Multi-Source Domain Adaptation for Cross-Domain Fault Diagnosis of Chemical Processes

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.