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

作者：

Highlights：

• Dilated convolutional neural network-based model is used for fault detection.

• Developed model expedites high accuracy with less number of layers.

• Proposed model provides faster convergence owing to dilated convolutions.

• Model facilitates self and automatic fault feature learning.

摘要

•Dilated convolutional neural network-based model is used for fault detection.•Developed model expedites high accuracy with less number of layers.•Proposed model provides faster convergence owing to dilated convolutions.•Model facilitates self and automatic fault feature learning.

论文关键词：Squirrel cage induction motor (SCIM),Dilated convolutional neural network (DCNN),Bearing fault,Broken rotor bar

论文评审过程：Received 2 February 2021, Revised 11 October 2021, Accepted 23 November 2021, Available online 6 December 2021, Version of Record 9 December 2021.

论文官网地址：https://doi.org/10.1016/j.eswa.2021.116290