Research on fault diagnosis method of ventilation network based on machine learning
The machine learning algorithm predicts unknown data by learning known data. Most of the existing fault diagnosis methods of ventilation system focus on a machine learning algorithm, which can not guarantee the selected algorithm to be optimal. In order to solve this problem, eight machine learning...
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| Format: | Article |
| Language: | zho |
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Editorial Department of Industry and Mine Automation
2022-03-01
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| Series: | Gong-kuang zidonghua |
| Subjects: | |
| Online Access: | http://www.gkzdh.cn/article/doi/10.13272/j.issn.1671-251x.2021120093 |
| _version_ | 1797868962342502400 |
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| author | ZHANG Lang ZHANG Yinghui ZHANG Yibin LI Zuo |
| author_facet | ZHANG Lang ZHANG Yinghui ZHANG Yibin LI Zuo |
| author_sort | ZHANG Lang |
| collection | DOAJ |
| description | The machine learning algorithm predicts unknown data by learning known data. Most of the existing fault diagnosis methods of ventilation system focus on a machine learning algorithm, which can not guarantee the selected algorithm to be optimal. In order to solve this problem, eight machine learning algorithms are compared, and three algorithms, support vector machine ( SVM), random forest and neural network, are selected to study the fault diagnosis of ventilation network. According to the actual layout of the mine ventilation system, a ventilation network pipeline model is constructed according to the criteria of geometric similarity, motion similarity and dynamic similarity. A ventilation network consisting of pipeline network branches and pipeline network nodes is obtained, and air volume data is obtained through experiments, and the data is preprocessed by a standardized method. Through cross-validation and grid search, the parameters of ventilation network fault diagnosis model based on SVM, random forest and neural network are optimized. The results of experiment and field test show that the accuracy of ventilation network fault diagnosis model based on SVM, random forest and neural network are 0.89, 0.88 and 0.95 respectively on the test set of experimental platform, and 0.86, 0.90 and 0.96 respectively on the test set of coal mine field. The neural network model has the best fault diagnosis effect. 120 sets of fresh air volume data collected in coal mine field are input into neural network model for prediction, and the fault diagnosis accuracy rate reaches 0.98, which verifies the feasibility and accuracy of the ventilation network fault diagnosis model based on neural network. |
| first_indexed | 2024-04-10T00:04:05Z |
| format | Article |
| id | doaj.art-11603f21e2224220b841b06f986f0edd |
| institution | Directory Open Access Journal |
| issn | 1671-251X |
| language | zho |
| last_indexed | 2024-04-10T00:04:05Z |
| publishDate | 2022-03-01 |
| publisher | Editorial Department of Industry and Mine Automation |
| record_format | Article |
| series | Gong-kuang zidonghua |
| spelling | doaj.art-11603f21e2224220b841b06f986f0edd2023-03-17T01:02:59ZzhoEditorial Department of Industry and Mine AutomationGong-kuang zidonghua1671-251X2022-03-01483919810.13272/j.issn.1671-251x.2021120093Research on fault diagnosis method of ventilation network based on machine learningZHANG LangZHANG YinghuiZHANG YibinLI ZuoThe machine learning algorithm predicts unknown data by learning known data. Most of the existing fault diagnosis methods of ventilation system focus on a machine learning algorithm, which can not guarantee the selected algorithm to be optimal. In order to solve this problem, eight machine learning algorithms are compared, and three algorithms, support vector machine ( SVM), random forest and neural network, are selected to study the fault diagnosis of ventilation network. According to the actual layout of the mine ventilation system, a ventilation network pipeline model is constructed according to the criteria of geometric similarity, motion similarity and dynamic similarity. A ventilation network consisting of pipeline network branches and pipeline network nodes is obtained, and air volume data is obtained through experiments, and the data is preprocessed by a standardized method. Through cross-validation and grid search, the parameters of ventilation network fault diagnosis model based on SVM, random forest and neural network are optimized. The results of experiment and field test show that the accuracy of ventilation network fault diagnosis model based on SVM, random forest and neural network are 0.89, 0.88 and 0.95 respectively on the test set of experimental platform, and 0.86, 0.90 and 0.96 respectively on the test set of coal mine field. The neural network model has the best fault diagnosis effect. 120 sets of fresh air volume data collected in coal mine field are input into neural network model for prediction, and the fault diagnosis accuracy rate reaches 0.98, which verifies the feasibility and accuracy of the ventilation network fault diagnosis model based on neural network.http://www.gkzdh.cn/article/doi/10.13272/j.issn.1671-251x.2021120093mine ventilationfault diagnosismachine learningsupport vector machinerandom forestneural networkcross-validationgrid search |
| spellingShingle | ZHANG Lang ZHANG Yinghui ZHANG Yibin LI Zuo Research on fault diagnosis method of ventilation network based on machine learning Gong-kuang zidonghua mine ventilation fault diagnosis machine learning support vector machine random forest neural network cross-validation grid search |
| title | Research on fault diagnosis method of ventilation network based on machine learning |
| title_full | Research on fault diagnosis method of ventilation network based on machine learning |
| title_fullStr | Research on fault diagnosis method of ventilation network based on machine learning |
| title_full_unstemmed | Research on fault diagnosis method of ventilation network based on machine learning |
| title_short | Research on fault diagnosis method of ventilation network based on machine learning |
| title_sort | research on fault diagnosis method of ventilation network based on machine learning |
| topic | mine ventilation fault diagnosis machine learning support vector machine random forest neural network cross-validation grid search |
| url | http://www.gkzdh.cn/article/doi/10.13272/j.issn.1671-251x.2021120093 |
| work_keys_str_mv | AT zhanglang researchonfaultdiagnosismethodofventilationnetworkbasedonmachinelearning AT zhangyinghui researchonfaultdiagnosismethodofventilationnetworkbasedonmachinelearning AT zhangyibin researchonfaultdiagnosismethodofventilationnetworkbasedonmachinelearning AT lizuo researchonfaultdiagnosismethodofventilationnetworkbasedonmachinelearning |