To solve the problem that it is difficult to effectively extract the mechanical fault features of high‐voltage circuit breakers from vibration signals and the fault identification rate is not high, a fault diagnosis method combining whale optimization algorithm enhanced variational mode decomposition (WOA‐VMD) with random forest (RF) is proposed. Initially, WOA is used to globally optimize the VMD parameters K , a . According to the optimization results, the mechanical vibration signals of the circuit breaker are decomposed to obtain a series of intrinsic mode functions (IMF) reflecting the mechanical state information of the circuit breaker. Then, the correlation coefficient is used to screen the modal components related to the original signal, and the sample entropy of the selected IMF is obtained as the feature vector, and the dataset is randomly divided into a training set and a test set. Finally, the training set is input into a random forest to train a classification model, and the trained model is used to classify the samples in the test set. Experimental results indicate that the vibration signal features extracted based on WOA‐VMD‐sample entropy are obvious, and the RF can accurately identify the mechanical fault of the circuit breaker. © 2026 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.
Zhang et al. (Sun,) studied this question.