Title: Fault Diagnosis of Locomotive RollingBearing Based on Rough Set Theoryand BP Neural Network
Abstract: A fault diagnosis method of the locomotive rolling bearing based on rough set theory and BP neural network is proposed.Firstly,the continuous attribute of original fault diagnosis sample is processed in the discrete way.Then,the conditional attribute is simplified by deleting redundant infernation based on the rough set theory.Lastly,considering the simplified minimum attribute set as the input of BP neural network,BP network is designed to diagnose the locomotive bearing. The simulation result shows that rough BP neural network not only can simplify the structure of neural network,but also can improve the convergence speed and accuracy of fault diagnosis.
Publication Year: 2014
Publication Date: 2014-01-01
Language: en
Type: article
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