module, the work load is expected to
be 150 hours for the student
Exam
Exams
Name of exam | Fault Detection and Diagnosis Techniques |
Type of exam | Written or oral examination |
ECTS | 5 |
Permitted aids | With certain aids, see list below
Unless otherwise stated in the course description in Moodle, it is
permitted to bring all kinds of (engineering) aids including books,
notes and advanced calculators. If the student brings a computer,
it is not permitted to have access to the Internet and the teaching
materials from Moodle must therefore be down loaded in advance on
the computer. It is emphasized that no form of electronic
communication must take place. |
Assessment | 7-point grading scale |
Type of grading | Internal examination |
Criteria of assessment | As stated in the Joint Programme Regulations.
http://www.engineering.aau.dk/uddannelse/studieadministration/ |