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

ABSTRACT: 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 c

SUBMITTER: Liu S 

PROVIDER: S-EPMC11379156 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature