{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T01:24:11Z","timestamp":1760059451611,"version":"build-2065373602"},"reference-count":40,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2025,6,14]],"date-time":"2025-06-14T00:00:00Z","timestamp":1749859200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["52205016"],"award-info":[{"award-number":["52205016"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Traditional fault feature generation models often face issues of uncontrollability, singularity, and slow convergence, limiting diagnostic accuracy. To address these challenges, this paper proposes a symmetry-aware approach that combines a conditional variational autoencoder (CVAE) and an auxiliary classifier generative adversarial network (ACGAN) for fault feature generation, leveraging symmetry characteristics inherent in fault data distributions and adversarial learning. Specifically, symmetrical Gaussian distributions in the CVAE enable robust extraction of latent fault features conditioned on fault classes, which are then input to the symmetrical adversarial framework of the ACGAN to guide the generator and discriminator toward a symmetrical Nash equilibrium. The original and generated features are jointly utilized in a convolutional neural network (CNN) for fault classification. Experimental results on the CWRU dataset show that the proposed CVAE-ACGAN achieves an average accuracy of 99.21%, precision of 97.81%, and recall of 98.24%, surpassing the baseline CNN. Similar improvements are achieved on the PADERBORN dataset. Furthermore, the model achieves significantly lower root mean square error (RMSE) and mean absolute error (MAE) than competing methods, confirming high consistency between the generated and real features and supporting its superior generalization and reliability. Visualization via confusion matrices and t-SNE further demonstrates clear boundaries between fault categories. These results affirm the value of incorporating symmetry principles into feature generation for mechanical fault diagnosis.<\/jats:p>","DOI":"10.3390\/sym17060947","type":"journal-article","created":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T04:20:23Z","timestamp":1750134023000},"page":"947","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Symmetry-Aware CVAE-ACGAN-Based Feature Generation Model and Its Application in Fault Diagnosis"],"prefix":"10.3390","volume":"17","author":[{"given":"Long","family":"Ma","sequence":"first","affiliation":[{"name":"Emergency Science Research Institute, CCTEG Chinese Institute of Coal Science, Beijing 100013, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yingjie","family":"Liu","sequence":"additional","affiliation":[{"name":"Emergency Science Research Institute, CCTEG Chinese Institute of Coal Science, Beijing 100013, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yue","family":"Zhang","sequence":"additional","affiliation":[{"name":"Emergency Science Research Institute, CCTEG Chinese Institute of Coal Science, Beijing 100013, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ming","family":"Chu","sequence":"additional","affiliation":[{"name":"School of Intelligent Engineering and Automation, Beijing University of Posts and Telecommunications, Beijing 100876, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,6,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1537","DOI":"10.1109\/TEC.2015.2431722","article-title":"Oil-whirl fault modeling, simulation, and detection in sleeve bearings of squirrel cage induction motors","volume":"30","author":"Ojaghi","year":"2015","journal-title":"IEEE Trans. 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