Research on Deep Learning Method and Optimization of Vibration Characteristics of Rotating Equipment

被引：1
作者：
Zhu, Xiaoxun [1 ,2 ,3 ]
Liu, Baoping [1 ]
Li, Zhentao [1 ]
Lin, Jiawei [1 ]
Gao, Xiaoxia [1 ,2 ,3 ]
机构：
[1] North China Elect Power Univ, Dept Power Engn, Baoding 071003, Peoples R China
[2] North China Elect Power Univ, Hebei Key Lab Low Carbon & High Efficiency Power, Baoding 071003, Peoples R China
[3] North China Elect Power Univ, Baoding Key Lab Low Carbon & High Efficiency Powe, Baoding 071003, Peoples R China
来源：
SENSORS | 2022年 / 22卷 / 10期
关键词：
deep learning; convolutional neural network; vibration; feature learning; condition recognition; CONVOLUTIONAL NEURAL-NETWORK; EMPIRICAL MODE DECOMPOSITION; LOCAL MEAN DECOMPOSITION; BEARING FAULT-DIAGNOSIS; SYSTEM; NOISE;
D O I：
10.3390/s22103693
中图分类号：
O65 [分析化学];
学科分类号：
070302 ; 081704 ;
摘要：
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%.
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页数：19
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