Fault Detection of Excavator’s Hydraulic System Using Dynamic General Regression Neural Network

Xiang Yu He; Shang Hong He

doi:10.4028/www.scientific.net/AMM.48-49.511

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 48-49Fault Detection of Excavator’s Hydraulic System...

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.

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Periodical:

Applied Mechanics and Materials (Volumes 48-49)

Pages:

511-514

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.48-49.511

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Online since:

February 2011

Authors:

Xiang Yu He, Shang Hong He

Keywords:

Fault Diagnosis, General Regression Neural Network (GRNN), Hydraulic System

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