Study on the influence of identify orientation on acoustic-based fault diagnosis for circuit breakers in switchgear
Year: 2026
Type: article
Abstract: As key equipment in the power system, the operating state of switchgear circuit breakers directly affects power grid reliability. Traditional fault diagnosis methods suffer from complex installation and high susceptibility to interference, while voiceprint diagnosis has become a research hotspot due to its non-contact, high sensitivity and easy deployment. However, the acquisition orientation (angle and distance) of sensors can significantly change voiceprint signal characteristics and reduce diagnostic accuracy. Therefore, this paper takes 10 kV switchgear circuit breakers as the research object, collects opening and closing voiceprint signals at different angles and distances through experiments, and studies the influence of sensor orientation on voiceprint characteristics. Time-frequency analysis shows that the energy of the closing signal is about twice that of the opening signal. Within the range of ±45°, the angle change has little effect on the energy (difference < 8 %), and the 0° orientation has the best signal-to-noise ratio. The angle has no significant effect on the frequency-domain characteristics, but the closing signal has richer high-frequency components. Distance experiments show that the signal energy follows the 1 / r 2 attenuation law, and the high-frequency components attenuate faster. Accordingly, this paper proposes a signal compensation method based on energy normalization, which can eliminate the spectrum distortion caused by distance and keep the signal characteristics consistent at different distances. The results provide a theoretical basis for the optimal layout of sensors in circuit breaker voiceprint diagnosis. The proposed compensation method can effectively improve the accuracy of fault diagnosis models and has important engineering application value for power equipment condition monitoring.
Authors: Guangmin Gu, Wei Zhang, Like Zhao, Hao Liu
Institution: Inner Mongolia Electric Power (China)
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $808
OpenAlex ID: W7204114587