A Fault Isolation Method via Classification and Regression Tree-Based Variable Ranking for Drum-Type Steam Boiler in Thermal Power Plant Article Swipe
Jungwon Yu
,
Jaeyel Jang
,
Jaeyeong Yoo
,
June Ho Park
,
Sungshin Kim
·
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.3390/en11051142
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.3390/en11051142
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.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/en11051142
- https://www.mdpi.com/1996-1073/11/5/1142/pdf?version=1525427927
- OA Status
- gold
- Cited By
- 11
- References
- 42
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2800870258
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2800870258Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/en11051142Digital Object Identifier
- Title
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A Fault Isolation Method via Classification and Regression Tree-Based Variable Ranking for Drum-Type Steam Boiler in Thermal Power PlantWork title
- Type
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articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2018Year of publication
- Publication date
-
2018-05-03Full publication date if available
- Authors
-
Jungwon Yu, Jaeyel Jang, Jaeyeong Yoo, June Ho Park, Sungshin KimList of authors in order
- Landing page
-
https://doi.org/10.3390/en11051142Publisher landing page
- PDF URL
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https://www.mdpi.com/1996-1073/11/5/1142/pdf?version=1525427927Direct link to full text PDF
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://www.mdpi.com/1996-1073/11/5/1142/pdf?version=1525427927Direct OA link when available
- Concepts
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Boiler (water heating), Fault detection and isolation, Classifier (UML), Decision tree learning, Decision tree, Computer science, Data mining, Artificial intelligence, Pattern recognition (psychology), Engineering, Waste management, ActuatorTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
11Total citation count in OpenAlex
- Citations by year (recent)
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2025: 2, 2024: 2, 2023: 2, 2022: 4, 2019: 1Per-year citation counts (last 5 years)
- References (count)
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42Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W2559748060, https://openalex.org/W2047090954, https://openalex.org/W2069672128, https://openalex.org/W3141321923, https://openalex.org/W2594248159, https://openalex.org/W2065659275, https://openalex.org/W2023534760, https://openalex.org/W2097232731, https://openalex.org/W2007540151, https://openalex.org/W6756688618, https://openalex.org/W799451717, https://openalex.org/W2133863004, https://openalex.org/W2050987342, https://openalex.org/W1970090588, https://openalex.org/W2021353881, https://openalex.org/W2095441350, https://openalex.org/W2027051742, https://openalex.org/W1991519797, https://openalex.org/W2019180557, https://openalex.org/W2191346030, https://openalex.org/W2155694896, https://openalex.org/W2783033425, https://openalex.org/W2323321467, https://openalex.org/W1991424144, https://openalex.org/W2020405858, https://openalex.org/W2207741098, https://openalex.org/W2487468712, https://openalex.org/W2085145501, https://openalex.org/W2120716022, https://openalex.org/W2114770214, https://openalex.org/W2003139437, https://openalex.org/W2072405524, https://openalex.org/W1994505190, https://openalex.org/W1974717100, https://openalex.org/W2475447835, https://openalex.org/W167689708, https://openalex.org/W2478693793, https://openalex.org/W2470166864, https://openalex.org/W2044610659, https://openalex.org/W2076769400, https://openalex.org/W1972084022, https://openalex.org/W2904009550 |
| referenced_works_count | 42 |
| abstract_inverted_index.a | 24, 58 |
| abstract_inverted_index.In | 19, 43 |
| abstract_inverted_index.MW | 162 |
| abstract_inverted_index.To | 145 |
| abstract_inverted_index.be | 140 |
| abstract_inverted_index.by | 52, 100 |
| abstract_inverted_index.in | 31, 115 |
| abstract_inverted_index.is | 62 |
| abstract_inverted_index.of | 4, 64 |
| abstract_inverted_index.on | 134 |
| abstract_inverted_index.to | 57, 73, 142, 156 |
| abstract_inverted_index.we | 22 |
| abstract_inverted_index.250 | 161 |
| abstract_inverted_index.The | 91 |
| abstract_inverted_index.and | 2, 17, 37, 66, 151, 160 |
| abstract_inverted_index.are | 7, 50, 85, 97, 106, 154 |
| abstract_inverted_index.can | 139 |
| abstract_inverted_index.for | 9, 28, 69, 81, 94 |
| abstract_inverted_index.not | 98, 124 |
| abstract_inverted_index.the | 44, 54, 88, 104, 109, 116, 120, 130, 135, 147, 149, 170, 179, 183 |
