摘要
利用支持向量机算法,建立了制冷剂充注量故障检测与诊断模型。采用网格搜索和十折交叉验证方法优化模型,通过测试数据验证模型性能。结果表明,制冷剂充注不足时的故障检测与诊断准确率较高,但制冷剂充注过量时准确率明显偏低。经过优化后,制冷剂充注量故障检测与诊断的总准确率由82.2%提高到94.6%。
Based on the support vector machine （SVM） aIgorithm, establishes a fault detection and diagnosis （FDD） model of refrigerant charge. Optimizes the model using grid search method and ten-fold cross validation method, and verifies model performance by testing data. The results show that the accuracy rate of fault detection and diagnosis is very high when the refrigerant charge is insufficient. But the accuracy rate is obviously low when the refrigerant charge is over charged. After the optimization, the total accuracy rate increases from 82.2% to 94.6%.
出处
《暖通空调》
2018年第1期91-95,103,共6页
Heating Ventilating & Air Conditioning
基金
国家自然科学基金资助项目(编号:51576074
51328602)
空调设备及系统运行节能国家重点实验室开放基金资助项目(编号:SKLACKF201606)
关键词
多联机
制冷剂充注量
故障检测与诊断
支持向量机
参数寻优
multi split unit, refrigerant charge, fault detection and diagnosis, support vector machine, parameter optimization
