@inproceedings{8e13682876794049833ef99f6d74d4c9,
title = "Rotating Machinery Fault Diagnosis Based on Spatial-Temporal GCN",
abstract = "Multi-sensor can provide more comprehensive and accurate information for mechanical fault diagnosis. Aiming at the weak ability of traditional artificial intelligence (AI) models to model multi-sensor signals, a method of fault diagnosis is proposed based on Spatial-Temporal Graph Convolution Network (ST-GCN) in this paper. The multi-sensor data is firstly modeled as a multivariate temporal graph. The relationship between different sensors expressed as graph topology is established adaptively using node features. Then, the spatial and temporal features are learnt simultaneously by a designed ST-GCN model. Finally, the fault type is inferred by a softmax classifier based on the entire graph representation. The diagnosis model is testified on bearing and gearbox and the results indicate effectiveness of extracting the information of multi-sensor and enhancing diagnosis performance.",
keywords = "fault diagnosis, graph generation, multi-sensor information fusion, spatial-temporal GCN (ST-GCN)",
author = "Chenyang Li and Lingfei Mo and Ruqiang Yan",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2nd International Conference on Sensing, Measurement and Data Analytics in the Era of Artificial Intelligence, ICSMD 2021 ; Conference date: 21-10-2021 Through 23-10-2021",
year = "2021",
doi = "10.1109/ICSMD53520.2021.9670851",
language = "英语",
series = "ICSMD 2021 - 2nd International Conference on Sensing, Measurement and Data Analytics in the Era of Artificial Intelligence",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "ICSMD 2021 - 2nd International Conference on Sensing, Measurement and Data Analytics in the Era of Artificial Intelligence",
}