Fault Diagnosis on Bevel Gearbox with Neural Networks and Feature Extraction

Abstract

In recent years, early fault detection and diagnosis of gears have become extremely important due to requirement to decrease the downtime on production machinery caused by the failures. For that reason, researches have been done for the early detection of faults through the analysis of their vibration signals. Modern day machines, due to their complexities, can have many vibration generating sources in addition to noises. Therefore it is important that the vibration signal of faulty gear to be recognized and recovered for the diagnostics. In this paper Back-Propagation neural network has been used for the classification of RPM and oil level related gearbox faults that can occur during operation. With the help of Power spectrum technique, signal was more refined in order to make the feature selection process much more accurate.