| 摘要: |
| 本文针对多联机系统实际运行中可能出现的多故障并发问题，提出一种结合线性判别分析（LDA）和随机森林（RF）算法的多故障诊断策略，可以在完成故障类型识别后，自适应地根据故障类型选择最佳细化诊断模型，进一步诊断出故障发生水平或故障发生原因。首先，在不同的制冷和制热工况下，引入四通阀故障、电子膨胀阀故障、制冷剂充注量故障，并按照7:3的比例划分为训练集和测试集，利用训练集建立基于RF算法的故障类型识别模型；然后，利用LDA方法对训练集中3类故障的特征分别进行降维，并利用降维后的训练集建立故障细化诊断模型；最后，测试集中的样本数据在经过故障类型识别后，根据识别结果自适应地输入至最优故障细化诊断模型。结果显示：故障类型识别模型在测试集上的准确率达到99.99%，3类故障细化诊断准确率分别为96.12%、100%、97.44%，说明该策略能够较好的完成针对多联机系统的多类型故障诊断任务。 |
| 关键词: 多联机 故障识别 故障详细诊断 随机森林 LDA |
| DOI： |
| 投稿时间：2020-03-04 修订日期：2020-10-09
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| 基金项目:国家自然科学基金(51876070,51576074) 资助项目。 |
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| Fault Type Identification and Fault Refinement Diagnosis Model of Variable Refrigerant Flow System |
| Liu Qian,Li Zhengfei,Ding Xinlei,Chen Huanxin,Wang Yuzhou,Xu Chang |
| (School of Energy and Power Engineering, Huazhong University of Science and Technology) |
| Abstract: |
| To address the multi-fault concurrency problem, which may occur in the actual operation of variable refrigerant flow air-conditioning systems, a multi-fault diagnosis strategy combining linear discriminant analysis (LDA) and random forest (RF) algorithms was proposed. After the completion of fault type identification, the best detailed diagnosis model was adaptively selected according to the fault type to further diagnose the fault level or determine the cause of the fault in detail. First, the original data set containing normal operating conditions, four-way valve failure, electronic expansion valve failure, and refrigerant charge failure was divided into a training set and test set with a ratio of 7:3. The training set was used to establish an RF algorithm-based fault type identification model. Then, the LDA method was used to reduce the dimensions of the three types of faults in the training set. The training set after the dimension reduction was used to establish a fault refinement diagnostic model. Finally, after the sample data in the test set were identified by the fault type, the test sample could adaptively input different fault refinement diagnostic models according to the recognition results. The results showed that the accuracy rate of the fault type identification model on the test set reached 99.99%, while the refinement diagnostic accuracy rates of the three types of faults were 96.12%, 100%, and 97.44%, respectively. The results indicated that the strategy proposed in this paper could better complete multiple types of fault diagnosis tasks for different refrigerant flow systems. |
| Key words: variable refrigerant flow system vapor injection vapor injection pressure superheating degree refrigerant flow rate |
