SIGNAL (programming language)

Quantum mechanics

Engineering

Fault detection and isolation

Node (physics)

Actuator

Real-time computing

Magnetic flux leakage

Operating system

Vibration

Computer science

Acoustics

Fault (geology)

Seismology

Condition monitoring

Geology

Accelerometer

Physics

Mathematics

Enhanced Data Rates for GSM Evolution

Artificial intelligence

Geometry

Structural engineering

Programming language

Bearing (navigation)

Magnet

Reduction (mathematics)

Electrical engineering

IEEE Transactions on Instrumentation and Measurement

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 noninvasive 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 to 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.

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