TY - GEN
T1 - An Early Fault Diagnosis Method of Rolling Element Bearings Based on MED, DFA, and Improved KNN
AU - Zhao, Sifang
AU - Song, Qiang
AU - Wang, Mingsheng
AU - Huang, Xin
AU - Cao, Dongdong
AU - Zhang, Qin
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/11/13
Y1 - 2020/11/13
N2 - Rolling bearings are the key component of rotating machinery. In the early stage of bearing fault, the faint feature is susceptible to environmental noise. Therefore, weak fault characteristics are difficult to be extracted. An early fault diagnosis method based on minimum entropy deconvolution (MED), detrended fluctuation analysis (DFA), and improved K-nearest neighbor (IKNN) is proposed in this paper. First, the MED filter is employed for the enhancement of the fault components which are drowned in the time-domain vibration signal. The DFA method is then used for the extraction of fault features from the enhanced signals. Finally, the proposed IKNN algorithm is applied as the classifier to identify the fault types. Three kinds of early failures occurred in the inner race, cage, and outer race are performed on the bearing test rig. The experimental results show that the average classification accuracy of the proposed diagnosis method can reach 97.5%.
AB - Rolling bearings are the key component of rotating machinery. In the early stage of bearing fault, the faint feature is susceptible to environmental noise. Therefore, weak fault characteristics are difficult to be extracted. An early fault diagnosis method based on minimum entropy deconvolution (MED), detrended fluctuation analysis (DFA), and improved K-nearest neighbor (IKNN) is proposed in this paper. First, the MED filter is employed for the enhancement of the fault components which are drowned in the time-domain vibration signal. The DFA method is then used for the extraction of fault features from the enhanced signals. Finally, the proposed IKNN algorithm is applied as the classifier to identify the fault types. Three kinds of early failures occurred in the inner race, cage, and outer race are performed on the bearing test rig. The experimental results show that the average classification accuracy of the proposed diagnosis method can reach 97.5%.
KW - Bearing fault
KW - Detrended fluctuation analysis (DFA)
KW - Fault diagnostics
KW - K-nearest neighbor(KNN)
KW - Minimum entropy deconvolution (MED)
UR - http://www.scopus.com/inward/record.url?scp=85101078042&partnerID=8YFLogxK
U2 - 10.1109/ICEICT51264.2020.9334200
DO - 10.1109/ICEICT51264.2020.9334200
M3 - Conference contribution
AN - SCOPUS:85101078042
T3 - ICEICT 2020 - IEEE 3rd International Conference on Electronic Information and Communication Technology
SP - 123
EP - 128
BT - ICEICT 2020 - IEEE 3rd International Conference on Electronic Information and Communication Technology
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 3rd IEEE International Conference on Electronic Information and Communication Technology, ICEICT 2020
Y2 - 13 November 2020 through 15 November 2020
ER -