Adaptive Fault Detection and Diagnosis Using Parsimonious Gaussian Mixture Models Trained with Distributed Computing Techniques

Abstract

After a great advance by the industry on processes automation, an important challenge still remains: the automation under abnormal situations. The first step towards solving this challenge is the Fault Detection and Diagnosis (FDD). This work proposes a batch-incremental adaptive methodology for fault detec- tion and diagnosis based on mixture models trained on a distributed computing environment. The models used are from a family of Parsimonious Gaussian Mix- ture Models (PGMM), in which the reduced number of parameters of the model brings important advantages when there are few data available, an expected sce- nario of faulty conditions. On the other side, a large number of different models rises another challenge, the best model selection for a given behaviour. For that, it is proposed to train a large number of models, using distributed computing techniques, for only then selecting the best model. The work proposes the us- age of the Spark framework, ideal for iterative computations. The proposed methodology was validated in a simulated process, the Tennessee Eastman Pro- cess (TEP), showing good results for both the detection and the diagnosis of faults. Furthermore, numeric experiments show the viability of training a large number of models for the best model selection a posteriori.