Design of neural network model for analysing hydrostatic circular recessed bearings with axial piston pump slipper


Canbulut F. , SINANOGLU C. , YILDIRIM Ş. , KOC E.

INDUSTRIAL LUBRICATION AND TRIBOLOGY, cilt.56, ss.288-299, 2004 (SCI İndekslerine Giren Dergi) identifier identifier

  • Cilt numarası: 56 Konu: 5
  • Basım Tarihi: 2004
  • Doi Numarası: 10.1108/00368790410550705
  • Dergi Adı: INDUSTRIAL LUBRICATION AND TRIBOLOGY
  • Sayfa Sayıları: ss.288-299

Özet

A neural network is employed to analyze axial piston pump of hydrostatic circular recessed bearing. Owing to complexity of the system, the neural network is used to predict the bearing parameters of the experimental system. The system mainly consists of cylinder block, piston, slipper, ball-joint and swash plate. The neural model of the system has three layers, which are input layer with one neuron, hidden layer with ten neurons and output layer with three neurons. it can be outlined from the results for both approaches neural network could be modeled bearing systems in real time applications.