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

Canbulut F., Sinanoglu C., Yildirim S., Koc E.

INDUSTRIAL LUBRICATION AND TRIBOLOGY, cilt.56, sa.5, ss.288-299, 2004 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 56 Sayı: 5
  • Basım Tarihi: 2004
  • Doi Numarası: 10.1108/00368790410550705
  • Dergi Adı: INDUSTRIAL LUBRICATION AND TRIBOLOGY
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.288-299
  • Çukurova Üniversitesi Adresli: Hayır

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.