TY - GEN
T1 - Motor Speed Signature Analysis of Bearing Fault Detection Based on SK and Adaptive Signal Reconstruction with EEMD
AU - Na, Chai
AU - Ming, Yang
AU - Boyang, Ren
AU - Dianguo, Xu
N1 - Publisher Copyright:
© 2019 The Korean Institute of Power Electronics (KIPE).
PY - 2019/5
Y1 - 2019/5
N2 - Local bearing faults will lead to impulses in motor speed which is already available in the vector controlled AC motor drives. Due to the limited sampling frequency of control system, the extraction of fault feature is difficult. Therefore, a signal processing scheme which combines spectral kurtosis (SK)with the adaptive signal reconstruction based on ensemble empirical mode decomposition (EEMD)is proposed. Firstly, motor speed is decomposed by EEMD into a finite number of intrinsic mode functions (IMFs)which reflect the local characteristics of original signal. Then, the correlation coefficient is employed to eliminate the irrelevant components, and reconstruct the speed signal with a higher signal-to-noise ratio adaptively. Finally, the reconstructed speed is analyzed with SK to extract fault features. The proposed scheme is verified on the motor bearing outer ring fault detection platform, and its effectiveness over the conventional SK is also shown.
AB - Local bearing faults will lead to impulses in motor speed which is already available in the vector controlled AC motor drives. Due to the limited sampling frequency of control system, the extraction of fault feature is difficult. Therefore, a signal processing scheme which combines spectral kurtosis (SK)with the adaptive signal reconstruction based on ensemble empirical mode decomposition (EEMD)is proposed. Firstly, motor speed is decomposed by EEMD into a finite number of intrinsic mode functions (IMFs)which reflect the local characteristics of original signal. Then, the correlation coefficient is employed to eliminate the irrelevant components, and reconstruct the speed signal with a higher signal-to-noise ratio adaptively. Finally, the reconstructed speed is analyzed with SK to extract fault features. The proposed scheme is verified on the motor bearing outer ring fault detection platform, and its effectiveness over the conventional SK is also shown.
KW - Bearing fault detection
KW - EEMD
KW - Motor speed signature analysis
KW - Spectral kurtosis
UR - https://www.scopus.com/pages/publications/85071643844
M3 - 会议稿件
AN - SCOPUS:85071643844
T3 - ICPE 2019 - ECCE Asia - 10th International Conference on Power Electronics - ECCE Asia
SP - 3315
EP - 3320
BT - ICPE 2019 - ECCE Asia - 10th International Conference on Power Electronics - ECCE Asia
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 10th International Conference on Power Electronics - ECCE Asia, ICPE 2019 - ECCE Asia
Y2 - 27 May 2019 through 30 May 2019
ER -