Research on Health State Prediction Method Based on Mixed Sampling
In response to the serious impact of imbalanced device health status sample data on health status prediction,a mixed sampling based approach is proposed to achieve data balance and improve prediction performance.A sample data balance process based on the mixed sampling method is designed.By using the Borderline SMOTE algorithm to supplement the number of minority class samples,and using the improved K-means algorithm to delete the majority class samples,after removing redundant data,a relatively balanced dataset is formed and provided to the classifier.The experimental data shows that both under sampling and Oversampling can improve the evaluation indicators AUC and G-mean;Using a mixed approach to balance the data and improve the evaluation indicators more significantly.The results indicate that this method can significantly improve the prediction effect of equipment health status,and has important reference value for equipment management departments to achieve precise maintenance.
imbalanced dataoversamplingunder samplingmixed samplinghealth status prediction
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