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

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 vibration signals are, respectively, acquired by a magnetic sensor accelerometer the IIoT noninvasive manner. These two are processed mixed transmitted to server. The received signal separated, cumulative rotation angle calculated, resampled identification. proposed method can reduce about 95% transmission while maintaining sufficient precision comparison traditional method. based edge computing reduces power consumption, hence it suitable use battery-supplied remote PMSM condition monitoring diagnosis.