# 2022/2023

## 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

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 exam ECTS 5 Assessment 7-point grading scale Type of grading Internal examination Criteria of assessment The criteria of assessment are stated in the Examination Policies and Procedures