Gustavo B. Santi1, Arthur Assis2, Victor Rebli3, Patrick Marques Ciarelli4, Thomas W Rauber5, Celso Jose Munaro2
14:20 - 14:40 | Wed 24 Apr | Veleiros | WeB1.1
This work proposes a classification model for fault diagnosis. In a first stage, an unsupervised clustering algorithm discovers groups of potential fault classes. Subsequently, a specialized classifier is trained for each cluster, thus reducing the complexity and augmenting the performance. The quality of the diagnosis is further improved by combining the classifiers in a ensemble. As benchmarks, data provided by the Tennessee Eastman chemical plant simulator were used and the results were promising.