基于XGBoost-RF的制冷剂泄漏故障检测与诊断
Refrigerant leakage fault detection and diagnosis based on extreme gradient boosting and random forest algorithm
摘要：
针对冷水机组运行中正常数据多于故障数据情况和制冷系统中最常见的制冷剂泄漏故障，本文采用极端梯度提升算法（XGBoost）建立故障检测模型，采用随机森林（RF）算法建立故障诊断模型，研究了检测阈值改变对检测模型的影响及有、无正常样本训练的诊断模型的对比。结果表明，在检测阈值设定为0.99时，可保证大部分故障样本均能被检测出来，且虚警率低，仅由故障数据训练得到的诊断模型整体性能最佳，可最大限度发挥检测模型和诊断模型的优势。
Abstract：
Aiming at the situation where the normal data in the chiller unit exceeds the fault data and the most common refrigerant leakage fault in the refrigeration system, this paper uses the extreme gradient boosting (XGBoost) algorithm to establish the fault detection model, and the random forest (RF) algorithm to establish the fault diagnosis model, and studies the influence of the detection threshold change on the detection model and the comparison of the diagnostic model with and without normal sample training. The results show that when the detection threshold is set to 0.99, most of the fault samples can be detected, the false alarm rate is low, and the diagnostic model trained only by the fault data makes the best overall performance, which can maximize the advantages of the detection model and diagnostic model.
Keywords:chiller;refrigerantleakage;faultdetectionanddiagnosis;extremegradientboosting(XGBoost);randomforest(RF);threshold