Research on feature selection for AC contactor vibration signals based on regularized random forest with recursive selection.

When conducting condition recognition research on AC contactor vibration signals through time-frequency analysis, the feature data exhibit a high degree of redundancy, which leads to repetitive information and hinders the accuracy of recognition. To address the redundancy issue in the features of AC contactor vibration signals, this study introduces a feature selection method based on Regularized Random Forest with Recursive Selection (RFRS). Initially, a test platform for AC contactor vibration signals was established, and time-frequency domain features of the AC contactor vibration signals were extracted. Subsequently, the traditional Random Forest (RF) was refined by optimizing its stopping criteria using the Recursive Feature Elimination approach and by incorporating a regularization coefficient during the splitting process to direct the split towards significant features. This modification not only enhances the Random Forest's capacity to leverage existing information but also introduces a bias, enabling it to favor important features. Finally, through case analysis, the proposed method effectively reduced the dimensionality of the feature set and achieved an average of 87.37% for Recall, 87.41% for F1-Score, 88.38% for Precision, and 85.74% for Accuracy. The overall performance of this method surpasses that of the three mainstream feature selection methods: Spearman's rank correlation coefficient method, the embedded method, and the filter method. This study thus provides a rather effective feature selection approach for the state recognition study of AC contactors.