Pressure Distribution Analysis of Hydrodynamic Journal Bearing using Artificial Neural Network


This paper investigates the variations in pressure distribution in the hydrodynamic journal bearing system for varying set of load, speed and various lubricants. First part of paper consists of experimental readings and second part consists of training neural network for practical pressure distribution readings. In experimental section practical pressure distribution developed in journal bearing system is measured on test rig. Test is performed at different set of load and using different lubricants. The recorded experimental data of pressure distributions are employed as training and testing data for an artificial neural network. The type of neural network is a feed forward network. Back propagation algorithm is used to update the weight of the network during the training and to minimize error. We found that prediction of artificial neural network was in close agreement with practical pressure distribution given by test rig.

  • Abstract
  • Keywords
  • 1. Introduction-
  • 2. Literature Review-
  • 3. Specification-
  • 4. Practical Readings-
  • 5. Artificial Neural Network-
  • 6. Result -
  • 7. Conclusion -
  • References

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