A Fault Isolation Method via Classification and Regression Tree-Based Variable Ranking for Drum-Type Steam Boiler in Thermal Power Plant
Accurate detection and isolation of possible faults are indispensable for operating complex industrial processes more safely, effectively, and economically. In this paper, we propose a fault isolation method for steam boilers in thermal power plants via classification and regression tree (CART)-base...
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| Format: | Article |
| Language: | English |
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MDPI AG
2018-05-01
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| Series: | Energies |
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| Online Access: | http://www.mdpi.com/1996-1073/11/5/1142 |
| _version_ | 1828154918001704960 |
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| author | Jungwon Yu Jaeyel Jang Jaeyeong Yoo June Ho Park Sungshin Kim |
| author_facet | Jungwon Yu Jaeyel Jang Jaeyeong Yoo June Ho Park Sungshin Kim |
| author_sort | Jungwon Yu |
| collection | DOAJ |
| description | Accurate detection and isolation of possible faults are indispensable for operating complex industrial processes more safely, effectively, and economically. In this paper, we propose a fault isolation method for steam boilers in thermal power plants via classification and regression tree (CART)-based variable ranking. In the proposed method, binary classification trees are constructed by applying the CART algorithm to a training dataset which is composed of normal and faulty samples for classifier learning then, to perform faulty variable isolation, variable importance values for each input variable are extracted from the constructed trees. The importance values for non-faulty variables are not influenced by faulty variables, because the values are extracted from the trees with decision boundaries only in the original input space; the proposed method does not suffer from smearing effect. Furthermore, the proposed method, based on the nonparametric CART classifier, can be applicable to nonlinear processes. To confirm the effectiveness, the proposed and comparison methods are applied to two benchmark problems and 250 MW drum-type steam boiler. Experimental results show that the proposed method isolates faulty variables more clearly without the smearing effect than the comparison methods. |
| first_indexed | 2024-04-11T22:45:59Z |
| format | Article |
| id | doaj.art-27a627bfb582436885eb6bbbdae92acb |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-11T22:45:59Z |
| publishDate | 2018-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-27a627bfb582436885eb6bbbdae92acb2022-12-22T03:58:46ZengMDPI AGEnergies1996-10732018-05-01115114210.3390/en11051142en11051142A Fault Isolation Method via Classification and Regression Tree-Based Variable Ranking for Drum-Type Steam Boiler in Thermal Power PlantJungwon Yu0Jaeyel Jang1Jaeyeong Yoo2June Ho Park3Sungshin Kim4Department of Electrical and Computer Engineering, Busan National University, Busan 46241, South KoreaTechnology & Information Department, Technical Solution Center, Korea East-West Power Co., Ltd., Dangjin 31700, South KoreaChief Technology Officer (CTO), XEONET Co., Ltd., Seongnam 13216, South KoreaDepartment of Electrical and Computer Engineering, Busan National University, Busan 46241, South KoreaDepartment of Electrical and Computer Engineering, Busan National University, Busan 46241, South KoreaAccurate detection and isolation of possible faults are indispensable for operating complex industrial processes more safely, effectively, and economically. In this paper, we propose a fault isolation method for steam boilers in thermal power plants via classification and regression tree (CART)-based variable ranking. In the proposed method, binary classification trees are constructed by applying the CART algorithm to a training dataset which is composed of normal and faulty samples for classifier learning then, to perform faulty variable isolation, variable importance values for each input variable are extracted from the constructed trees. The importance values for non-faulty variables are not influenced by faulty variables, because the values are extracted from the trees with decision boundaries only in the original input space; the proposed method does not suffer from smearing effect. Furthermore, the proposed method, based on the nonparametric CART classifier, can be applicable to nonlinear processes. To confirm the effectiveness, the proposed and comparison methods are applied to two benchmark problems and 250 MW drum-type steam boiler. Experimental results show that the proposed method isolates faulty variables more clearly without the smearing effect than the comparison methods.http://www.mdpi.com/1996-1073/11/5/1142drum-type steam boilerfault isolationclassification and regression treevariable rankingsmearing effect |
| spellingShingle | Jungwon Yu Jaeyel Jang Jaeyeong Yoo June Ho Park Sungshin Kim A Fault Isolation Method via Classification and Regression Tree-Based Variable Ranking for Drum-Type Steam Boiler in Thermal Power Plant Energies drum-type steam boiler fault isolation classification and regression tree variable ranking smearing effect |
| title | A Fault Isolation Method via Classification and Regression Tree-Based Variable Ranking for Drum-Type Steam Boiler in Thermal Power Plant |
| title_full | A Fault Isolation Method via Classification and Regression Tree-Based Variable Ranking for Drum-Type Steam Boiler in Thermal Power Plant |
| title_fullStr | A Fault Isolation Method via Classification and Regression Tree-Based Variable Ranking for Drum-Type Steam Boiler in Thermal Power Plant |
| title_full_unstemmed | A Fault Isolation Method via Classification and Regression Tree-Based Variable Ranking for Drum-Type Steam Boiler in Thermal Power Plant |
| title_short | A Fault Isolation Method via Classification and Regression Tree-Based Variable Ranking for Drum-Type Steam Boiler in Thermal Power Plant |
| title_sort | fault isolation method via classification and regression tree based variable ranking for drum type steam boiler in thermal power plant |
| topic | drum-type steam boiler fault isolation classification and regression tree variable ranking smearing effect |
| url | http://www.mdpi.com/1996-1073/11/5/1142 |
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