Incremental learning fault detection algorithm based on hyperplane-distance

Abstract

Traditional methods for Fault Detection and Diagnosis (FDD) usually, consider that processes operate under a single steady condition, but because of several reasons (e.g.: equipment aging), operation conditions of industrial processes change continuously in practice. Under these new circumstances, the use of the originally tuned FDD system would cause false alarms and will reduce the fault classification performance. In this study, the Hyperplane-Distance Support Vector Machine (HD-SVM) method is exploited for process FDD to maintain FDD performance when it decays because of the ageing. Its effectiveness is shown through simulation studies on a CSTR reactor, for which an aging term is simulated by progressively decreasing the heat transfer coefficient (5%). This aging will lead to reduce the classification performance accordingly. Next, performance of HD-SVM, Traditional Incremental Learning (TIL) and Non-Incremental Learning (NIL) (using all data) are compared. The HD-SVM incremental learning is shown to reduce the training time of the classifier, while increasing the accuracy of the classifier. Therefore, HD-SVM is shown to cover the weaknesses of Traditional incremental learning algorithms to lose possible information and to improve classification performance in process FDD.

Postprint (author's final draft)