{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T16:09:07Z","timestamp":1775146147889,"version":"3.50.1"},"reference-count":32,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2024,3,1]],"date-time":"2024-03-01T00:00:00Z","timestamp":1709251200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Natural Science Foundation of Hubei Province of China","award":["2022CFB935"],"award-info":[{"award-number":["2022CFB935"]}]},{"name":"Natural Science Foundation of Hubei Province of China","award":["2022KJX10"],"award-info":[{"award-number":["2022KJX10"]}]},{"name":"Open Fund of Hubei Key Laboratory for Operation and Control of Cascaded Hydropower Station","award":["2022CFB935"],"award-info":[{"award-number":["2022CFB935"]}]},{"name":"Open Fund of Hubei Key Laboratory for Operation and Control of Cascaded Hydropower Station","award":["2022KJX10"],"award-info":[{"award-number":["2022KJX10"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>As vital equipment in modern industry, the health state of rotating machinery influences the production process and equipment safety. However, rotating machinery generally operates in a normal state most of the time, which results in limited fault data, thus greatly constraining the performance of intelligent fault diagnosis methods. To solve this problem, this paper proposes a novel fault diagnosis method for rotating machinery with limited multisensor fusion samples based on the fused attention-guided Wasserstein generative adversarial network (WGAN). Firstly, the dimensionality of collected multisensor data is reduced to three channels by principal component analysis, and then the one-dimensional data of each channel are converted into a two-dimensional pixel matrix, of which the RGB images are obtained by fusing the three-channel two-dimensional images. Subsequently, the limited RGB samples are augmented to obtain sufficient samples utilizing the fused attention-guided WGAN combined with the gradient penalty (FAWGAN-GP) method. Lastly, the augmented samples are applied to train a residual convolutional neural network for fault diagnosis. The effectiveness of the proposed method is demonstrated by two case studies. When training samples per class are 50, 35, 25, and 15 on the KAT-bearing dataset, the average classification accuracy is 99.9%, 99.65%, 99.6%, and 98.7%, respectively. Meanwhile, the methods of multisensor fusion and the fused attention mechanism have an average improvement of 1.51% and 1.09%, respectively, by ablation experiments on the WT gearbox dataset.<\/jats:p>","DOI":"10.3390\/sym16030285","type":"journal-article","created":{"date-parts":[[2024,3,1]],"date-time":"2024-03-01T06:07:53Z","timestamp":1709273273000},"page":"285","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Rotating Machinery Fault Diagnosis with Limited Multisensor Fusion Samples by Fused Attention-Guided Wasserstein GAN"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4084-1085","authenticated-orcid":false,"given":"Wenlong","family":"Fu","sequence":"first","affiliation":[{"name":"College of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, China"},{"name":"Hubei Provincial Key Laboratory for Operation and Control of Cascaded Hydropower Station, China Three Gorges University, Yichang 443002, China"}]},{"given":"Ke","family":"Yang","sequence":"additional","affiliation":[{"name":"College of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, China"}]},{"given":"Bin","family":"Wen","sequence":"additional","affiliation":[{"name":"College of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, China"}]},{"given":"Yahui","family":"Shan","sequence":"additional","affiliation":[{"name":"Wuhan Second Ship Design and Research Institute, Wuhan 430064, China"}]},{"given":"Shuai","family":"Li","sequence":"additional","affiliation":[{"name":"College of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, China"}]},{"given":"Bo","family":"Zheng","sequence":"additional","affiliation":[{"name":"College of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,3,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"10944","DOI":"10.1109\/TII.2023.3242813","article-title":"Few-shot fault diagnosis method of rotating machinery using novel MCGM based CNN","volume":"19","author":"Yu","year":"2023","journal-title":"IEEE Trans. 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