TY - GEN
T1 - An improved random forest algorithm of fault diagnosis for rotating machinery
AU - Wang, Zilan
AU - Zhong, Maiying
AU - Yang, Rui
AU - Liu, Yang
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/7
Y1 - 2019/7
N2 - In this paper, a semi-supervised random forest (RF) algorithm is presented for fault diagnosis of rotating machinery. Firstly, many unlabeled samples are divided into two parts, denoted respectively as unlabeled sample I and unlabeled sample II. Then a graph-all the labeled samples are used to train the multiple decision trees. If the classification result is consistent with the one of label prediction, then the unlabeled sample I is added to the labeled samples and used for building RF model. While, the data of unlabeled sample II are utilized for testing of the obtained RF model. Finally, the developed RF algorithm is applied to an experimental platform of rotating machinery. It is shown from the simulation results that, for the cases of noisy samples with unsatisfying labels, the new developed semi-supervised RF algorithm can improve the fault classification accuracy than the conventional RF.
AB - In this paper, a semi-supervised random forest (RF) algorithm is presented for fault diagnosis of rotating machinery. Firstly, many unlabeled samples are divided into two parts, denoted respectively as unlabeled sample I and unlabeled sample II. Then a graph-all the labeled samples are used to train the multiple decision trees. If the classification result is consistent with the one of label prediction, then the unlabeled sample I is added to the labeled samples and used for building RF model. While, the data of unlabeled sample II are utilized for testing of the obtained RF model. Finally, the developed RF algorithm is applied to an experimental platform of rotating machinery. It is shown from the simulation results that, for the cases of noisy samples with unsatisfying labels, the new developed semi-supervised RF algorithm can improve the fault classification accuracy than the conventional RF.
KW - Fault Classification
KW - Fault Diagnosis
KW - Random Forest
KW - Rotating Machinery
KW - Semi-supervised Learning
UR - http://www.scopus.com/inward/record.url?scp=85094653918&partnerID=8YFLogxK
U2 - 10.1109/SAFEPROCESS45799.2019.9213389
DO - 10.1109/SAFEPROCESS45799.2019.9213389
M3 - Conference Proceeding
AN - SCOPUS:85094653918
T3 - Proceedings of 2019 11th CAA Symposium on Fault Detection, Supervision, and Safety for Technical Processes, SAFEPROCESS 2019
SP - 12
EP - 17
BT - Proceedings of 2019 11th CAA Symposium on Fault Detection, Supervision, and Safety for Technical Processes, SAFEPROCESS 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 11th CAA Symposium on Fault Detection, Supervision, and Safety for Technical Processes, SAFEPROCESS 2019
Y2 - 5 July 2019 through 7 July 2019
ER -