Multi-source fault data fusion diagnosis method based on hyper-feature space graph collaborative embedding

被引:5
作者
Dong, Xiaoxin [1 ]
Ding, Hua [1 ]
Gao, Dawei [2 ]
Zheng, Guangyu [1 ]
Wang, Jiaxuan [1 ]
Lang, Qifa [1 ]
机构
[1] Taiyuan Univ Technol, Coll Mech Engn, Taiyuan 030024, Peoples R China
[2] Northwestern Polytech Univ, Sch Marine Sci & Technol, Xian 710072, Peoples R China
基金
中国国家自然科学基金;
关键词
Fault diagnosis; Multi-source data fusion; Hyper-feature space; Graph embedding;
D O I
10.1016/j.aei.2024.103092
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Rotating machinery fault diagnosis based on multi-source sensor monitoring presents high dimensionality, high sampling frequency, and nonlinearity problems, making it challenging to accurately and timely determine the true health status of the equipment. Moreover, existing methods, such as deep learning models, face issues like a large number of training parameters and limited interpretability, which hinder their application in engineering practice, especially in scenarios that require fast diagnostic performance and ease of deployment. To address this problem, a novel fault diagnosis framework based on hyper-feature space graph collaborative embedding (HFSGCE) is proposed in this paper to improve the health status identification efficiency. Firstly, the algorithm realizes the preservation of the near-neighbor structure of the data by establishing a hyper-feature space embedding graph model corresponding to different types of sensor data. Secondly, a fused hyper-Laplacian scatter matrix is established based on the graph structure model to achieve feature-level fusion of multisource data. Finally, the dimensionality-reduced multi-source monitoring data is fed into the classifier for pattern recognition. The algorithm was experimentally validated using two types of bearing fault simulation data from Paderborn University and our laboratory. The results demonstrate that the algorithm effectively eliminates redundant information from large volumes of low-value-density monitoring data, providing a new insight for rotating machinery fault diagnosis in the context of big data.
引用
收藏
页数:14
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