Fault Type Identification and Fault Refinement Diagnosis Model of Variable Refrigerant Flow System
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 t...
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| Format: | Article |
| Language: | zho |
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Journal of Refrigeration Magazines Agency Co., Ltd.
2021-01-01
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| Series: | Zhileng xuebao |
| Subjects: | |
| Online Access: | http://www.zhilengxuebao.com/thesisDetails#10.3969/j.issn.0253-4339.2021.02.118 |
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| author | Liu Qian Li Zhengfei Ding Xinlei Chen Huanxin Wang Yuzhou Xu Chang |
| author_facet | Liu Qian Li Zhengfei Ding Xinlei Chen Huanxin Wang Yuzhou Xu Chang |
| author_sort | Liu Qian |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-f66cd3f2f4944686a0d4de2d26d9d6e2 |
| institution | OA Journals |
| issn | 0253-4339 |
| language | zho |
| publishDate | 2021-01-01 |
| publisher | Journal of Refrigeration Magazines Agency Co., Ltd. |
| record_format | Article |
| series | Zhileng xuebao |
| spelling | doaj-art-f66cd3f2f4944686a0d4de2d26d9d6e22025-08-20T02:02:57ZzhoJournal of Refrigeration Magazines Agency Co., Ltd.Zhileng xuebao0253-43392021-01-014266506301Fault Type Identification and Fault Refinement Diagnosis Model of Variable Refrigerant Flow SystemLiu QianLi ZhengfeiDing XinleiChen HuanxinWang YuzhouXu ChangTo 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.http://www.zhilengxuebao.com/thesisDetails#10.3969/j.issn.0253-4339.2021.02.118variable refrigerant flow systemvapor injectionvapor injection pressuresuperheating degreerefrigerant flow rate |
| spellingShingle | Liu Qian Li Zhengfei Ding Xinlei Chen Huanxin Wang Yuzhou Xu Chang Fault Type Identification and Fault Refinement Diagnosis Model of Variable Refrigerant Flow System Zhileng xuebao variable refrigerant flow system vapor injection vapor injection pressure superheating degree refrigerant flow rate |
| title | Fault Type Identification and Fault Refinement Diagnosis Model of Variable Refrigerant Flow System |
| title_full | Fault Type Identification and Fault Refinement Diagnosis Model of Variable Refrigerant Flow System |
| title_fullStr | Fault Type Identification and Fault Refinement Diagnosis Model of Variable Refrigerant Flow System |
| title_full_unstemmed | Fault Type Identification and Fault Refinement Diagnosis Model of Variable Refrigerant Flow System |
| title_short | Fault Type Identification and Fault Refinement Diagnosis Model of Variable Refrigerant Flow System |
| title_sort | fault type identification and fault refinement diagnosis model of variable refrigerant flow system |
| topic | variable refrigerant flow system vapor injection vapor injection pressure superheating degree refrigerant flow rate |
| url | http://www.zhilengxuebao.com/thesisDetails#10.3969/j.issn.0253-4339.2021.02.118 |
| work_keys_str_mv | AT liuqian faulttypeidentificationandfaultrefinementdiagnosismodelofvariablerefrigerantflowsystem AT lizhengfei faulttypeidentificationandfaultrefinementdiagnosismodelofvariablerefrigerantflowsystem AT dingxinlei faulttypeidentificationandfaultrefinementdiagnosismodelofvariablerefrigerantflowsystem AT chenhuanxin faulttypeidentificationandfaultrefinementdiagnosismodelofvariablerefrigerantflowsystem AT wangyuzhou faulttypeidentificationandfaultrefinementdiagnosismodelofvariablerefrigerantflowsystem AT xuchang faulttypeidentificationandfaultrefinementdiagnosismodelofvariablerefrigerantflowsystem |