Abstract: The existing insulation diagnosis technology for highway electromechanical equipment mostly adopts single signal analysis or generalized feature extraction methods, which have insufficient diagnostic accuracy for weak insulation defects and composite faults, resulting in a high false alarm rate of faults. To this end, research is being conducted on insulation status monitoring and fault diagnosis technology for highway electromechanical equipment. This method targets core components such as power distribution cables and power transformers, and first uses specialized equipment such as high-frequency current sensors and ultrasonic sensors to non invasively collect key parameters such as insulation resistance and partial discharge in real-time. Then, a dynamic benchmark threshold system for temperature and humidity correction is established, combined with time-domain sliding window analysis and frequency-domain Fourier transform, to extract core abnormal features such as trend deviation rate and characteristic frequency. Finally, relying on the quantitative matching model of fault membership degree, accurate judgment of fault types and multi-sensor fusion positioning can be achieved. The experimental results show that the false alarm rate of this technology is less than 1.5% in 6 typical fault scenarios, and the deviation between the monitored and actual partial discharge values of power transformers is less than 1.5 pC, significantly improving the diagnostic reliability in complex environments and achieving good application results.