Study on Immune Relevant Vector Machine Based Intelligent Fault Detection and Diagnosis Algorithm

Abstract

An immune relevant vector machine (IRVM) based intelligent classification method is proposed by combining the random realvalued negative selection (RRNS) algorithm and the relevant vector machine (RVM) algorithm. The method proposed is aimed to handle the training problem of missing or incomplete fault sampling data and is inspired by the "self/nonself " recognition principle in the artificial immune systems. The detectors, generated by the RRNS, are treated as the "nonself " training samples and used to train the RVM model together with the "self " training samples. After the training succeeds, the "nonself " detection model, which requires only the "self " training samples, is obtained for the fault detection and diagnosis. It provides a general way solving the problems of this type and can be applied for both fault detection and fault diagnosis. The standard Fisher's Iris flower dataset is used to experimentally testify the proposed method, and the results are compared with those from the support vector data description (SVDD) method. Experimental results have shown the validity and practicability of the proposed method.