Fault detection and isolation

Programming language

Engineering

Artificial intelligence

Fault (geology)

Geology

Motor drive

SIGNAL (programming language)

Voltage

Rotor (electric)

Computer science

Actuator

Control (management)

Electrical engineering

Commutation

Seismology

Control theory (sociology)

Mechanical engineering

IEEE Transactions on Industrial Electronics

A brushless dc (BLDC) motor with an outer rotor has great potential for emerging electric vehicle (EV) applications as an in-wheel hub motor. Interturn short circuit faults (ISCFs) are common electrical faults in these applications, and their fault diagnosis is of critical importance. This article proposes a signal analysis-based method to detect and quantitatively analyze the ISCF in a BLDC motor under a six-step commutation control strategy. The zero-sequence voltage component (ZSVC) and three-phase currents are simultaneously determined while the fundamental frequency amplitude of the ZSVC is developed as a fault indicator for diagnostic purposes. Thereafter, the faulty phase is identified, and the fault severity is estimated by jointly analyzing the ZSVC and faulted phase current. The proposed technique entails creating an analytical model, a simulation model, and experimental tests. Experimental results demonstrate that the proposed technique has excellent accuracy, quick response, and real-time fault diagnosis for BLDC motors. This technique will accelerate the use of BLDC-based hub motors in EVs and the widespread application of EVs to reduce the carbon emissions.

Detection and Evaluation of the Interturn Short Circuit Fault in a BLDC-Based Hub Motor