FAULT DIAGNOSIS OF UNBALANCED DATA OF CHILLERS BASED ON XGBOOST
The chiller operating data has unbalanced,non-Gaussian,non-linear,noise-containing characteristics,which poses a challenge for data-based chiller fault diagnosis.Aiming at these characteristics,a chiller fault diagnosis method based on Minority Oversampling under Local Area Density and e Xtreme Grad...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | zho |
| Published: |
Editorial Office of Journal of Mechanical Strength
2021-01-01
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| Series: | Jixie qiangdu |
| Subjects: | |
| Online Access: | http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2021.01.004 |
| _version_ | 1797767677584867328 |
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| author | PAN Jin DING Qiang JIANG AiPeng CHEN YueZen XIA YuDong |
| author_facet | PAN Jin DING Qiang JIANG AiPeng CHEN YueZen XIA YuDong |
| author_sort | PAN Jin |
| collection | DOAJ |
| description | The chiller operating data has unbalanced,non-Gaussian,non-linear,noise-containing characteristics,which poses a challenge for data-based chiller fault diagnosis.Aiming at these characteristics,a chiller fault diagnosis method based on Minority Oversampling under Local Area Density and e Xtreme Gradient Boosting is proposed to chiller fault diagnosis to overcome sample distribution imbalance.Introduce cost-sensitive learning theory to increase the recall rate of important faults.The simulations of seven fault monitoring data commonly used in centrifugal chillers show that XGBoost can better classify chiller status monitoring data compared to the control group.The MOLAD-XGBoost composite model can effectively deal with data imbalance problems; Cost sensitive weights can effectively increase the recall rate for critical failures. |
| first_indexed | 2024-03-12T20:42:19Z |
| format | Article |
| id | doaj.art-8f0aaff6839a48128b7aa6b39f379d2f |
| institution | Directory Open Access Journal |
| issn | 1001-9669 |
| language | zho |
| last_indexed | 2024-03-12T20:42:19Z |
| publishDate | 2021-01-01 |
| publisher | Editorial Office of Journal of Mechanical Strength |
| record_format | Article |
| series | Jixie qiangdu |
| spelling | doaj.art-8f0aaff6839a48128b7aa6b39f379d2f2023-08-01T07:52:33ZzhoEditorial Office of Journal of Mechanical StrengthJixie qiangdu1001-96692021-01-0143273330609714FAULT DIAGNOSIS OF UNBALANCED DATA OF CHILLERS BASED ON XGBOOSTPAN JinDING QiangJIANG AiPengCHEN YueZenXIA YuDongThe chiller operating data has unbalanced,non-Gaussian,non-linear,noise-containing characteristics,which poses a challenge for data-based chiller fault diagnosis.Aiming at these characteristics,a chiller fault diagnosis method based on Minority Oversampling under Local Area Density and e Xtreme Gradient Boosting is proposed to chiller fault diagnosis to overcome sample distribution imbalance.Introduce cost-sensitive learning theory to increase the recall rate of important faults.The simulations of seven fault monitoring data commonly used in centrifugal chillers show that XGBoost can better classify chiller status monitoring data compared to the control group.The MOLAD-XGBoost composite model can effectively deal with data imbalance problems; Cost sensitive weights can effectively increase the recall rate for critical failures.http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2021.01.004Fault diagnosis;Chiller;Extreme gradient boosting;Unbalanced data;Oversampling |
| spellingShingle | PAN Jin DING Qiang JIANG AiPeng CHEN YueZen XIA YuDong FAULT DIAGNOSIS OF UNBALANCED DATA OF CHILLERS BASED ON XGBOOST Jixie qiangdu Fault diagnosis;Chiller;Extreme gradient boosting;Unbalanced data;Oversampling |
| title | FAULT DIAGNOSIS OF UNBALANCED DATA OF CHILLERS BASED ON XGBOOST |
| title_full | FAULT DIAGNOSIS OF UNBALANCED DATA OF CHILLERS BASED ON XGBOOST |
| title_fullStr | FAULT DIAGNOSIS OF UNBALANCED DATA OF CHILLERS BASED ON XGBOOST |
| title_full_unstemmed | FAULT DIAGNOSIS OF UNBALANCED DATA OF CHILLERS BASED ON XGBOOST |
| title_short | FAULT DIAGNOSIS OF UNBALANCED DATA OF CHILLERS BASED ON XGBOOST |
| title_sort | fault diagnosis of unbalanced data of chillers based on xgboost |
| topic | Fault diagnosis;Chiller;Extreme gradient boosting;Unbalanced data;Oversampling |
| url | http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2021.01.004 |
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