Paper Title:
Fault Detection of Excavator’s Hydraulic System Using Dynamic General Regression Neural Network
  Abstract

In order to improve reliability of excavator’s hydraulic system, a fault detection approach based upon dynamic general regression neural network (GRNN) approach was proposed. Dynamic GRNN is an extension of GRNN, which could effectively caputure the dynamic behavior of the nonlinear process. With this approach, normal samples were used as training data to develop a dynamic GRNN model in the first step. Secondly, this dynamic GRNN model performed as a fault determinant of the test fault. Experimental faults were used to validate the approach. Experimental results show that the proposed fault detection approach could effectively applied to the excavator’s hydraulic system.

  Info
Periodical
Edited by
Zhixiang Hou
Pages
511-514
DOI
10.4028/www.scientific.net/AMM.48-49.511
Citation
X. Y. He, S. H. He, "Fault Detection of Excavator’s Hydraulic System Using Dynamic General Regression Neural Network", Applied Mechanics and Materials, Vols. 48-49, pp. 511-514, 2011
Online since
February 2011
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