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| 基于LSSVM的制冷系统故障诊断 |
| Fault Diagnosis for Refrigeration System Based on LS-SVM |
| 投稿时间：2015-12-08 |
| DOI：10.13259/j.cnki.eri.2017.01.001 |
| 中文关键词: 制冷系统 故障诊断 最小二乘支持向量机 误差反向传播 支持向量机 |
| 英文关键词:refrigeration system fault diagnosis least squares support vector machine error back-propagation support vector machine |
| 基金项目:国家自然科学基金项目（51506125） |
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| 中文摘要: |
| 为了提高制冷系统故障诊断速度及准确性，提出了基于最小二乘支持向量机（LS-SVM）的制冷系统故障诊断模型，并采用ASHRAE制冷系统故障模拟实验数据进行模型训练与验证.对一台 90冷吨（约316 kW）的离心式冷水机组的7类制冷循环典型故障进行了实验.研究结果表明，LS-SVM模型对制冷系统七类故障的总体诊断正确率比支持向量机（SVM）诊断模型、误差反向传播（BP）神经网络诊断模型分别提高0.12%和1.32%；尽管对个别局部故障（冷凝器结垢、冷凝器水流量不足、制冷剂含不凝性气体）的诊断性能较SVM模型的略有下降，但对系统故障的诊断性能均有较大改善，特别是对制冷剂泄漏/不足故障；诊断耗时比SVM模型减少近一半，快速性亦有所改善.可见，LS-SVM模型在制冷系统故障诊断中具有良好的应用前景. |
| 英文摘要: |
| In order to improve the fault diagnosis speed and accuracy for refrigeration system,a fault diagnosis model based on least squares support vector machine(LS-SVM) was proposed.American Society of Heating,Refrigerating,and Air-conditioning Engineering(ASHRAE) refrigeration system fault simulation data was used for the model training and validation.The experiments of a centrifugal chiller of 90 tons with seven types of typical faults were conducted.The results showed that the overall diagnostic accuracy of LS-SVM model for seven types of faults increased by 0.12% and 1.32% respectively,compared with support vector machine(SVM) diagnosis model and error back-propagation(BP) neural network model.Although diagnostic performance of LS-SVM model for individual component-level fault(ConFoul/ReduCF/NonCon) was low slightly compared with SVM model,the diagnosis performance for system-level were greatly improved,especially for refrigerant leakage or lack of refrigerant.The diagnosis time of LS-SVM model reduced nearly half than that of SVM model.At the same time,its rapidity improved.Therefore,LS-SVM diagnostic model had good application in the fault diagnosis of refrigeration system. |
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