Diagnosing the operational status of High-voltage circuit breakers (HVCBs) is crucial for ensuring the safe and stable operation of the grid. Mechanical characteristic parameters are effective indicators for evaluating the performance of HVCBs. Recent studies have shown that the actions of the springs and cams in HVCBs can be used to detect the operational status of the mechanical mechanisms

which occur extremely quickly

usually in the speed of m/ms. In this paper

dynamic vision sensing technology was employed to rapidly and dynamically capture the movements of the springs and cam of the HPL245B1 HVCB. The data volume of a single experiment is less than 100 MB

whereas the data collected by a high-speed camera at the same frame rate exceeds 1 GB. Action data streams of the springs and cam were obtained and images were reconstructed from the event streams. The Lucas–Kanade optical flow algorithm and the normalised cross-correlation algorithm are applied to calculate the parameters of spring deformation characteristics and cam rotation characteristics for mechanical feature detection of HVCBs. This is the first attempt to utilize brain-inspired hardware technology for the status monitoring of electrical equipment. The advantages of dynamic vision sensing technology

such as high dynamic range

low data transmission

and low energy consumption

also offer significant benefits for air discharge monitoring and status monitoring of electrical equipment.