Advanced Set Bounding Methods for Fault Detection

Advanced Set Bounding Methods for Fault Detection

Ritsma, Folkert (TU Delft Mechanical, Maritime and Materials Engineering)

Ferrari, Riccardo (mentor)
Al-Ars, Zaid (mentor)

Delft University of Technology

Mechanical Engineering | Systems and Control

2019-12-06

Performance of set based fault detection is highly dependent on the complexity of the set bounding methods used to bound the healthy residual set. Existing methods achieve robust performance with complex set bounding that narrowly define healthy system behavior, yet at the cost of higher computation times. In this thesis a major improvement is reached in both accuracy and computation time by applying machine learning methods to set bounding. A method is developed which achieves fault detection at several orders of magnitude the speed of an existing set based fault detection method without sacrificing a robust performance.

Fault Detection
Machine Learning
Anomaly Detection
Outlier Detection
Support Vector Machines
Model Based Fault Detection
Set Based Fault Detection

http://resolver.tudelft.nl/uuid:b6bad7a5-0afd-4268-873d-32a4a18b4281

Student theses

master thesis

© 2019 Folkert Ritsma