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Efficient Data Reduction at the Edge of Industrial Internet of Things for PMSM Bearing Fault Diagnosis

Research output: Contribution to journalJournal articlepeer-review

Published
  • X. Wang
  • S. Lu
  • W. Huang
  • Q. Wanga
  • S. Zhang
  • M. Xia
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Article number3508612
<mark>Journal publication date</mark>1/03/2021
<mark>Journal</mark>IEEE Transactions on Instrumentation and Measurement
Volume70
Number of pages12
Publication StatusPublished
Early online date14/01/21
<mark>Original language</mark>English

Abstract

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.

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©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.