FAULT DIAGNOSIS BASED ON LMD-SVD AND INFORMATION-GEOMETRIC SUPPORT VECTOR MACHINE FOR HYDRAULIC PUMPS

Abstract

This study proposes a fault diagnosis method for hydraulic pumps based on local mean decomposition (LMD), singular value decomposition (SVD), and information-geometric support vector machine (IG-SVM). First, the nonlinear and non-stationary vibration signals are decomposed using LMD into several product functions (PFs). Then, the PFs are processed by SVD to obtain more stable and compact feature vectors. Finally, the health states are identified by an IG-SVM classifier, which is less-dependent on the selected kernel function and parameters than SVM. In addition, the comparisons between LMD, EMD, and WPD demonstrate the superiority of LMD in feature extraction. Compared with SVM and BP neural network, IG-SVM shows higher classification accuracy and computational efficiency in dealing with small-sample fault diagnosis. From the experimental results, it was concluded that the proposed method can effectively realize fault diagnosis for hydraulic pumps under small-sample conditions.