Aiming to address the issues of single-feature selection and low prediction accuracy in the residual life prediction of circuit breakers,a prediction model based on an attention mechanism(AM)algorithm is proposed to optimize the long short-term memory(LSTM)network.First,characteristic parameters are extracted using a circuit breaker electrical life test platform.Next,the pearson correlation coefficient(PCC)is employed to select the optimal feature subset from multiple parameters,effectively capturing the degradation process of the electrical life.Finally,the residual electrical life of the miniature circuit breaker is used as the prediction label,and the remaining life is predicted using the AM-LSTM model.Experimental results demonstrate that the proposed model outperforms both GRU and LSTM models,achieving an accuracy of 87.78％,which meets the practical engineering requirements.
维普
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