基于XGBoost的冷水机组不平衡数据故障诊断

Abstract

The chiller operating data has unbalanced,non-Gaussian,non-linear,noise-containing characteristics,which poses a challenge for data-based chiller fault diagnosis.Aiming at these characteristics,a chiller fault diagnosis method based on Minority Oversampling under Local Area Density and e Xtreme Gradient Boosting is proposed to chiller fault diagnosis to overcome sample distribution imbalance.Introduce cost-sensitive learning theory to increase the recall rate of important faults.The simulations of seven fault monitoring data commonly used in centrifugal chillers show that XGBoost can better classify chiller status monitoring data compared to the control group.The MOLAD-XGBoost composite model can effectively deal with data imbalance problems; Cost sensitive weights can effectively increase the recall rate for critical failures.