Skip to main content
SpringerLink
Log in

Analysis of effects of sizes of orifice and pockets on the rigidity of hydrostatic bearing using neural network predictor system

  • Published:
KSME International Journal Aims and scope Submit manuscript

Abstract

This paper presents a neural network predictor for analysing rigidity variations of hydrostatic bearing system. The designed neural network has feedforward structure with three layers. The layers are input layer, hidden layer and output layer. Two main parameter could be considered for hydrostatic bearing system. These parameters are the size of bearing pocket and the orifice dimension. Due to importancy of these parameters, it is necessary to analyse with a suitable optimisation method such as neural network. As depicted from the results, the proposed neural predictor exactly follows experimental desired results.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

Abbreviations

P c :

Pocket pressure

R d :

Outer radius of slipper

R i :

Inner radius of slipper

\(\bar P\) :

=Pp/Pc non-dimensional pressure

P :

Pressure

P p :

Supply pressure

Q c :

Leakage from pool across slipper lands

Q r :

Radial direction flow rate

η:

Dynamic viscosity

d e :

Diameter of orifice

l c :

Length of orifice

k c :

Orifice coefficient

\(\bar h\) :

h/R d non-dimensional clearance

k :

Bearing rigidity

\(\bar k\) :

=k/P p R d non-dimensional bearing rigidity

\(\bar W\) :

W/PpRd 2 non-dimensional vertical load

\(\bar R\) :

Rd/Ri non-dimensional radius

\(E(\bar \omega )\) :

Error variations for weights

f(x) :

\( = \frac{1}{{1 + e^{ - x} }}\) logistic function

μ:

Momentum

ηl(t):

Learning rate

\(\bar \delta (t)\) :

Exponential average of past value of δ

δ(t):

Derivation of error in weights

t :

Time

K :

Constant value for learning rate

φ:

Correction factor of learning rate

θ:

Coefficient of the exponential average value

\(\Delta (\bar \omega )\) :

The weights variations after update

n 0 :

Numbers of output units

nH :

Numbers of hidden units

n I :

Numbers of input units

R :

Radius

\(\bar R^* \) :

Inverse dimensionless radius

u i :

Output signals of hidden layer

f(.):

Simple non-linear function

h :

Clearance

W :

Vertical load

S(t) :

\( = \frac{{\partial E}}{{\partial \bar \omega }}(t)\)

AF:

Activation function

N:

Training numbers

References

  • Canbulut, F., Sinanoglu, C. and Yildirim, Ş., 2004, “Neural Network Analysis of Leakage Oil Quantity In The Design of Partially Hydrostatic Slipper Bearings,”Industrial Lubrication and Tribology, (in press).

  • Canbulut, F., Koç, E. and Canbulut, F., 1992, “The Effects of Sizes of Capillary Tube and Pockets on the Rigidity of Hydrostatic Bearing.”5th. National Machine Design and Manufacturing Congress, ODTU, Turkey.

    Google Scholar 

  • Durak, E., 2003, “Experimental Investigation of Porous Bearings Under Different Lubricant and Lubricating Conditions,”KSME International Journal, Vol. 17, No. 9, pp. 1276–1286.

    Article  Google Scholar 

  • Koç, E., 1989, “Analytical and Experimental Investigation into the Sealing and Lubrication Mechanism of the Gear ends in Pumps,”Wear, Vol. 135. pp. 79–94.

    Article  Google Scholar 

  • Koç, E and Hooke, C. J., 1996, “Investigation into the Effects of Orifice Size, Offset and Overclamp Ratio on the Lubrication of Slipper Bearings,”Tribology International, Vol. 29, No. 4, pp. 299–305.

    Article  Google Scholar 

  • Koç, E. and Hooke, C. J., 1997, “Considerations in the Design of Partially Hydrostatic Slipper Bearings,”Tribology International, Vol. 30, pp. 815–823.

    Article  Google Scholar 

  • Sinanoglu, C, Kurban, A. O. and Yildirim, Ş., 2004, “Analysis of Pressure Variations on Journal Bearing System Using Artificial Neural Network, ”Industrial Lubrication and Tribology, (in press).

  • Sinanoğlu, C., 2004, “The Analysis of The Effects of Surface Texture on The Capability of Load Carriage of Journal Bearings Using Neural Network,”Industrial Lubrication and Tribology, (in press).

  • Wang, X. and Yamaguchi, A., 2002a. “Characteristics of Hydrostatic Bearing/Seal Parts for Water Hydraulic Pumps and Motors. Part 1: Experiment and Theory,”Tribology International, Vol. 35, pp. 425–433.

    Article  Google Scholar 

  • Wang, X. and Yamaguchi, A., 2002b, “Characteristics of Hydrostatic Bearing/Seal Parts for Water Hydraulic Pumps and Motors. Part 2: On Eccentric Loading and Power Loses,”Tribology International, Vol. 35, pp. 435–442.

    Article  Google Scholar 

  • Yildirim, Ş. and Uzmay, I., 2003, “Neural network applications to vehicle’s vibration analysis.”Mechanism and Machine Theory, Vol. 38, pp. 27–41.

    Article  Google Scholar 

  • Yildirim, Ş., 2002, “Robot Trajectory Control Using Neural Networks.”IEE Electronics Letters, Vol. 38, No. 19, pp. 1111–1113.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Mechanical Engineering Department, Erciyes University, Faculty of Eng., 38039, Kayseri, Turkey

    Fazil Canbulut, Cem Sinanoğlu & Şahin Yildirim

Authors
  1. Fazil Canbulut
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Cem Sinanoğlu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Şahin Yildirim
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Fazil Canbulut.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Canbulut, F., Sinanoğlu, C. & Yildirim, Ş. Analysis of effects of sizes of orifice and pockets on the rigidity of hydrostatic bearing using neural network predictor system. KSME International Journal 18, 432–442 (2004). https://doi.org/10.1007/BF02996108

Download citation

  • Received:

  • Revised:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/BF02996108

Key Words

Search

Navigation