Fault Detection and Diagnosis Techniques


Recommended prerequisite for participation in the module

Probability, statistics and stochastic processes, system identification and estimation

Content, progress and pedagogy of the module


  • To contribute to students’ attainment of comprehension of some typical fault detection and diagnosis techniques.


  • 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


  • Have comprehension of some typical model-free fault detection and diagnosis methods
  • Have comprehension of some typical model-based fault detection and diagnosis methods


  • 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


  • 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



Name of examFault Detection and Diagnosis Techniques
Type of exam
Written or oral exam
Assessment7-point grading scale
Type of gradingInternal examination
Criteria of assessmentThe criteria of assessment are stated in the Examination Policies and Procedures

Facts about the module

Danish titleFejlfinding og diagnosticeringsteknikker
Module codeN-IRS-K2-3
Module typeCourse
Duration1 semester
Language of instructionEnglish
Empty-place SchemeYes
Location of the lectureCampus Esbjerg
Responsible for the module


Education ownerMaster of Science (MSc) in Engineering (Intelligent Reliable Systems)
Study BoardStudy Board of Build, Energy, Electronics and Mechanics in Esbjerg
DepartmentDepartment of Energy
FacultyThe Faculty of Engineering and Science