An EWT-PCA and Extreme Learning Machine Based Diagnosis Approach for Hydraulic Pump
Yu Ding, Liang Ma, Chao Wang, Laifa Tao
Abstract
Aiming at solving the problem that the fault characteristic of hydraulic pumps in the signal is weak, a fault diagnosis method combining empirical wavelet transform (EWT), principal component analysis (PCA) and extreme learning machine (ELM) is proposed. The vibration signal is firstly decomposed into several components that contain different frequency adaptively by EWT. After extracting features from the decomposed signals, the dimension of the feature vector is reduced by PCA. Finally, the dimensionally-reduced feature vectors that retain key fault characteristics are fed into ELM, which is a classifier with high learning speed and high generalization ability, to obtain the fault modes classification result. Experiments show that the proposed method can capture fault patterns of the hydraulic pump well and achieve high-accuracy fault diagnosis results.