In order to realize the purpose of fault pattern recognition of hydraulic pump, the methods of fault feature extraction and pattern recognition are researched. Aiming at the bad performance of large scale sample entropy to reflect the state of pump as the scale of Multi-scale Sample Entropy (MSE) is larger and then the length of time series is shorter, the modified multi-scale entropy (MMSE) is proposed in this paper. The results obtained by adopting MMSE to practical signals of pump testified the favorable performance of it. Considering the nonlinear relationship between pump fault pattern and fault features, the Support Vector Machine (SVM) is used to realize the fault pattern recognition of pump and the Artificial Bee Colony (ABC) algorithm is proposed to optimize the parameters of SVM model. The fault pattern recognition of pump is realized based on the MMSE and ABCSVM, the favorable performance of proposed method is demonstrated with comparison and analysis.
LI Hongru1, MA Jiqiao1,2, WANG Yuku,1,YE Peng1.
Fault pattern recognition of Hydraulic Pump based on MMSE and ABCSVM[J]. Journal of Vibration and Shock, 2016, 35(9): 152-158
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