چكيده لاتين :
Maintenance reliability and efficiency in industrial hydraulic systems
operation has become a point ofconcern for the professionals in maintenance
engineering. One practical approach in this regard is the realization of symptoms
of early stage malfunctioning in fluid power systems after which maintenance
planning and preventive means would follow upon a reasonably accurate and
subsequently acceptable determination. Among the highly reliable sources
providing such convenience, Artificial Neural Network (ANN) stands ahigh chance
of success neural network method has been used to detect faults occurring in most
hydraulic systems. These faults could be related to supply pressure, effective bulk
modulus and total leakage. The simulated system in this study consists of hydraulic
servo valve, double acting cylinder and a spring that resists piston movement. Two
main reasons causing this system to have a nonlinear behavior are hydraulic servo
valve and compressibility effect of hydraulic fluid.The neural network approach in
this investigation comprises of an efficient use in nonlinear systems and requires
advance knowledge about the system behavior under faulty conditions and
assumptions about the type and severity of faults likely to occur. Neural networks
trained with different training algorithms are investigated. After training the
network, the system was examined for different inputs and obtained results were compared.
