Corrosion acoustic emission signal recognition based on relevance vector machine

The classification performance of the relevance vector machine (RVM) model is closely related to its associated kernel function parameter. The artificial bee colony algorithm (ABC), particle swarm optimization (PSO) and genetic algorithm (GA) are proposed to find the optimal parameter of the RVM model, and the performance of these methods had been compared. Based on the binary tree structure and one-against all method, the binary-classification RVM model is extended to establish a four-classification model. The tank bottom corrosion acoustic emission signals were recognized with the established model. The characteristics parameters of the acoustic emission signal and the frequency-domain parameters were selected as the input parameters of the model, and a good recognition was obtained.