  Data-driven fault diagnosis is complicated by unknown fault classes and
limited training data from different fault realizations. In these situations,
conventional multi-class classification approaches are not suitable for fault
diagnosis. One solution is the use of anomaly classifiers that are trained
using only nominal data. Anomaly classifiers can be used to detect when a fault
occurs but give little information about its root cause. Hybrid fault diagnosis
methods combining physically-based models and available training data have
shown promising results to improve fault classification performance and
identify unknown fault classes. Residual generation using grey-box recurrent
neural networks can be used for anomaly classification where physical insights
about the monitored system are incorporated into the design of the machine
learning algorithm. In this work, an automated residual design is developed
using a bipartite graph representation of the system model to design grey-box
recurrent neural networks and evaluated using a real industrial case study.
Data from an internal combustion engine test bench is used to illustrate the
potentials of combining machine learning and model-based fault diagnosis
techniques.
