Fault Diagnosis of Hydraulic Syetem Based on Neural Network

Hong Bin Tang; Yun Xin Wu

doi:10.4028/www.scientific.net/AMM.48-49.515

Paper Titles

Embedded System Based Television Data Collection and Return Technology
p.496

Exponential Stability for a Stochastic Competitive Population System with Age-Structure
p.502

Fast Nondestructive Measurement and Discrimination of Tablet Based on Vis/NIR Spectroscopy and Chemometrics
p.506

Fault Detection of Excavator’s Hydraulic System Using Dynamic General Regression Neural Network
p.511

Fault Diagnosis of Hydraulic Syetem Based on Neural Network
p.515

Fault Diagnosis with Fuzzy Expert System
p.519

Finite Element Analysis on Dynamic Tension of Quad-Bundle Conductor of Transmission Line Galloping
p.523

Finite Element Simulation of Large Gap Magnetic Drive System
p.527

Simulation of the Unloading Pressure and Recovery Pressure of Internal Unloading Valve for Emulsion Pump Station
p.531

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 48-49Fault Diagnosis of Hydraulic Syetem Based on...

Fault Diagnosis of Hydraulic Syetem Based on Neural Network

Abstract:

Because neural network has the advantages of fast parallel processing, associative memory, self-organizing and self-learning, it is widely applied in the fault diagnosis of hydraulic system. Present in this paper is a fault diagnosis approch to a typical failure in hydraulic system which is leakage of hydraulic cylinder.The fault diagnosis approch is based on monitoring preesure singal,time domain feature and neural network. According to the method, the time domain feature is extracted from the pressure singal and costitutes the eigenvectors at first, then these eigenvectors are input into neural network to identify faults. The experimental results show that three modes of no leakage, slighter leakage and severe leakage are correctly identified and it can be used in the fault diagnosia of hydraulic syetem.

You might also be interested in these eBooks

Measuring Technology and Mechatronics Automation

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volumes 48-49)

Pages:

515-518

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.48-49.515

Citation:

Cite this paper

Online since:

February 2011

Authors:

Hong Bin Tang, Yun Xin Wu

Keywords:

Fault Diagnosis, Hydraulic System, Neural Network (NN), Time Domain Feature

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] TOTTN G E. Handbook of hydraulic fluid technology[M]. Marcel Dekker, (2000).

Google Scholar

[2] Zhou Rusheng, Jiao Zongxia. Current research and developing trends on fault diagnosis of hydraulic systems[J]. Chinese Journal of Mechanical Engineering, 2006, 42(9): 6-14.

Google Scholar

[3] Teodor Marcu. Design of fault detection for a hydraulic looper using dynamic neural networks[J]. Control Engineering Practice, 2007, 10(1016): 1-22.

DOI: 10.1016/j.conengprac.2006.11.017

Google Scholar

[4] Wang Xuanyin, Li Xiaoxiao, Li Fushang. Analysis on oscillation in electro-hydraulic regulating system of steam turbine and fault diagnosis based on PSOBP[J]. Expert Systems with Applications, 2010, 37: 3887-3892.

DOI: 10.1016/j.eswa.2009.11.029

Google Scholar

[5] He Xiangyu, He Qinghua. Fault Diagnosis Approach of Hydraulic System Using RBF Network and ARX Model[J]. Journal of System Simulation, 2009, 21(1): 282-285.

Google Scholar

[6] NARENDRA K S, PARTHASARATHY K. Identification and control of dynamical systems using neural networks[J]. IEEE Trans. on Neural Networks, 1990, 1(1): 4-27.

DOI: 10.1109/72.80202

Google Scholar