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

Autor:	Xiaoxun Zhu, Baoping Liu, Zhentao Li, Jiawei Lin, Xiaoxia Gao
Jazyk:	angličtina
Rok vydání:	2022
Předmět:	deep learning convolutional neural network vibration feature learning condition recognition Chemical technology TP1-1185
Zdroj:	Sensors, Vol 22, Iss 10, p 3693 (2022)
Druh dokumentu:	article
ISSN:	1424-8220
DOI:	10.3390/s22103693
Popis:	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%.
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/553dddb9fefa45258b3973665b820ef1 Zobrazit plný text záznamu View record in DOAJ Plný text ve formátu PDF Plný text ve formátu HTML
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