TY - CONF
T1 - Convolutional Neural Networks Using Fourier Transform Spectrogram to Classify the Severity of Gear Tooth Breakage
AU - Monteiro, Rodrigo
AU - Bastos-Filho, Carmelo
AU - Cerrada, Mariela
AU - Cabrera, Diego
AU - Sanchez, Rene Vinicio
PY - 2019/3/11
Y1 - 2019/3/11
N2 - 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.
AB - 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.
KW - Convolutional Neural Network
KW - Fault Severity
KW - Fourier Transform
KW - Gearbox Diagnosis
KW - Spectrogram
UR - https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85062338245&origin=inward
UR - https://www.scopus.com/inward/citedby.uri?partnerID=HzOxMe3b&scp=85062338245&origin=inward
UR - http://www.mendeley.com/research/convolutional-neural-networks-using-fourier-transform-spectrogram-classify-severity-gear-tooth-break
U2 - 10.1109/SDPC.2018.8664985
DO - 10.1109/SDPC.2018.8664985
M3 - Paper
SP - 490
EP - 496
T2 - Proceedings - 2018 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2018
Y2 - 11 March 2019
ER -