Research on Deep Learning Method and Optimization of Vibration Characteristics of Rotating Equipment
CNN extracts the signal characteristics layer by layer through the local perception of convolution kernel, but the rotation speed and sampling frequency of the vibration signal of rotating equipment are not the same. Extracting different signal features with a fixed convolution kernel will affect th...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-05-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/22/10/3693 |
| _version_ | 1797495739891318784 |
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| author | Xiaoxun Zhu Baoping Liu Zhentao Li Jiawei Lin Xiaoxia Gao |
| author_facet | Xiaoxun Zhu Baoping Liu Zhentao Li Jiawei Lin Xiaoxia Gao |
| author_sort | Xiaoxun Zhu |
| collection | DOAJ |
| description | CNN extracts the signal characteristics layer by layer through the local perception of convolution kernel, but the rotation speed and sampling frequency of the vibration signal of rotating equipment are not the same. Extracting different signal features with a fixed convolution kernel will affect the local feature perception and ultimately affect the learning effect and recognition accuracy. In order to solve this problem, the matching between the size of convolution kernel and the signal (rotation speed, sampling frequency) was optimized with the matching relation obtained. Through the study of this paper, the ability of extracting vibration features of CNN was improved, and the accuracy of vibration state recognition was finally improved to 98%. |
| first_indexed | 2024-03-10T01:53:56Z |
| format | Article |
| id | doaj.art-553dddb9fefa45258b3973665b820ef1 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T01:53:56Z |
| publishDate | 2022-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-553dddb9fefa45258b3973665b820ef12023-11-23T12:59:33ZengMDPI AGSensors1424-82202022-05-012210369310.3390/s22103693Research on Deep Learning Method and Optimization of Vibration Characteristics of Rotating EquipmentXiaoxun Zhu0Baoping Liu1Zhentao Li2Jiawei Lin3Xiaoxia Gao4Department of Power Engineering, North China Electric Power University, Baoding 071003, ChinaDepartment of Power Engineering, North China Electric Power University, Baoding 071003, ChinaDepartment of Power Engineering, North China Electric Power University, Baoding 071003, ChinaDepartment of Power Engineering, North China Electric Power University, Baoding 071003, ChinaDepartment of Power Engineering, North China Electric Power University, Baoding 071003, ChinaCNN extracts the signal characteristics layer by layer through the local perception of convolution kernel, but the rotation speed and sampling frequency of the vibration signal of rotating equipment are not the same. Extracting different signal features with a fixed convolution kernel will affect the local feature perception and ultimately affect the learning effect and recognition accuracy. In order to solve this problem, the matching between the size of convolution kernel and the signal (rotation speed, sampling frequency) was optimized with the matching relation obtained. Through the study of this paper, the ability of extracting vibration features of CNN was improved, and the accuracy of vibration state recognition was finally improved to 98%.https://www.mdpi.com/1424-8220/22/10/3693deep learningconvolutional neural networkvibrationfeature learningcondition recognition |
| spellingShingle | Xiaoxun Zhu Baoping Liu Zhentao Li Jiawei Lin Xiaoxia Gao Research on Deep Learning Method and Optimization of Vibration Characteristics of Rotating Equipment Sensors deep learning convolutional neural network vibration feature learning condition recognition |
| title | Research on Deep Learning Method and Optimization of Vibration Characteristics of Rotating Equipment |
| title_full | Research on Deep Learning Method and Optimization of Vibration Characteristics of Rotating Equipment |
| title_fullStr | Research on Deep Learning Method and Optimization of Vibration Characteristics of Rotating Equipment |
| title_full_unstemmed | Research on Deep Learning Method and Optimization of Vibration Characteristics of Rotating Equipment |
| title_short | Research on Deep Learning Method and Optimization of Vibration Characteristics of Rotating Equipment |
| title_sort | research on deep learning method and optimization of vibration characteristics of rotating equipment |
| topic | deep learning convolutional neural network vibration feature learning condition recognition |
| url | https://www.mdpi.com/1424-8220/22/10/3693 |
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