Deep learning approach for vibration signals applications

Han Yun Chen, Ching-Hung Lee*

*此作品的通信作者

研究成果: Article同行評審

10 引文 斯高帕斯(Scopus)

摘要

This study discusses convolutional neural networks (CNNs) for vibration signals analysis, including applications in machining surface roughness estimation, bearing faults diagnosis, and tool wear detection. The one-dimensional CNNs (1DCNN) and two-dimensional CNNs (2DCNN) are applied for regression and classification applications using different types of inputs, e.g., raw signals, and time-frequency spectra images by short time Fourier transform. In the application of regression and the estimation of machining surface roughness, the 1DCNN is utilized and the corresponding CNN structure (hyper parameters) optimization is proposed by using uniform experimental design (UED), neural network, multiple regression, and particle swarm optimization. It demonstrates the effectiveness of the proposed approach to obtain a structure with better performance. In applications of classification, bearing faults and tool wear classification are carried out by vibration signals analysis and CNN. Finally, the experimental results are shown to demonstrate the effectiveness and performance of our approach.

原文American English
文章編號3929
期刊Sensors
21
發行號11
DOIs
出版狀態Published - 7 6月 2021

指紋

深入研究「Deep learning approach for vibration signals applications」主題。共同形成了獨特的指紋。

引用此