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
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Induction Motors ◽  
Bearing Faults ◽  
Broken Rotor Bar ◽  
Squirrel Cage

scholarly journals Convolutional Neural Networks for Automated Rolling Bearing Diagnostics in Induction Motors Based on Electromagnetic Signals

2021 ◽  
Vol 11 (17) ◽  
pp. 7878 ◽  
Author(s):  
Marcello Minervini ◽  
Maria Evelina Mognaschi ◽  
Paolo Di Barba ◽  
Lucia Frosini
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Induction Motor ◽  
Experimental Tests ◽  
Rolling Bearing ◽  
Electromagnetic Signal ◽  
Bearing Faults ◽  
Bearing Diagnostics ◽  
Bearing Defects ◽  
Electromagnetic Signals

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.

Fault Detection in Soft-started Induction Motors using Convolutional Neural Network Enhanced by Data Augmentation Techniques

2021 ◽  
Author(s):  
Dario Pasqualotto ◽  
Angela Navarro Navarro ◽  
Mauro Zigliotto ◽  
Jose A. Antonino-Daviu ◽  
Vicente Biot-Monterde
Keyword(s):  
Neural Network ◽  
Fault Detection ◽  
Convolutional Neural Network ◽  
Data Augmentation ◽  
Induction Motors ◽  
Augmentation Techniques
Sign in / Sign up

Export Citation Format

Share Document