Intelligent condition monitoring of Tractor Gearbox using artificial neural network

A neural network simulator built for prediction of faults in rotating machinery is discussed. A back propagation learning algorithm and a multi-layer network have been employed. The layers are constituted of nonlinear neurons and an input vector normalization scheme has been built into the simulator. Features to apply as input to ANN were extracted from analysis of vibration signals in frequency domain by means of fast Fourier transform method. The classified network outputs are worn, broken-teeth of gear and faultless condition. The optimal model of ANN was selected after several evaluations based on minimizing of mean square error (MSE), correct classification rate (CCR) and coefficient of correlation (r). The networks are validated for test data with unknown faults. The optimal ANN model for the classifier was of 489-10-3 configuration.

کلیدواژه ها:

Gearbox ، Fault diagnosis ، Vibration Condition Monitoring ، Artificial Neural Network ، Classification