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              1. 暖通空调杂志社>期刊目次>2018年>第1期

                基于支持向量机的多联机系统制冷剂充注量故障检测与诊断

                SVM-based FDD method for refrigerant charge in variable refrigerant flow system

                黄倩云[1] 陈焕新[1] 孙劭波[1] 刘江岩[1] 李冠男[1] 李绍斌[2]
                [1]华中科技大学 [2]珠海格力电器股份有限公司

                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%。

                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

                Abstract:

                Based on the support vector machine (SVM) algorithm, 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%.

                Keywords:multisplitunit,refrigerantcharge,faultdetectionanddiagnosis,supportvectormachine,parameteroptimization

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