To analyse a self‐acting parallel surface thrust bearing using a proposed feedforward neural network.
Firstly, a one‐piece hydrodynamic thrust bearing with an initially flat surface is analysed, designed and tested. Analysis of the configuration used is particularly simple and gives good agreement with experimental results. Secondly, some artificial neural network types are designed to analyse minimum film thickness for specified load of thrust bearing system.
A more efficient film shape might result if the length of the cantilever did not increase with radius, since with the configuration used, the deflection of the outer corner was almost three times greater than the deflection of the inner corner, although this effect only becomes acute with regard to film thickness at fairly high loads. The design analysis of an asymmetric cantilever would be more lengthy and less easy to apply. Extrapolation of results for the plain bearing shows that high loads could be carried, but under severe conditions of temperature and clearance.
Owing to finance problems, it was not easy to setup system in real time applications. This approach would be given usefulness elsewhere.
In future, this technique will be implemented for designing experimental neural network predictor on thrust bearing system. Also, this kind of neural predictor will be suitable for complex bearing systems.
A new type of neural network is used to investigate film thickness of thrust bearing system. Quick propagation neural network has given superior performance for designing of model of thrust bearing system. As described and shown in figures and tables, this kind of neural predictor could be employed for analysing such systems in practical analyses.
