Document Details

Document Type : Article In Journal 
Document Title :
Fault Diagnosis of a Hydraulic Power System Using an Artificial Neural Network
تشخيص الأعطال في منظومة قدرة هيدروليكية باستخدام الشبكات العصبية الاصطناعية
 
Subject : Mechanical Engineering 
Document Language : English 
Abstract : This paper deals with the problem of fault detection, isolation and identification of a hydraulic power system. A proposed fault diagnostic scheme (FDS) using an artificial neural network (ANN) is investigated. A feedforward neural network is employed to diagnose two commonly occurring faults of the hydraulic power system: actuator internal leakage and valve spool blockage. The characterizing model of each fault is derived. The fault diagnostic scheme is applied to a hydraulic power test rig to diagnose real encountered faults. The ANN based FDS has been trained with sufficient data of the faults. Extensive experiments have been carried out and their results are presented and discussed. The experimental results have showed that the trained network has the capability to detect and identify various severity magnitudes of the faults of interest. Furthermore, the trained ANN based FDS has the ability to identify fault levels of untrained fault cases accurately. Therefore, the validity of the proposed FDS as a diagnostic tool for the hydraulic actuator internal leakage and the valve blockage has been assured. Finally, the proposed fault diagnostic scheme can be practically implemented. 
ISSN : 1319-1047 
Journal Name : Engineering Sciences Journal 
Volume : 17 
Issue Number : 1 
Publishing Year : 1427 AH
2006 AD 
Number Of Pages : 21 
Article Type : Article 
Added Date : Sunday, October 11, 2009 

Researchers

Researcher Name (Arabic)Researcher Name (English)Researcher TypeDr GradeEmail
أحمد البيطار Ahemd El-BetarResearcher  
مجدي عبدالحميد Magdy M. AbdelhamedResearcher  
أحمد العسالAhmed El-AssalResearcher  
روبي عبدالستارRoubi AbdelsatarResearcher  

Files

File NameTypeDescription
 23006.pdf pdf 

Back To Researches Page