Intelligent fault diagnosis of plunger pump in truck crane based on a hybrid fault diagnosis scheme

Abstract

At the initial stage of the mechanism, the collected samples are always in actual state, and the signals in fault conditions are gathered after a certain running time, so the general fault diagnosis model cannot be trained effectively. In this paper, a hybrid fault diagnosis scheme for pump in truck crane was proposed based on particle swarm optimization (PSO) SVDD and DBI K-Cluster method. Firstly, the SVDD procedure was constructed with the data in actual state, and the model parameters were optimized with PSO algorithm. Secondly, when the total number of novelty samples reached a given threshold, the K-Cluster method was utilized to classify the collected samples and the labels were allocated. In this procedure, the number of the class was determined with the Davies Bouldin index (DBI). Finally, each class data was trained with SVDD, and a whole diagnosis model was constructed with all the two-class classifiers. For the multi-fault mode samples of the pump in truck crane, experiments show that a promising classification performance is achieved.