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Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - Efficient Data Reduction at the Edge of Industrial Internet of Things for PMSM Bearing Fault Diagnosis
AU - Wang, X.
AU - Lu, S.
AU - Huang, W.
AU - Wanga, Q.
AU - Zhang, S.
AU - Xia, M.
N1 - ©2021 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
PY - 2021/3/1
Y1 - 2021/3/1
N2 - An efficient data reduction algorithm is designed and implemented on an industrial Internet of Things (IIoT) node for permanent magnet synchronous motor (PMSM) bearing fault diagnosis in variable speed conditions. Leakage flux and vibration signals are respectively acquired by a magnetic sensor and an accelerometer on the IIoT node in a non-invasive manner. These two signals are processed and mixed on the IIoT and transmitted to a server. The received signal is separated, the cumulative rotation angle is calculated, and the vibration signal is resampled for bearing fault identification. The proposed method can reduce about 95% of the transmission data while maintaining sufficient precision in bearing fault diagnosis in comparison with a traditional method. The proposed method based on edge computing reduces the power consumption, and hence it is suitable to use on a battery-supplied IIoT node for remote PMSM condition monitoring and fault diagnosis.
AB - An efficient data reduction algorithm is designed and implemented on an industrial Internet of Things (IIoT) node for permanent magnet synchronous motor (PMSM) bearing fault diagnosis in variable speed conditions. Leakage flux and vibration signals are respectively acquired by a magnetic sensor and an accelerometer on the IIoT node in a non-invasive manner. These two signals are processed and mixed on the IIoT and transmitted to a server. The received signal is separated, the cumulative rotation angle is calculated, and the vibration signal is resampled for bearing fault identification. The proposed method can reduce about 95% of the transmission data while maintaining sufficient precision in bearing fault diagnosis in comparison with a traditional method. The proposed method based on edge computing reduces the power consumption, and hence it is suitable to use on a battery-supplied IIoT node for remote PMSM condition monitoring and fault diagnosis.
KW - bearing fault diagnosis
KW - Condition monitoring
KW - data reduction
KW - edge computing
KW - Fault diagnosis
KW - IIoT
KW - Industrial Internet of Things
KW - Magnetic levitation
KW - multiple sensor signal fusion
KW - Permanent magnet motors
KW - PMSM
KW - Servers
KW - Vibrations
KW - Data reduction
KW - Failure analysis
KW - Fault detection
KW - Green computing
KW - Magnetic leakage
KW - Permanent magnets
KW - Synchronous motors
KW - Bearing fault diagnosis
KW - Data-reduction algorithms
KW - Permanent Magnet Synchronous Motor
KW - Received signals
KW - Rotation angles
KW - Transmission data
KW - Variable speed conditions
KW - Vibration signal
KW - Industrial internet of things (IIoT)
U2 - 10.1109/TIM.2021.3051668
DO - 10.1109/TIM.2021.3051668
M3 - Journal article
VL - 70
JO - IEEE Transactions on Instrumentation and Measurement
JF - IEEE Transactions on Instrumentation and Measurement
SN - 0018-9456
M1 - 3508612
ER -