Incipient Chiller Fault Diagnosis Using an Optimized Least Squares Support Vector Machine With Gravitational Search Algorithm
Operational faults in centrifugal chillers will lead to high energy consumption, poor indoor thermal comfort, and low operational safety, and thus it is of significance to detect and diagnose the anomalies timely and effectively, especially for those at their incipient stages. The least squares supp...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2021-11-01
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| Series: | Frontiers in Energy Research |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fenrg.2021.755649/full |
| _version_ | 1818837318591578112 |
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| author | Yudong Xia Ju Zhao Qiang Ding Aipeng Jiang |
| author_facet | Yudong Xia Ju Zhao Qiang Ding Aipeng Jiang |
| author_sort | Yudong Xia |
| collection | DOAJ |
| description | Operational faults in centrifugal chillers will lead to high energy consumption, poor indoor thermal comfort, and low operational safety, and thus it is of significance to detect and diagnose the anomalies timely and effectively, especially for those at their incipient stages. The least squares support vector machine (LSSVM) has been regarded as an effective algorithm for multiclass classification. One of the most difficult issues in LSSVM is parameter tuning. Therefore, this paper reports a development of a gravitational search algorithm (GSA) optimized LSSVM method for incipient fault diagnosis in centrifugal chillers. Considering the inadequacies of conventional principle component analysis (PCA) algorithm for nonlinear data transformation, kernel principle component analysis (KPCA) was firstly employed to reduce the dimensionality of the original input data. Secondly, an optimized “one against one” multi-class LSSVM classifier was developed and its penalty constant and kernel bandwidth were tuned by GSA. Based on the fault samples of seven typical faults at their incipient stages in chillers from ASHRAE RP 1043, the proposed GSA optimized LSSVM fault diagnostic model was trained and validated. For the purpose of demonstrating the priority of the proposed fault diagnosis method, the obtained results were compared to that of using the LSSVM classifier optimized by another two algorithms, namely, the conventional cross-validation method and particle swarm optimizer. Results showed that the best fault diagnosis performance could be achieved using the proposed GSA-LSSVM classifier. The overall average fault diagnosis accuracy for the least severity faults was reported over 95%. |
| first_indexed | 2024-12-19T03:20:36Z |
| format | Article |
| id | doaj.art-769b0d082e9141afa5d861745e7b1b3a |
| institution | Directory Open Access Journal |
| issn | 2296-598X |
| language | English |
| last_indexed | 2024-12-19T03:20:36Z |
| publishDate | 2021-11-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Energy Research |
| spelling | doaj.art-769b0d082e9141afa5d861745e7b1b3a2022-12-21T20:37:47ZengFrontiers Media S.A.Frontiers in Energy Research2296-598X2021-11-01910.3389/fenrg.2021.755649755649Incipient Chiller Fault Diagnosis Using an Optimized Least Squares Support Vector Machine With Gravitational Search AlgorithmYudong Xia0Ju Zhao1Qiang Ding2Aipeng Jiang3School of Automation (School of Artificial Intelligence), Hangzhou Dianzi University, Hangzhou, ChinaShanghai Institute of Quality Inspection and Technical Research (SQI), Shanghai, ChinaSchool of Automation (School of Artificial Intelligence), Hangzhou Dianzi University, Hangzhou, ChinaSchool of Automation (School of Artificial Intelligence), Hangzhou Dianzi University, Hangzhou, ChinaOperational faults in centrifugal chillers will lead to high energy consumption, poor indoor thermal comfort, and low operational safety, and thus it is of significance to detect and diagnose the anomalies timely and effectively, especially for those at their incipient stages. The least squares support vector machine (LSSVM) has been regarded as an effective algorithm for multiclass classification. One of the most difficult issues in LSSVM is parameter tuning. Therefore, this paper reports a development of a gravitational search algorithm (GSA) optimized LSSVM method for incipient fault diagnosis in centrifugal chillers. Considering the inadequacies of conventional principle component analysis (PCA) algorithm for nonlinear data transformation, kernel principle component analysis (KPCA) was firstly employed to reduce the dimensionality of the original input data. Secondly, an optimized “one against one” multi-class LSSVM classifier was developed and its penalty constant and kernel bandwidth were tuned by GSA. Based on the fault samples of seven typical faults at their incipient stages in chillers from ASHRAE RP 1043, the proposed GSA optimized LSSVM fault diagnostic model was trained and validated. For the purpose of demonstrating the priority of the proposed fault diagnosis method, the obtained results were compared to that of using the LSSVM classifier optimized by another two algorithms, namely, the conventional cross-validation method and particle swarm optimizer. Results showed that the best fault diagnosis performance could be achieved using the proposed GSA-LSSVM classifier. The overall average fault diagnosis accuracy for the least severity faults was reported over 95%.https://www.frontiersin.org/articles/10.3389/fenrg.2021.755649/fullfault diagnosiswater chillersleast squares support vector machinekernel principle component analysisgravitational search algorithm |
| spellingShingle | Yudong Xia Ju Zhao Qiang Ding Aipeng Jiang Incipient Chiller Fault Diagnosis Using an Optimized Least Squares Support Vector Machine With Gravitational Search Algorithm Frontiers in Energy Research fault diagnosis water chillers least squares support vector machine kernel principle component analysis gravitational search algorithm |
| title | Incipient Chiller Fault Diagnosis Using an Optimized Least Squares Support Vector Machine With Gravitational Search Algorithm |
| title_full | Incipient Chiller Fault Diagnosis Using an Optimized Least Squares Support Vector Machine With Gravitational Search Algorithm |
| title_fullStr | Incipient Chiller Fault Diagnosis Using an Optimized Least Squares Support Vector Machine With Gravitational Search Algorithm |
| title_full_unstemmed | Incipient Chiller Fault Diagnosis Using an Optimized Least Squares Support Vector Machine With Gravitational Search Algorithm |
| title_short | Incipient Chiller Fault Diagnosis Using an Optimized Least Squares Support Vector Machine With Gravitational Search Algorithm |
| title_sort | incipient chiller fault diagnosis using an optimized least squares support vector machine with gravitational search algorithm |
| topic | fault diagnosis water chillers least squares support vector machine kernel principle component analysis gravitational search algorithm |
| url | https://www.frontiersin.org/articles/10.3389/fenrg.2021.755649/full |
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