TY - GEN
T1 - Fault diagnosis of rolling bearing with small samples based on wavelet packet theory and random forest
AU - Yan, Hongmei
AU - Mu, Huina
AU - Yi, Xiaojian
AU - Yang, Yuanyuan
AU - Chen, Guangliang
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/8
Y1 - 2019/8
N2 - In order to solve the problem of non-stationary vibration signals of rolling bearings and large sample size, a small sample fault diagnosis method based on wavelet packet theory and Random Forest algorithm was proposed in this paper. Firstly, the signal was preprocessed, then three-layer wavelet packet decomposition was carried out, and the decomposition coefficients are extracted as feature vectors, which were used to train BP Neural Network, Support Vector Machine (SVM) and Random Forest model. Through the diagnosis of multi-fault states of rolling bearings, the diagnostic accuracy rates of the three groups of classifiers are respectively 25%, 93.75% and 100%, which proves that Random Forest has better diagnosis effect and robustness for rolling bearings with small samples.
AB - In order to solve the problem of non-stationary vibration signals of rolling bearings and large sample size, a small sample fault diagnosis method based on wavelet packet theory and Random Forest algorithm was proposed in this paper. Firstly, the signal was preprocessed, then three-layer wavelet packet decomposition was carried out, and the decomposition coefficients are extracted as feature vectors, which were used to train BP Neural Network, Support Vector Machine (SVM) and Random Forest model. Through the diagnosis of multi-fault states of rolling bearings, the diagnostic accuracy rates of the three groups of classifiers are respectively 25%, 93.75% and 100%, which proves that Random Forest has better diagnosis effect and robustness for rolling bearings with small samples.
KW - Fault Diagnosis; Random Forest
KW - Rolling Bearing
KW - SVM
KW - Wavelet Packet Decomposition
UR - http://www.scopus.com/inward/record.url?scp=85091522709&partnerID=8YFLogxK
U2 - 10.1109/SDPC.2019.00062
DO - 10.1109/SDPC.2019.00062
M3 - Conference contribution
AN - SCOPUS:85091522709
T3 - Proceedings - 2019 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2019
SP - 305
EP - 310
BT - Proceedings - 2019 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2019
A2 - Li, Chuan
A2 - Zhang, Shaohui
A2 - Long, Jianyu
A2 - Cabrera, Diego
A2 - Ding, Ping
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2019
Y2 - 15 August 2019 through 17 August 2019
ER -