Neural Network Based Ball Bearing Fault Detection Using Vibration Features for Aerospace Applications

Traditionally, ball bearing condition monitoring is done by a human expert whose judgement is based on bearing vibration and temperature. In this paper, a method is described for classifying normal ball bearings and damaged ball bearings using scalar features, derived from their vibration signals, and a feedforward multi-layer neural network, trained using the back propagation algorithm. Two experimental test rigs, used for acquiring the vibration signals for the two types of ball bearings studied here, are described. Several scalar features, derived from the raw vibration signals, are discussed. Next, training of a feedforward multi-layer neural network with these scalar features, using back propagation algorithm, is presented. It is shown that with these scalar features, the neural network is successful in classifying normal and damaged ball bearings.