TY - GEN
T1 - Spectrogram-based Aging Assessment for Insulations on Stator Coil Bar
AU - Liu, Shuo
AU - Zhang, Yue
AU - Hu, Bo
AU - Ding, Jian
AU - Wang, Jianji
N1 - Publisher Copyright:
© 2021 IEEE
PY - 2021
Y1 - 2021
N2 - Based on four kinds of spectrograms, this paper studies how to effectively predict the age of insultations on stator coil bar. The prediction of using time can guide the assessment of stator coil bar. In this work, some samples with different ages and temperatures of epoxy insulations are generated in an accelerated aging test. Then the microscopic morphology spectrograms are obtained for the samples. We use the histogram of oriented gradient (HOG) and gray-level co-occurrence matrix (GLCM) to extract the features of the spectrograms. Finally, support vector machine (SVM) is used to classify and predict the using time of samples based on HOG and GLCM features. This method with accuracy 90.3333% on the classification and prediction for the age of insulations on stator coil bar.
AB - Based on four kinds of spectrograms, this paper studies how to effectively predict the age of insultations on stator coil bar. The prediction of using time can guide the assessment of stator coil bar. In this work, some samples with different ages and temperatures of epoxy insulations are generated in an accelerated aging test. Then the microscopic morphology spectrograms are obtained for the samples. We use the histogram of oriented gradient (HOG) and gray-level co-occurrence matrix (GLCM) to extract the features of the spectrograms. Finally, support vector machine (SVM) is used to classify and predict the using time of samples based on HOG and GLCM features. This method with accuracy 90.3333% on the classification and prediction for the age of insulations on stator coil bar.
KW - aging assessment
KW - gray-level co-occurrence matrix
KW - histogram of oriented gradient
KW - spectrogram
KW - support vector machine
UR - https://www.scopus.com/pages/publications/85128053444
U2 - 10.1109/CAC53003.2021.9727327
DO - 10.1109/CAC53003.2021.9727327
M3 - 会议稿件
AN - SCOPUS:85128053444
T3 - Proceeding - 2021 China Automation Congress, CAC 2021
SP - 6350
EP - 6355
BT - Proceeding - 2021 China Automation Congress, CAC 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 China Automation Congress, CAC 2021
Y2 - 22 October 2021 through 24 October 2021
ER -