Dilated convolutional neural network based model for bearing faults and broken rotor bar detection in squirrel cage induction motors

2022 ◽  
Vol 191 ◽  
pp. 116290
Author(s):  
Prashant Kumar ◽  
Ananda Shankar Hati
2021 ◽  
Vol 11 (17) ◽  
pp. 7878 ◽  
Author(s):  
Marcello Minervini ◽  
Maria Evelina Mognaschi ◽  
Paolo Di Barba ◽  
Lucia Frosini

Bearing faults account for over 40% of induction motor faults, and for this reason, for several decades, much attention has been paid to their condition monitoring, through vibration measurements and, more recently, through electromagnetic signal analysis. Furthermore, in the last few years, research has been focused on evaluating deep learning algorithms for the automatic diagnosis of these faults. Therefore, the purpose of this study is to propose a novel procedure to automatically diagnose different types of bearing faults and load anomalies by means of the stator current and the external stray flux measured on the induction motor in which the bearings are installed. All the data were collected by performing experimental tests in the laboratory. Then, these data were processed to obtain images (scalograms and spectrograms), which were elaborated by a pre-trained Deep Convolutional Neural Network, modified through the transfer learning technique. The results demonstrated the ability of the electromagnetic signals, and in particular of the stray flux, to detect bearing faults and mechanical anomalies, in agreement with the recent literature. Moreover, the Convolutional Neural Network has been proven to be able to automatically discriminate bearing defects and with respect to the healthy condition.


2021 ◽  
Author(s):  
Dario Pasqualotto ◽  
Angela Navarro Navarro ◽  
Mauro Zigliotto ◽  
Jose A. Antonino-Daviu ◽  
Vicente Biot-Monterde

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