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Author:

Yuan, Yongtao (Yuan, Yongtao.) | Zhang, Xiaojun (Zhang, Xiaojun.) | Miao, Yang (Miao, Yang.)

Indexed by:

EI

Abstract:

Cavitations which is one of the worst faults forseawater hydraulic pump should be diagnosed and prognosed. In this paper, a improved Hilbert-Huang Transform ( HHT) is proposed to extract faults feature of seawater hydraulic pump's vibration signal; vibration signal is decomposed into several Intrinsic Mode Function (IMF) based on Ensemble Empirical Mode Decomposition ( EEMD ).Then IMFincluding the features of failure are extracted into fault feature vectors by HHT. Prognosis model is established based on support vector machine (SVM) method. Fault feature vectors are acted as inputs of the model to predict cavitations of seawater hydraulic pump. It is shown that the method of this paper is effective. © 2019 IEEE.

Keyword:

Mathematical transformations Support vector machines Fluid Power Cavitation Seawater Signal processing Pumps

Author Community:

  • [ 1 ] [Yuan, Yongtao]Beijing University of Technology, Beijing Key Laboratory of Advanced Manufacturing Technology, Beijing, China
  • [ 2 ] [Zhang, Xiaojun]Beijing University of Technology, Beijing Key Laboratory of Advanced Manufacturing Technology, Beijing, China
  • [ 3 ] [Miao, Yang]Beijing University of Technology, Beijing Key Laboratory of Advanced Manufacturing Technology, Beijing, China

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Source :

Year: 2019

Page: 426-431

Language: English

Cited Count:

WoS CC Cited Count: 0

SCOPUS Cited Count:

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 1

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