可见性图
计算机科学
图形
特征提取
模式识别(心理学)
人工智能
特征学习
算法
数据挖掘
断层(地质)
机器学习
理论计算机科学
数学
地质学
正多边形
地震学
几何学
作者
Chenyang Li,Lingfei Mo,Ruqiang Yan
出处
期刊:IEEE Transactions on Instrumentation and Measurement
[Institute of Electrical and Electronics Engineers]
日期:2021-01-01
卷期号:70: 1-11
被引量:31
标识
DOI:10.1109/tim.2021.3087834
摘要
In recent years, emerging intelligent algorithms have achieved great success in the domain of fault diagnosis due to effective feature extraction and powerful learning ability. However, the current models can only handle the data in Euclidean space, ignoring latent structure relationships of the signal, which can provide additional helpful information to distinguish diverse fault patterns. To address this issue, a graph convolution network (GCN) incorporating the weighted horizontal visibility graph (WHVG) is proposed for bearing faults diagnosis. The WHVG is utilized to transform time series to graph data from a geometric perspective. Edges are weighted by the difference between the sampling indexes to weaken the influence of remote nodes that are considered as noise. Furthermore, the graph isomorphism network (GIN) is improved as GIN+ to learn the graph representation and perform fault classification. Finally, the validity of WHVG and GIN+ is testified by three real-world bearing datasets. Meanwhile, the GIN+ model is compared with other machine learning models, multilayer perceptron (MLP), long short-term memory (LSTM), and two GCN models. The experimental results show that GIN+ boosts the performance and the internal structure relationships of the data contribute to the bearing faults diagnosis.
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