Fault Detection and Diagnosis Techniques


Prerequisite/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 assessmentAs stated in the Joint Programme Regulations.

Facts about the module

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


Study BoardStudy Board of Energy
DepartmentDepartment of Energy Technology
FacultyFaculty of Engineering and Science