Abstract

Gearboxes are essential devices for some applications, e.g., industrial rotating mechanical machines. Besides, the gearboxes malfunctioning can cause economic losses, risks to the human safety and can impair the performance of the systems in which they are included. Thus, it is necessary to find feasible and efficient methods to evaluate their physical condition. This work proposes an approach that uses the Fourier Transform spectrograms and Convolutional Neural Networks (CNN) to classify the gearbox fault severity condition by analyzing the vibration signals provided by an accelerometer. We used a dataset with ten damage levels of one failure mode of a helical gearbox operating under different load and speed values to assess the performance of the proposed solution. Three different CNN configurations were compared concerning accuracy, training time and other parameters. The proposed system achieves average values of accuracy up to 0.9743 regarding AUC, while it presents classification times close to 0.03 seconds, showing itself to be a competitive solution.