Fault diagnosis for hydraulic pump based on EEMDKPCA and LVQ

摘要：

　　Hydraulic pump is regarded as the heart of hydraulic system.Achieving the real-time fault diagnosis of hydraulic pump is of great importance for the maintenance of the entire system.An accurate fault clustering solution with self-adaptive signal processing is needed for extracting performance degradation information hidden in the nonlinear and non-stationary signals of hydraulic pumps.Therefore, a fault diagnosis approach based on ensemble empirical mode decomposition (EEMD), kernel principal component analysis (KPCA), and learning vector quantization (LVQ) network is proposed in this study.First, EEMD is employed to acquire more significant intrinsic mode functions (IMFs), thus overcoming the drawback of empirical mode decomposition, and further extracting the energy values of each IMF to form the feature vector.Second, KPCA, a nonlinear dimension reduction method, is used to remove redundancies of the extracted feature vector for high accuracy of fault diagnosis.Finally, LVQ is employed to classify faults based on the reduced feature vector.The efficiency and accuracy of the proposed method is validated by a case study based on the vibration dataset of a plunger pump.

关键词： hydraulic pump fault diagnosis ensemble empirical mode decomposition kernel principal component analysis learning vector quantization

作者: Chao Wang Zili Wang Jian Ma Hang Yuan

作者单位: Science and Technology on Reliability and Environmental Engineering Laboratory, Beijing 100191, Chin School of Reliability and Systems Engineering, Beihang University, Beijing 100191, China

会议类型: 国际会议

会议名称: the International Conference Vibroengineering-2014

会议地点: 贵阳

会议语种:英文

页码: 188-193

在线出版日期: 2014-11-07（万方平台首次上网日期，不代表论文的发表时间）