计算机科学
图形
模式识别(心理学)
理论计算机科学
数据挖掘
人工智能
作者
Chaoying Yang,Jie Liu,Kaibo Zhou,Xingxing Jiang,Xiangyu Zeng
出处
期刊:Measurement
[Elsevier]
日期:2022-02-01
卷期号:190: 110720-110720
被引量:27
标识
DOI:10.1016/j.measurement.2022.110720
摘要
Different from most of deep learning-based rotating machinery diagnosis methods, graph convolutional network-based method can effectively mine relationship between nodes in the graph by feature aggregation and transformation. But the performance is limited to graph quality. Currently, edge connections of the graph are often established by calculating the feature similarity of single sensor data. To further improve graph quality, an improved multi-channel graph convolutional network (iMCGCN) for rotating machinery diagnosis is proposed in this paper. Multi-sensor data are used to construct graphs, where corresponding undirected k-nearest neighbor graphs (UK-NNGs) are constructed for each sensor data. A parallel graph data processing framework is designed to extract graph features from the constructed UK-NNGs. Then, an iMCGCN is constructed to learn graph features and achieve multi-channel feature fusion. Case studies are implemented to verify effectiveness of the proposed iMCGCN in learning health features for fault diagnosis.
科研通智能强力驱动
Strongly Powered by AbleSci AI