Combining model-based diagnosis and data-driven anomaly classifiers for fault isolation
Machine learning can be used to automatically process sensor data and
create data-driven models for prediction and classification. However,
in applications such as fault diagnosis, faults are rare events and
learning models for fault classification is complicated because of
lack of relevant training data. This paper proposes a hybrid diagnosis
system design which combines model-based residuals with incremental
anomaly classifiers. The proposed method is able to identify unknown
faults and also classify multiple-faults using only single-fault
training data. The proposed method is verified using a physical model
and data collected from an internal combustion engine.
Daniel Jung, Kok Ng, Erik Frisk and Mattias Krysander
Control Engineering Practice,
2018
Informationsansvarig: webmaster
Senast uppdaterad: 2021-11-10
LiU Homepage
