Multi-Source Domain Adaptation for Cross-Domain Fault Diagnosis of
  Chemical Processes
Eduardo Fernandes Montesuma Michela Mulas Fred Ngolè Mboula Francesco Corona Antoine Souloumiac
Fault diagnosis is an essential component in process supervision. Indeed, it
determines which kind of fault has occurred, given that it has been previously
detected, allowing for appropriate intervention. Automatic fault diagnosis
systems use machine learning for predicting the fault type from sensor
readings. Nonetheless, these models are sensible to changes in the data
distributions, which may be caused by changes in the monitored process, such as
changes in the mode of operation. This scenario is known as Cross-Domain Fault
Diagnosis (CDFD). We provide an extensive comparison of single and multi-source
unsupervised domain adaptation (SSDA and MSDA respectively) algorithms for
CDFD. We study these methods in the context of the Tennessee-Eastmann Process,
a widely used benchmark in the chemical industry. We show that using multiple
domains during training has a positive effect, even when no adaptation is
employed. As such, the MSDA baseline improves over the SSDA baseline
classification accuracy by 23% on average. In addition, under the
multiple-sources scenario, we improve classification accuracy of the no
adaptation setting by 8.4% on average.