Air switchgear is widely used in medium- and low-voltage power distribution systems; however

insulation failures occur frequently. To address this issue

this study proposes a discharge fault monitoring method based on the fusion of ultrasonic and gas sensor signals. A discharge monitoring device integrating ultrasonic and gas sensors was developed

along with an air discharge fault simulation platform. The diffusion behavior of discharge-generated gases within the discharge chamber was simulated

and an optimal deployment strategy was determined

namely

deploying the monitoring device at the bottom of the switchgear. Subsequently

the device was installed at the upper

middle

and lower positions to conduct experiments on following three types of discharge faults: corona discharge

arc discharge

and surface contamination discharge on insulators. Sensor response signals were collected to validate the simulation results. Furthermore

an early warning method based on a sliding time window for ultrasonic signals was introduced

and a discharge fault identification algorithm using a classification and regression tree (CART) was developed.A discharge monitoring device for sensing and computing was realized

achieving accurate classification of multiple fault types with a recognition accuracy of 96.67%. This research holds strong potential for large-scale application in the field of discharge monitoring for air switchgear.