Addressing the constraints of utilizing a single signal for diagnosing various faults in high-voltage circuit breakers. In this paper,a multi-fault diagnosis method for high-voltage circuit breakers with joint characteristics of electric vibration is proposed.Firstly, extracting key time nodes of current waveforms and corresponding amplitudes of coil current signals during high-voltage circuit breaker closing operation by peak-valley algorithm to construct electrical features;and perform a variational modal decomposition(VMD) of the vibration signal, calculating multiscale dispersion entropy values for different modal components to construct mechanical characteristics.Next,he electrical and mechanical feature vectors were subjected to principal component analysis with dimensionality reduction, generating joint features of electric vibration based on the obtained variance contribution, effectively solving the feature vector redundancy problem; finally, the combined characteristics of signals under different faults are input into fuzzy cluster analysis, successfully identified the specific fault type in the high-voltage circuit breaker.According to the experimental findings,the proposed method demonstrates superior accuracy in fault diagnosis compared to single-signal approaches.It classifies effectively,validated in different diagnostic models with 98.6%.It successfully enables the diagnosis of faults in high-voltage circuit breakers.