{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,18]],"date-time":"2025-12-18T14:27:37Z","timestamp":1766068057729,"version":"build-2065373602"},"reference-count":44,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2024,8,22]],"date-time":"2024-08-22T00:00:00Z","timestamp":1724284800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Heilongjiang Province Key Research and Development Program","award":["2023ZX01A12","CXRC20231112683"],"award-info":[{"award-number":["2023ZX01A12","CXRC20231112683"]}]},{"name":"Harbin Science and Technology Innovation Talent Funds","award":["2023ZX01A12","CXRC20231112683"],"award-info":[{"award-number":["2023ZX01A12","CXRC20231112683"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Accurate recognition of tool state is important for maximizing tool life. However, the tool sensor data collected in real-life scenarios has unbalanced characteristics. Additionally, although graph neural networks (GNNs) show excellent performance in feature extraction in the spatial dimension of data, it is difficult to extract features in the temporal dimension efficiently. Therefore, we propose a tool state recognition method based on the Pruned Optimized Graph Neural Network-Gated Recurrent Unit (POGNN-GRU) under unbalanced data. Firstly, design the Improved-Majority Weighted Minority Oversampling Technique (IMWMOTE) by introducing an adaptive noise removal strategy and improving the MWMOTE to alleviate the unbalanced problem of data. Subsequently, propose a POG graph data construction method based on a multi-scale multi-metric basis and a Gaussian kernel weight function to solve the problem of one-sided description of graph data under a single metric basis. Then, construct the POGNN-GRU model to deeply mine the spatial and temporal features of the data to better identify the state of the tool. Finally, validation and ablation experiments on the PHM 2010 and HMoTP datasets show that the proposed method outperforms the other models in terms of identification, and the highest accuracy improves by 1.62% and 1.86% compared with the corresponding optimal baseline model.<\/jats:p>","DOI":"10.3390\/s24165433","type":"journal-article","created":{"date-parts":[[2024,8,23]],"date-time":"2024-08-23T12:58:07Z","timestamp":1724417887000},"page":"5433","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Tool State Recognition Based on POGNN-GRU under Unbalanced Data"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9883-4665","authenticated-orcid":false,"given":"Weiming","family":"Tong","sequence":"first","affiliation":[{"name":"Laboratory for Space Environment and Physical Sciences, Harbin Institute of Technology, Harbin 150001, China"}]},{"given":"Jiaqi","family":"Shen","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, China"}]},{"given":"Zhongwei","family":"Li","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, China"}]},{"given":"Xu","family":"Chu","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, China"}]},{"given":"Wenqi","family":"Jiang","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, China"}]},{"given":"Liguo","family":"Tan","sequence":"additional","affiliation":[{"name":"Laboratory for Space Environment and Physical Sciences, Harbin Institute of Technology, Harbin 150001, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,8,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1032","DOI":"10.1016\/j.jmrt.2019.10.031","article-title":"Tool condition monitoring techniques in milling process\u2014A review","volume":"9","author":"Mohanraj","year":"2020","journal-title":"J. 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