TY - JOUR
T1 - Dilated convolutional neural network based model for bearing faults and broken rotor bar detection in squirrel cage induction motors
AU - Kumar, Prashant
AU - Hati, Ananda Shankar
N1 - Publisher Copyright:
© 2021 Elsevier Ltd
PY - 2022/4/1
Y1 - 2022/4/1
N2 - Deep learning can play a pivotal role in early fault detection in squirrel cage induction motors (SCIMs) and achieving Industry 4.0. SCIM finds application in industries like mining, textile, manufacturing, and many more. Early fault detection in SCIM can significantly reduce downtime and optimize productivity. This paper proposes a novel fault detection technique for bearing faults and broken rotor bar detection in SCIM using the dilated convolutional neural network-based model. A simple 1-D signal to image conversion technique is also proposed for transforming the 1-D vibration signal acquired from multiple accelerometers to images. The proposed method provides an end-to-end learning solution for fault detection. The propounded approach has accomplished an average accuracy of more than 99.50%. A comparison has also been made between different convolutional neural network (CNN) models and conventional machine learning models to show the proposed method's efficiency. The complete experimental work has been carried out on a 5 kW, 3-phase, 415 V, 50 Hz SCIM. The dilated CNN model development has been done using python software, and the packages used are Keras and TensorFlow.
AB - Deep learning can play a pivotal role in early fault detection in squirrel cage induction motors (SCIMs) and achieving Industry 4.0. SCIM finds application in industries like mining, textile, manufacturing, and many more. Early fault detection in SCIM can significantly reduce downtime and optimize productivity. This paper proposes a novel fault detection technique for bearing faults and broken rotor bar detection in SCIM using the dilated convolutional neural network-based model. A simple 1-D signal to image conversion technique is also proposed for transforming the 1-D vibration signal acquired from multiple accelerometers to images. The proposed method provides an end-to-end learning solution for fault detection. The propounded approach has accomplished an average accuracy of more than 99.50%. A comparison has also been made between different convolutional neural network (CNN) models and conventional machine learning models to show the proposed method's efficiency. The complete experimental work has been carried out on a 5 kW, 3-phase, 415 V, 50 Hz SCIM. The dilated CNN model development has been done using python software, and the packages used are Keras and TensorFlow.
KW - Bearing fault
KW - Broken rotor bar
KW - Dilated convolutional neural network (DCNN)
KW - Squirrel cage induction motor (SCIM)
UR - http://www.scopus.com/inward/record.url?scp=85120887507&partnerID=8YFLogxK
U2 - 10.1016/j.eswa.2021.116290
DO - 10.1016/j.eswa.2021.116290
M3 - Article
AN - SCOPUS:85120887507
SN - 0957-4174
VL - 191
JO - Expert Systems with Applications
JF - Expert Systems with Applications
M1 - 116290
ER -