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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2405.00311 |
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| _version_ | 1866911983538798592 |
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| author | Lu, Ming Gao, Zhen Zou, Ying Chen, Zuguo Li, Pei |
| author_facet | Lu, Ming Gao, Zhen Zou, Ying Chen, Zuguo Li, Pei |
| contents | With the development of technology, the chemical production process is becoming increasingly complex and large-scale, making fault detection particularly important. However, current detective methods struggle to address the complexities of large-scale production processes. In this paper, we integrate the strengths of deep learning and machine learning technologies, combining the advantages of bidirectional long and short-term memory neural networks, fully connected neural networks, and the extra trees algorithm to propose a novel fault detection model named three-layer deep learning network random trees (TDLN-trees). First, the deep learning component extracts temporal features from industrial data, combining and transforming them into a higher-level data representation. Second, the machine learning component processes and classifies the features extracted in the first step. An experimental analysis based on the Tennessee Eastman process verifies the superiority of the proposed method. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_00311 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Three-layer deep learning network random trees for fault detection in chemical production process Lu, Ming Gao, Zhen Zou, Ying Chen, Zuguo Li, Pei Machine Learning With the development of technology, the chemical production process is becoming increasingly complex and large-scale, making fault detection particularly important. However, current detective methods struggle to address the complexities of large-scale production processes. In this paper, we integrate the strengths of deep learning and machine learning technologies, combining the advantages of bidirectional long and short-term memory neural networks, fully connected neural networks, and the extra trees algorithm to propose a novel fault detection model named three-layer deep learning network random trees (TDLN-trees). First, the deep learning component extracts temporal features from industrial data, combining and transforming them into a higher-level data representation. Second, the machine learning component processes and classifies the features extracted in the first step. An experimental analysis based on the Tennessee Eastman process verifies the superiority of the proposed method. |
| title | Three-layer deep learning network random trees for fault detection in chemical production process |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2405.00311 |