TY - GEN
T1 - Amalgamation of Transfer Learning and Deep Convolutional Neural Network for Multiple Fault Detection in SCIM
AU - Kumar, Prashant
AU - Hati, Ananda Shankar
AU - Padmanaban, Sanjeevikumar
AU - Leonowicz, Zbigniew
AU - Chakrabarti, Prasun
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/6
Y1 - 2020/6
N2 - The modern industries are driven by the Squirrel cage induction motors (SCIMs), and zero downtime is the need of the hour. Condition-based maintenance is pivotal for achieving zero downtime. The ability of automatic feature extraction of Deep learning has effectively used in fault diagnosis in SCIMs. This paper proposes a novel transfer learning (TL) based deep convolutional neural network (CNN) fault detection model for bearing fault and broken rotor bar detection in SCIM, both individually and jointly. The transfer learning enables the faster learning and accelerates the training of deep CNN based fault detection model. Compared with the deep CNN model trained from scratch, the developed method is meticulous and computationally efficient. This paper has used a current analysis for fault detection in SCIMs. The proposed method owing to its deep structures and inherent ability, automatically learns the features from current signals for fault detection. The proposed fault detection model has achieved a mean accuracy of 99.40%. Also, the proposed method overcomes the disadvantages of deep CNN by applying for the knowledge transfer through transfer learning.
AB - The modern industries are driven by the Squirrel cage induction motors (SCIMs), and zero downtime is the need of the hour. Condition-based maintenance is pivotal for achieving zero downtime. The ability of automatic feature extraction of Deep learning has effectively used in fault diagnosis in SCIMs. This paper proposes a novel transfer learning (TL) based deep convolutional neural network (CNN) fault detection model for bearing fault and broken rotor bar detection in SCIM, both individually and jointly. The transfer learning enables the faster learning and accelerates the training of deep CNN based fault detection model. Compared with the deep CNN model trained from scratch, the developed method is meticulous and computationally efficient. This paper has used a current analysis for fault detection in SCIMs. The proposed method owing to its deep structures and inherent ability, automatically learns the features from current signals for fault detection. The proposed fault detection model has achieved a mean accuracy of 99.40%. Also, the proposed method overcomes the disadvantages of deep CNN by applying for the knowledge transfer through transfer learning.
KW - bearing fault
KW - broken rotor bar
KW - Convolutional neural network
KW - Deep learning
KW - Squirrel cage induction motors
KW - Transfer learning
UR - http://www.scopus.com/inward/record.url?scp=85090423369&partnerID=8YFLogxK
U2 - 10.1109/EEEIC/ICPSEurope49358.2020.9160712
DO - 10.1109/EEEIC/ICPSEurope49358.2020.9160712
M3 - Conference contribution
AN - SCOPUS:85090423369
T3 - Proceedings - 2020 IEEE International Conference on Environment and Electrical Engineering and 2020 IEEE Industrial and Commercial Power Systems Europe, EEEIC / I and CPS Europe 2020
BT - Proceedings - 2020 IEEE International Conference on Environment and Electrical Engineering and 2020 IEEE Industrial and Commercial Power Systems Europe, EEEIC / I and CPS Europe 2020
A2 - Leonowicz, Zhigniew
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 IEEE International Conference on Environment and Electrical Engineering and 2020 IEEE Industrial and Commercial Power Systems Europe, EEEIC / I and CPS Europe 2020
Y2 - 9 June 2020 through 12 June 2020
ER -
