A combined diagnosis system design using model-based and data-driven techniques
A hybrid diagnosis system design is proposed that combines model-based
and data-driven diagnosis methods for fault isolation. A set of
residuals are used to detect if there is a fault in the system and a
consistency-based fault isolation algorithm is used to compute all
diagnosis candidates that can explain the triggered residuals. To
improve fault isolation, diagnosis candidates are ranked by evaluating
the residuals using a set of one-class support vector machines trained
using data from different faults. The proposed diagnosis system design
is evaluated using simulations of a model describing the air-flow in
an internal combustion engine.
Daniel Jung, Kok Ng, Erik Frisk and Mattias Krysander
Conference on Control and Fault-Tolerant Systems (SysTol'16),
2016
Informationsansvarig: webmaster
Senast uppdaterad: 2021-11-10
LiU Homepage
