External Breaking Vibration Identification Method of Transmission Line Tower Based on Solar-Powered RFID Sensor and CNN
This paper proposes an external breaking vibration identification method of transmission line tower based on a radio frequency identification (RFID) sensor and deep learning. The RFID sensor is designed to obtain the vibration signal of the transmission line tower. In order to achieve long-time moni...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-03-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/9/3/519 |
| _version_ | 1798040157770743808 |
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| author | Fangming Deng Kaiyun Wen Zhongxin Xie Huafeng Liu Jin Tong |
| author_facet | Fangming Deng Kaiyun Wen Zhongxin Xie Huafeng Liu Jin Tong |
| author_sort | Fangming Deng |
| collection | DOAJ |
| description | This paper proposes an external breaking vibration identification method of transmission line tower based on a radio frequency identification (RFID) sensor and deep learning. The RFID sensor is designed to obtain the vibration signal of the transmission line tower. In order to achieve long-time monitoring and longer working distance, the proposed RFID sensor tag employs a photovoltaic cell combined with a super capacitor as the power management module. convolution neural network (CNN) is adopted to extract the characteristics of vibration signals and relevance vector machine (RVM) is then employed to achieve vibration pattern identification. Furthermore, the Softmax classifier and gradient descent method are used to adjust the weights and thresholds of CNN, so as to obtain a high-precision identification structure. The experiment results show that the minimum sensitivity of the proposed solar-powered RFID sensor tag is −29 dBm and the discharge duration of the super capacitor is 63.35 h when the query frequencies are 5/min. The optimum batch size of CNN is 5, and the optimum number of convolution cores in the first layer and the second layer are 2 and 4, respectively. The maximum number of iterations is 10 times. The vibration identification accuracy of the proposed method is over 99% under three different conditions. |
| first_indexed | 2024-04-11T22:03:36Z |
| format | Article |
| id | doaj.art-d20598e51ae44e6ab94078fc4c5d2f9d |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-04-11T22:03:36Z |
| publishDate | 2020-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-d20598e51ae44e6ab94078fc4c5d2f9d2022-12-22T04:00:50ZengMDPI AGElectronics2079-92922020-03-019351910.3390/electronics9030519electronics9030519External Breaking Vibration Identification Method of Transmission Line Tower Based on Solar-Powered RFID Sensor and CNNFangming Deng0Kaiyun Wen1Zhongxin Xie2Huafeng Liu3Jin Tong4School of Electrical and Automation Engineering, East China Jiaotong University, Nanchang 330013, ChinaSchool of Electrical and Automation Engineering, East China Jiaotong University, Nanchang 330013, ChinaSchool of Electrical and Automation Engineering, East China Jiaotong University, Nanchang 330013, ChinaSchool of Electrical and Automation Engineering, East China Jiaotong University, Nanchang 330013, ChinaSchool of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230009, ChinaThis paper proposes an external breaking vibration identification method of transmission line tower based on a radio frequency identification (RFID) sensor and deep learning. The RFID sensor is designed to obtain the vibration signal of the transmission line tower. In order to achieve long-time monitoring and longer working distance, the proposed RFID sensor tag employs a photovoltaic cell combined with a super capacitor as the power management module. convolution neural network (CNN) is adopted to extract the characteristics of vibration signals and relevance vector machine (RVM) is then employed to achieve vibration pattern identification. Furthermore, the Softmax classifier and gradient descent method are used to adjust the weights and thresholds of CNN, so as to obtain a high-precision identification structure. The experiment results show that the minimum sensitivity of the proposed solar-powered RFID sensor tag is −29 dBm and the discharge duration of the super capacitor is 63.35 h when the query frequencies are 5/min. The optimum batch size of CNN is 5, and the optimum number of convolution cores in the first layer and the second layer are 2 and 4, respectively. The maximum number of iterations is 10 times. The vibration identification accuracy of the proposed method is over 99% under three different conditions.https://www.mdpi.com/2079-9292/9/3/519tower vibration identificationrfid sensorconvolutional neural networkrelevance vector machine |
| spellingShingle | Fangming Deng Kaiyun Wen Zhongxin Xie Huafeng Liu Jin Tong External Breaking Vibration Identification Method of Transmission Line Tower Based on Solar-Powered RFID Sensor and CNN Electronics tower vibration identification rfid sensor convolutional neural network relevance vector machine |
| title | External Breaking Vibration Identification Method of Transmission Line Tower Based on Solar-Powered RFID Sensor and CNN |
| title_full | External Breaking Vibration Identification Method of Transmission Line Tower Based on Solar-Powered RFID Sensor and CNN |
| title_fullStr | External Breaking Vibration Identification Method of Transmission Line Tower Based on Solar-Powered RFID Sensor and CNN |
| title_full_unstemmed | External Breaking Vibration Identification Method of Transmission Line Tower Based on Solar-Powered RFID Sensor and CNN |
| title_short | External Breaking Vibration Identification Method of Transmission Line Tower Based on Solar-Powered RFID Sensor and CNN |
| title_sort | external breaking vibration identification method of transmission line tower based on solar powered rfid sensor and cnn |
| topic | tower vibration identification rfid sensor convolutional neural network relevance vector machine |
| url | https://www.mdpi.com/2079-9292/9/3/519 |
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