Prediction of synthetic oil parameters by artificial neural networks at durability tests of porous bearings; pp. 123–131Full article in PDF format | doi: 10.3176/proc.2016.2.08
In this article we use artificial neural networks (ANN) in durability analysis of porous bearings. First, we present briefly the results of durability tests of porous sleeves impregnated with synthetic ester oil under different duration of the tests (100, 500, and 1000 h) and bearing temperature (60, 80, and 130 °C) at a rotational speed of 1000 rpm. After each durability test oil samples were removed from the bearings and some chosen parameters were checked (Fourier Transform Infrared spectra and total acid number). In the second stage, the collected data were used in the design of ANN, i.e. work parameters as the inputs and oil properties as the outputs. The tests of various ANN types were performed to achieve the smallest training error and the best performance. The best parameters were achieved for multilayer perceptrons neural networks, and also quite good prediction of oil parameters after the test was observed. The achieved results, i.e. the ANN designed, algorithms, and oil parameters used, were compared with those observed in the previous tests for mineral oil.
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