Fault diagnosis of drivetrain systems based on vibration signals and convolutional neural networks

Accurate and fast fault diagnosis can improve the reliability of drivetrain systems. The fault diagnosis model based on convolutional neural networks is tricky because the accuracy and efficiency of diagnosis depend on the neural network parameters, such as layer size and number of layers. In this paper, we developed a data pre-processing technique to format vibrational data to be receptible to a neural network. First one will study the difference in performance by inputting a spectrogram of the vibrational data compared to the raw data itself. Second, the windowizing technique is used and its parameters are explored to train different configurations of convolutional neural networks. Finally, the optimal neural network parameters are obtained to improve the computational efficiency while the training model has the highest accuracy.