{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T17:24:36Z","timestamp":1787851476544,"version":"build-2784847793"},"reference-count":33,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T00:00:00Z","timestamp":1787788800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Science and Technology Program Project of the Zhejiang Provincial Market Supervision Administration","award":["ZC2026055"],"award-info":[{"award-number":["ZC2026055"]}]},{"name":"Hubei Science and Technology Innovation Talent Program","award":["2024DJC043"],"award-info":[{"award-number":["2024DJC043"]}]},{"name":"Science and Technology Research Project of the Education Department of Hubei Province","award":["D20241102"],"award-info":[{"award-number":["D20241102"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Cross-condition fault identification for crane slewing bearings is difficult because low rotational speed, heavy loading, and operating-condition variation jointly weaken fault-induced impulses and alter the distribution of monitoring data. In addition, vibration and acoustic emission signals describe different aspects of bearing degradation, making fixed multi-sensor fusion insufficient when sensor sensitivity changes across fault states. This study proposes a Lightweight Domain-Adaptive Graph Convolutional Network (LDAGCN) for cross-condition diagnosis. The model uses six vibration channels and one acoustic emission channel as synchronized heterogeneous inputs. Two compact one-dimensional encoders first learn modality-specific temporal representations, after which a feature-wise gate determines the relative contribution of each sensing modality. The fused embeddings in every mini-batch are regarded as graph nodes, and a sparse sample graph is reconstructed from Top-k cosine similarities. Multi-receptive-field graph convolution then aggregates one-hop and higher-order neighborhood information, while a residual connection limits excessive modification of the original fused features. To reduce the discrepancy between operating conditions, the training objective combines source-domain classification, adversarial domain discrimination, maximum mean discrepancy, and supervision from a small labeled target-domain adaptation subset. Experiments were carried out on a dedicated crane slewing-bearing test rig containing normal, inner-race fault, outer-race fault, and B1 localized-fault states. On the target-condition test set, LDAGCN achieved an accuracy of 97.22%, a Macro-F1 score of 97.21%, a Macro-Precision of 97.37%, and a Macro-Recall of 97.22%. The proposed model also outperformed 1D-CNN, ResNet1D, CNN-LSTM, DANN, MMD-DAN, and DeepCORAL under the same data partition. The confusion matrix and t-SNE projection indicate that the learned representation reduces the source\u2013target distribution gap while retaining fault-class separation. The ablation results further indicate that the acoustic emission information, gated fusion, graph-based association learning, and domain adaptation complement each other in terms of the final diagnostic performance, while maintaining a lightweight architecture that is suitable for practical monitoring.<\/jats:p>","DOI":"10.3390\/s26175433","type":"journal-article","created":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T16:47:13Z","timestamp":1787849233000},"page":"5433","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Cross-Condition Fault Diagnosis of Crane Slewing Bearings Based on a Lightweight Domain-Adaptive Graph Convolutional Network"],"prefix":"10.3390","volume":"26","author":[{"given":"Wuben","family":"Yang","sequence":"first","affiliation":[{"name":"Wenzhou Special Equipment Inspection & Science Research Institute, Wenzhou 325000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiangyin","family":"Wu","sequence":"additional","affiliation":[{"name":"Wenzhou Special Equipment Inspection & Science Research Institute, Wenzhou 325000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anding","family":"Wu","sequence":"additional","affiliation":[{"name":"Wenzhou Special Equipment Inspection & Science Research Institute, Wenzhou 325000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lipeng","family":"Su","sequence":"additional","affiliation":[{"name":"Wenzhou Special Equipment Inspection & Science Research Institute, Wenzhou 325000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yifan","family":"Lou","sequence":"additional","affiliation":[{"name":"Wenzhou Special Equipment Inspection & Science Research Institute, Wenzhou 325000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiafu","family":"Wu","sequence":"additional","affiliation":[{"name":"Wenzhou Special Equipment Inspection & Science Research Institute, Wenzhou 325000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhaoyi","family":"Wang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Metallurgical Equipment and Control Technology, Ministry of Education, Wuhan University of Science and Technology, Wuhan 430080, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0471-0280","authenticated-orcid":false,"given":"Cancan","family":"Yi","sequence":"additional","affiliation":[{"name":"Key Laboratory of Metallurgical Equipment and Control Technology, Ministry of Education, Wuhan University of Science and Technology, Wuhan 430080, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,8,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"e17584","DOI":"10.1016\/j.heliyon.2023.e17584","article-title":"Machine learning for fault analysis in rotating machinery: A comprehensive review","volume":"9","author":"Das","year":"2023","journal-title":"Heliyon"},{"key":"ref_2","first-page":"1","article-title":"Deep transfer learning for bearing fault diagnosis: A systematic review since 2016","volume":"72","author":"Chen","year":"2023","journal-title":"IEEE Trans. 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