TY - GEN
T1 - Support Vector Classifier-Based Broken Rotor Bar Detection in Squirrel Cage Induction Motor
AU - Kumar, Prashant
AU - Hati, Ananda Shankar
N1 - Publisher Copyright:
© 2022, Springer Nature Singapore Pte Ltd.
PY - 2022
Y1 - 2022
N2 - Condition monitoring based on machine learning techniques for preventive maintenance of squirrel cage induction motors (SCIM) is the need of modern industries. Early detection of broken rotor bar (BRB) fault can reduce the unwanted production loss and minimizes downtime. With the advancement in high computational machines, machine learning techniques like logistic regression, artificial neural network, random forest technique, etc. can be efficiently implemented in BRB detection in SCIM. This paper deals with broken rotor bar detection in SCIM under different loading condition based on support vector machine (SVM)-based technique with the help of current spectrum analysis. Different kernel functions like linear, quadratic, cubic and Gaussian functions are analysed for finding the best kernel functions for achieving the good accuracy of the system.
AB - Condition monitoring based on machine learning techniques for preventive maintenance of squirrel cage induction motors (SCIM) is the need of modern industries. Early detection of broken rotor bar (BRB) fault can reduce the unwanted production loss and minimizes downtime. With the advancement in high computational machines, machine learning techniques like logistic regression, artificial neural network, random forest technique, etc. can be efficiently implemented in BRB detection in SCIM. This paper deals with broken rotor bar detection in SCIM under different loading condition based on support vector machine (SVM)-based technique with the help of current spectrum analysis. Different kernel functions like linear, quadratic, cubic and Gaussian functions are analysed for finding the best kernel functions for achieving the good accuracy of the system.
KW - Kernel functions
KW - SCIM
KW - Support vector machines
UR - http://www.scopus.com/inward/record.url?scp=85113282378&partnerID=8YFLogxK
U2 - 10.1007/978-981-16-0550-5_42
DO - 10.1007/978-981-16-0550-5_42
M3 - Conference contribution
AN - SCOPUS:85113282378
SN - 9789811605499
T3 - Lecture Notes in Mechanical Engineering
SP - 429
EP - 438
BT - Machines, Mechanism and Robotics - Proceedings of iNaCoMM 2019
A2 - Kumar, Rajeev
A2 - Chauhan, Vishal S.
A2 - Talha, Mohammad
A2 - Pathak, Himanshu
PB - Springer Science and Business Media Deutschland GmbH
T2 - 4th International and 19th National Conference on Machines and Mechanism, iNaCoMM 2019
Y2 - 5 December 2019 through 7 December 2019
ER -