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subject to high speed, high-temperature rise, large load, and other continuous complex fluctuation conditions, which makes their health assessment tasks more difficult. To solve this problem, an intelligent health assessment method based on a new Deep Transfer Graph Convolutional Network (DTGCN) is proposed for aviation bearings under large speed fluctuation conditions. First, a new DTGCN algorithm is designed, which mainly uses the domain adaptation mechanism to enhance the performance of Graph Convolutional Network (GCN) and the generalization performance of transfer properties. Specifically, order spectrum analysis is employed to resample the vibration signals of aviation bearings and transform them into order spectral signals. Then, the trained 1dGCN is used as the feature extractor, and the designed Dynamic Multiple Kernel Maximum Mean Discrepancy (DMKMMD) is calculated to match the difference in edge distribution. Finally, the aligned features are fed into the softmax classifier for intelligent health assessment. The effectiveness of the proposed diagnostic algorithm and method are validated by using aviation bearing fault data set under large speed fluctuation conditions.<\/jats:p>","DOI":"10.3390\/s23094379","type":"journal-article","created":{"date-parts":[[2023,4,28]],"date-time":"2023-04-28T09:54:53Z","timestamp":1682675693000},"page":"4379","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Intelligent Health Assessment of Aviation Bearing Based on Deep Transfer Graph Convolutional Networks under Large Speed Fluctuations"],"prefix":"10.3390","volume":"23","author":[{"given":"Xiaoli","family":"Zhao","sequence":"first","affiliation":[{"name":"School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China"},{"name":"State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou 310027, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xingjun","family":"Zhu","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianyong","family":"Yao","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenxiang","family":"Deng","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yudong","family":"Cao","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Southeast University, Nanjing 211189, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peng","family":"Ding","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Southeast University, Nanjing 211189, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Minping","family":"Jia","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Southeast University, Nanjing 211189, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haidong","family":"Shao","sequence":"additional","affiliation":[{"name":"College of Mechanical and Vehicle Engineering, Hunan University, Changsha 410082, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,4,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2587","DOI":"10.1109\/TIE.2020.2972458","article-title":"Deep Residual Networks with Adaptively Parametric Rectifier Linear Units for Fault Diagnosis","volume":"68","author":"Zhao","year":"2021","journal-title":"IEEE Trans. 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