Diagnosis methods based on machine learning and deep learning are widely used in the field of motor fault diagnosis. However, due to the fact that the data imbalance caused by the high cost of obtaining fault data will lead to insufficient generalization performance of the diagnosis method. In response to this problem, a motor fault monitoring system is proposed, which includes a fault diagnosis method (Xgb_LR) based on the optimized gradient boosting decision tree (Xgboost) and logistic regression (LR) fusion model and a data augmentation method named data simulation neighborhood interpolation(DSNI). The Xgb_LR method combines the advantages of the two models and has positive adaptability to imbalanced data. Simultaneously, the DSNI method can be used as an auxiliary method of the diagnosis method to reduce the impact of data imbalance by expanding the original data (signal). Simulation experiments verify the effectiveness of the proposed methods.

Liling Ma;Tianyi Wang;Xiaoran Liu;Junzheng Wang;Wei Shen

School of Automation,Beijing Insti-tute of Technology,Beijing 100081,China

北京理工大学学报（英文版）

[1]Liling Ma;Tianyi Wang;Xiaoran Liu;Junzheng Wang;Wei Shen-.Fault Diagnosis Method Based on Xgboost and LR Fusion Model under Data Imbalance)[J].北京理工大学学报（英文版）,2022(04):401-412

Imbalance,Xgb,DSNI

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