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

Shuxin Liu1, Xinzhi Qi1, Chaojian Xing1

  • 1Key Laboratory of Special Electric Machines and High Voltage Apparatus in the Ministry of Education, Shenyang University of Technology, Shenyang, China.

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This study introduces a Regularized Random Forest with Recursive Selection (RFRS) method to reduce redundant features in AC contactor vibration signals. The RFRS method improves condition recognition accuracy by selecting the most significant features.

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Area of Science:

  • Engineering
  • Signal Processing
  • Machine Learning

Background:

  • Condition recognition of AC contactors relies on vibration signal analysis.
  • Time-frequency analysis of vibration signals often results in high feature redundancy, hindering recognition accuracy.
  • Existing feature selection methods may not be optimal for AC contactor vibration data.

Purpose of the Study:

  • To develop an effective feature selection method for AC contactor vibration signals.
  • To address the issue of feature redundancy in condition recognition.
  • To improve the accuracy and efficiency of AC contactor state recognition.

Main Methods:

  • Established an AC contactor vibration signal test platform.
  • Extracted time-frequency domain features.
  • Developed a Regularized Random Forest with Recursive Selection (RFRS) method, refining Random Forest (RF) with optimized stopping criteria and regularization.

Main Results:

  • The RFRS method effectively reduced feature set dimensionality.
  • Achieved high performance metrics: 87.37% Recall, 87.41% F1-Score, 88.38% Precision, and 85.74% Accuracy.
  • Outperformed Spearman's rank correlation, embedded, and filter methods in feature selection.

Conclusions:

  • The proposed RFRS method is an effective approach for AC contactor condition recognition.
  • Feature selection significantly enhances the accuracy of AC contactor state recognition.
  • This study provides a valuable tool for improving the reliability of AC contactor monitoring systems.