Fault diagnosis using the incremental learning algorithm with support vector machine
Document typeConference lecture
Rights accessOpen Access
To prevent process interruption and eventual losses, the need for a reliable fault detection and diagnosis system (FDD) is completely acknowledged. Besides the capability to recognize known faults automatically, a further requirement for a FDD is adaptability. If th e model cannot be adapted to deal with changes, variations due to external changes, decaying performance, Poisoning of catalyst etc. the FDD system could perform misleadingly. This paper presents an advantageous of incremental learning algorithm for fault d iagnosis, when a support vector machine algorithm are implemented as a classifier. The method which is followed in order to use the incremental learning algorithm is based on hyperplane - distance (HD)  . In the continues reactor which is studied, two cases are compared in order to clarify the role and importance of incremental learning algorithm. Result show t he effectiveness of this method
CitationArdakani, M., Graells, M., Escudero, G. Fault diagnosis using the incremental learning algorithm with support vector machine. A: European Congress of Chemical Engineering. "ECCE 10+ECAB 3+EPIC 5: 10th European Congress of Chemical Engineering: Nice, France: proceedings book". Niza: 2015.