Demonstrates effective early insulation fault detection in induction motors, implying improved real-time monitoring capabilities.
Aiming at the problems that existing detection methods struggle to accurately identify early insulation faults of induction motors, are susceptible to interference, and have poor installation adaptability, a non-intrusive detection method for early insulation faults of induction motors based on a microstrip antenna array is proposed. Relying on the low-loss electromagnetic wave transmission characteristic of the heat dissipation hole at the tail of the induction motor, a four-element microstrip antenna array with multiple narrow beams and dual detection frequencies is designed, with the detection frequencies accurately set at 1.14 GHz and 2.23 GHz, which effectively avoids the motor operation noise frequency band (≤300 MHz) and the strong interference frequency band of mobile base stations (900 MHz, 1.8 GHz, 2.4 GHz). Utilizing the high gain and strong directivity of the array antenna, the accurate extraction and amplification of weak electromagnetic wave signals from early insulation fault discharge penetrating through the heat dissipation hole are realized. The full-dimensional simulation design of the antenna array is completed by using HFSS electromagnetic simulation software, and an industrial-grade experimental platform is built to carry out multi-condition verification experiments. The results show that the proposed detection system can realize non-intrusive, non-stop, and non-disassembly identification of early insulation discharge faults in induction motors, with a fault recognition rate of 94% for single faults and 90% for composite faults, and the average signal-to-noise ratio reaches 31.6–35.2 dB. Even under strong industrial electromagnetic interference, the recognition rate remains above 85%. This method overcomes the problems of traditional methods such as severe noise interference, difficult installation, and inability to monitor online, providing a high-efficiency scheme for real-time insulation state monitoring of industrial induction motors with good engineering application value.
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Xu et al. (2026) studied this question.
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