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

Wang, X. and Lu, S. and Huang, W. and Wanga, Q. and Zhang, S. and Xia, M. (2021) Efficient Data Reduction at the Edge of Industrial Internet of Things for PMSM Bearing Fault Diagnosis. IEEE Transactions on Instrumentation and Measurement, 70. ISSN 0018-9456

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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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Journal Article
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IEEE Transactions on Instrumentation and Measurement
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12 Feb 2021 11:54
Last Modified:
20 Sep 2023 01:40