A Pattern Recognition Method for Filter Bags in Bag Dust Collectors Based on Φ-Optical Time-Domain Reflectometry
The use of phase-sensitive optical time-domain reflectometry (Φ-OTDR)-distributed fiber vibration sensors to detect and identify damaged bags in bag dust collectors has the potential to overcome the inadequacy of traditional damaged bag detection methods. In our previous study, we verified the feasi...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-02-01
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| Series: | Photonics |
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| Online Access: | https://www.mdpi.com/2304-6732/11/2/152 |
| _version_ | 1797297213009821696 |
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| author | Xu’an Liu Yuquan Tang Zhirong Zhang Shuang Yang Zhouchang Hu Yuan Xu |
| author_facet | Xu’an Liu Yuquan Tang Zhirong Zhang Shuang Yang Zhouchang Hu Yuan Xu |
| author_sort | Xu’an Liu |
| collection | DOAJ |
| description | The use of phase-sensitive optical time-domain reflectometry (Φ-OTDR)-distributed fiber vibration sensors to detect and identify damaged bags in bag dust collectors has the potential to overcome the inadequacy of traditional damaged bag detection methods. In our previous study, we verified the feasibility of applying this technique in the field of damaged bag detection in bag filters. However, many problems still occur in engineering applications when using this technology to detect and identify damaged filter bags in pulse-jet dust-cleaning bag dust collectors. Further studies are needed to characterize the fiber vibration signals inside different types of rectangular damaged filter bags. A filter bag damage identification and detection method based on empirical mode decomposition (EMD) and a backpropagation (BP) neural network is proposed. The signal feature differences between intact filter bags and damaged filter bags with different rectangular hole sizes and positions are comparatively analyzed, and optimal feature difference parameters are proposed. Support vector machine (SVM) and a BP neural network are used to recognize different types of filter bag signals, and the comparison results show that the BP neural network algorithm is better at recognizing different types of filter bags, obtaining the highest recognition rate of 97.3%. |
| first_indexed | 2024-03-07T22:17:07Z |
| format | Article |
| id | doaj.art-8b116c3b033247efb061746e167a6e05 |
| institution | Directory Open Access Journal |
| issn | 2304-6732 |
| language | English |
| last_indexed | 2024-03-07T22:17:07Z |
| publishDate | 2024-02-01 |
| publisher | MDPI AG |
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| series | Photonics |
| spelling | doaj.art-8b116c3b033247efb061746e167a6e052024-02-23T15:31:41ZengMDPI AGPhotonics2304-67322024-02-0111215210.3390/photonics11020152A Pattern Recognition Method for Filter Bags in Bag Dust Collectors Based on Φ-Optical Time-Domain ReflectometryXu’an Liu0Yuquan Tang1Zhirong Zhang2Shuang Yang3Zhouchang Hu4Yuan Xu5School of Information Engineering, Huangshan University, Huangshan 245041, ChinaAnhui Provincial Key Laboratory of Photonic Devices and Materials, Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Hefei 230031, ChinaAnhui Provincial Key Laboratory of Photonic Devices and Materials, Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Hefei 230031, ChinaAnhui Provincial Key Laboratory of Photonic Devices and Materials, Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Hefei 230031, ChinaAnhui Provincial Key Laboratory of Photonic Devices and Materials, Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Hefei 230031, ChinaSchool of Information Engineering, Huangshan University, Huangshan 245041, ChinaThe use of phase-sensitive optical time-domain reflectometry (Φ-OTDR)-distributed fiber vibration sensors to detect and identify damaged bags in bag dust collectors has the potential to overcome the inadequacy of traditional damaged bag detection methods. In our previous study, we verified the feasibility of applying this technique in the field of damaged bag detection in bag filters. However, many problems still occur in engineering applications when using this technology to detect and identify damaged filter bags in pulse-jet dust-cleaning bag dust collectors. Further studies are needed to characterize the fiber vibration signals inside different types of rectangular damaged filter bags. A filter bag damage identification and detection method based on empirical mode decomposition (EMD) and a backpropagation (BP) neural network is proposed. The signal feature differences between intact filter bags and damaged filter bags with different rectangular hole sizes and positions are comparatively analyzed, and optimal feature difference parameters are proposed. Support vector machine (SVM) and a BP neural network are used to recognize different types of filter bag signals, and the comparison results show that the BP neural network algorithm is better at recognizing different types of filter bags, obtaining the highest recognition rate of 97.3%.https://www.mdpi.com/2304-6732/11/2/152optical fiber sensing technologybag filterempirical mode decomposition methodBP neural network |
| spellingShingle | Xu’an Liu Yuquan Tang Zhirong Zhang Shuang Yang Zhouchang Hu Yuan Xu A Pattern Recognition Method for Filter Bags in Bag Dust Collectors Based on Φ-Optical Time-Domain Reflectometry Photonics optical fiber sensing technology bag filter empirical mode decomposition method BP neural network |
| title | A Pattern Recognition Method for Filter Bags in Bag Dust Collectors Based on Φ-Optical Time-Domain Reflectometry |
| title_full | A Pattern Recognition Method for Filter Bags in Bag Dust Collectors Based on Φ-Optical Time-Domain Reflectometry |
| title_fullStr | A Pattern Recognition Method for Filter Bags in Bag Dust Collectors Based on Φ-Optical Time-Domain Reflectometry |
| title_full_unstemmed | A Pattern Recognition Method for Filter Bags in Bag Dust Collectors Based on Φ-Optical Time-Domain Reflectometry |
| title_short | A Pattern Recognition Method for Filter Bags in Bag Dust Collectors Based on Φ-Optical Time-Domain Reflectometry |
| title_sort | pattern recognition method for filter bags in bag dust collectors based on φ optical time domain reflectometry |
| topic | optical fiber sensing technology bag filter empirical mode decomposition method BP neural network |
| url | https://www.mdpi.com/2304-6732/11/2/152 |
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