计算机科学
断层(地质)
峰度
图形
可见性图
人工智能
模式识别(心理学)
能见度
算法
一般化
信号(编程语言)
特征提取
数学
理论计算机科学
统计
数学分析
物理
几何学
正多边形
地震学
光学
程序设计语言
地质学
作者
Xiaoyun Gong,Feng Kunpeng,Zeheng Zhi,Yiyuan Gao,Wenliao Du
标识
DOI:10.1088/1361-6501/aca706
摘要
Abstract Multiple faults often occur in the operation of rotating machinery transmission systems. The fault signals of multiple bearings interfere with each other, which makes feature extraction and diagnosis of complex compound fault signals difficult. Because the graph convolution networks (GCN) can effectively map the structural information from complex data and its model has a certain generalization ability, this paper proposes a multiple fault diagnosis method for rolling bearings employing complete ensemble empirical mode decomposition (CEEMD) and a GCN (CEEMD-GCN) based on a horizontal visibility graph (HVG). Firstly, in order to highlight the effective feature information in the multiple fault signal and reduce noise interference, multiple indicators of correlation and kurtosis are used to reconstruct the decomposed signals through CEEMD; secondly, the reconstructed signals are constructed as an HVG, and the HVG maps the time series signal to the graphic structure data, reflecting the local geometric characteristics of the vibration signal through the horizontal visibility relationship; finally, taking the signal samples obtained by the HVG algorithm as the input data of the model, the GCN model is trained to realize the diagnosis of multiple faults. The experimental results show that the presented methodology is superior to other methods and exhibits generalization ability for multiple fault diagnosis.
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