Recommended prerequisite for participation in the module

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