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The reliable storage of spring potential energy is a prerequisite for ensuring the correct closing and opening operations of a circuit breaker. A fault identification method for circuit breaker energy storage mechanism, combined with the current–vibration signal entropy weight characteristic and grey wolf optimization-support vector machine (GWO-SVM), is proposed by analyzing the energy conversion and transmission relationship between control loop, motor, transmission component, and spring. First, the current envelope is denoised in the modulus maxima wavelet domain, the time-domain feature is extracted via a Hilbert transform (HT), and the kurtosis is calculated. Second, the energy method is used to select $K$ parameters, the objective function optimization method is used to select $\alpha $ for variational mode decomposition (VMD), the intrinsic mode function (IMF) is obtained by decomposing the vibration signal with VMD and the permutation entropy characteristics of IMFs are extracted. Third, the characteristic of current and vibration signals for classification are edited by the entropy weight method, and the corresponding weights are provided in accordance with the sample information amount and importance. Finally, the current–vibration entropy weight characteristics are constructed and sent to the SVM model for learning and training. The GWO algorithm is used to optimize the parameters of the SVM model based on the mixed kernel function, which reduces adverse effects from the model parameters’ choice of circuit breaker fault diagnosis. The results show that the overall diagnostic accuracy for the experimental samples reaches 100% and has good generalization capability based on the current–vibration entropy weight characteristic and the GWO-SVM method.

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Fault Diagnosis of Circuit Breaker Energy Storage Mechanism Based on Current-Vibration Entropy Weight Characteristic and Grey Wolf Optimization–Support Vector Machine