| abstract_inverted_index.two | 157 |
| abstract_inverted_index.via | 35 |
| abstract_inverted_index.CART | 55, 137 |
| abstract_inverted_index.does | 123 |
| abstract_inverted_index.each | 82 |
| abstract_inverted_index.from | 87, 108, 126 |
| abstract_inverted_index.more | 14, 176 |
| abstract_inverted_index.only | 114 |
| abstract_inverted_index.show | 168 |
| abstract_inverted_index.than | 182 |
| abstract_inverted_index.that | 169 |
| abstract_inverted_index.this | 20 |
| abstract_inverted_index.tree | 39 |
| abstract_inverted_index.with | 111 |
| abstract_inverted_index.based | 133 |
| abstract_inverted_index.fault | 25 |
| abstract_inverted_index.input | 83, 118 |
| abstract_inverted_index.power | 33 |
| abstract_inverted_index.steam | 29, 164 |
| abstract_inverted_index.then, | 72 |
| abstract_inverted_index.trees | 49, 110 |
| abstract_inverted_index.which | 61 |
| abstract_inverted_index.binary | 47 |
| abstract_inverted_index.effect | 181 |
| abstract_inverted_index.faults | 6 |
| abstract_inverted_index.faulty | 67, 75, 101, 174 |
| abstract_inverted_index.method | 27, 122, 172 |
| abstract_inverted_index.normal | 65 |
| abstract_inverted_index.paper, | 21 |
| abstract_inverted_index.plants | 34 |
| abstract_inverted_index.space; | 119 |
| abstract_inverted_index.suffer | 125 |
| abstract_inverted_index.trees. | 90 |
| abstract_inverted_index.values | 80, 93, 105 |
| abstract_inverted_index.applied | 155 |
| abstract_inverted_index.because | 103 |
| abstract_inverted_index.boiler. | 165 |
| abstract_inverted_index.boilers | 30 |
| abstract_inverted_index.clearly | 177 |
| abstract_inverted_index.complex | 11 |
| abstract_inverted_index.confirm | 146 |
| abstract_inverted_index.dataset | 60 |
| abstract_inverted_index.effect. | 128 |
| abstract_inverted_index.method, | 46, 132 |
| abstract_inverted_index.methods | 153 |
| abstract_inverted_index.perform | 74 |
| abstract_inverted_index.propose | 23 |
| abstract_inverted_index.results | 167 |
| abstract_inverted_index.safely, | 15 |
| abstract_inverted_index.samples | 68 |
| abstract_inverted_index.thermal | 32 |
| abstract_inverted_index.without | 178 |
| abstract_inverted_index.Accurate | 0 |
| abstract_inverted_index.applying | 53 |
| abstract_inverted_index.composed | 63 |
| abstract_inverted_index.decision | 112 |
| abstract_inverted_index.isolates | 173 |
| abstract_inverted_index.learning | 71 |
| abstract_inverted_index.methods. | 185 |
| abstract_inverted_index.original | 117 |
| abstract_inverted_index.possible | 5 |
| abstract_inverted_index.problems | 159 |
| abstract_inverted_index.proposed | 45, 121, 131, 150, 171 |
| abstract_inverted_index.ranking. | 42 |
| abstract_inverted_index.smearing | 127, 180 |
| abstract_inverted_index.training | 59 |
| abstract_inverted_index.variable | 41, 76, 78, 84 |
| abstract_inverted_index.algorithm | 56 |
| abstract_inverted_index.benchmark | 158 |
| abstract_inverted_index.detection | 1 |
| abstract_inverted_index.drum-type | 163 |
| abstract_inverted_index.extracted | 86, 107 |
| abstract_inverted_index.isolation | 3, 26 |
| abstract_inverted_index.nonlinear | 143 |
| abstract_inverted_index.operating | 10 |
| abstract_inverted_index.processes | 13 |
| abstract_inverted_index.variables | 96, 175 |
| abstract_inverted_index.applicable | 141 |
| abstract_inverted_index.boundaries | 113 |
| abstract_inverted_index.classifier | 70 |
| abstract_inverted_index.comparison | 152, 184 |
| abstract_inverted_index.importance | 79, 92 |
| abstract_inverted_index.industrial | 12 |
| abstract_inverted_index.influenced | 99 |
| abstract_inverted_index.isolation, | 77 |
| abstract_inverted_index.non-faulty | 95 |
| abstract_inverted_index.processes. | 144 |
| abstract_inverted_index.regression | 38 |
| abstract_inverted_index.variables, | 102 |
| abstract_inverted_index.classifier, | 138 |
| abstract_inverted_index.constructed | 51, 89 |
| abstract_inverted_index.(CART)-based | 40 |
| abstract_inverted_index.Experimental | 166 |
| abstract_inverted_index.Furthermore, | 129 |
| abstract_inverted_index.effectively, | 16 |
| abstract_inverted_index.economically. | 18 |
| abstract_inverted_index.indispensable | 8 |
| abstract_inverted_index.nonparametric | 136 |
| abstract_inverted_index.classification | 36, 48 |
| abstract_inverted_index.effectiveness, | 148 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 90 |
| corresponding_author_ids | https://openalex.org/A5047492763 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 5 |
| corresponding_institution_ids | https://openalex.org/I161217753 |
| citation_normalized_percentile.value | 0.47388041 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |