{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:08:28Z","timestamp":1760144908439,"version":"build-2065373602"},"reference-count":25,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2024,5,31]],"date-time":"2024-05-31T00:00:00Z","timestamp":1717113600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Key R&amp;D Plan of Hebei Province of China","award":["21314303D","2023YFB3407703","F2021202022","21JCZDJC00850"],"award-info":[{"award-number":["21314303D","2023YFB3407703","F2021202022","21JCZDJC00850"]}]},{"name":"National Key Research and Development Plan","award":["21314303D","2023YFB3407703","F2021202022","21JCZDJC00850"],"award-info":[{"award-number":["21314303D","2023YFB3407703","F2021202022","21JCZDJC00850"]}]},{"name":"Science and Technology Plan Project of Tianjin of China","award":["21314303D","2023YFB3407703","F2021202022","21JCZDJC00850"],"award-info":[{"award-number":["21314303D","2023YFB3407703","F2021202022","21JCZDJC00850"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Although traditional fault diagnosis methods are proficient in extracting signal features, their diagnostic interpretability remains challenging. Consequently, this article proposes a conditionally interpretable generative adversarial network (C-InGAN) model for the interpretable feature fault diagnosis of bearings. Initially, the vibration signal is denoised and transformed into a frequency domain signal. The model consists of the two primary networks, each employing a convolutional layer and an attention module, generator (G) and discriminator (D), respectively. Latent code was incorporated into G to constrain the generated samples, and a discriminant layer was added to D to identify the interpretable features. During training, the two networks were alternately trained, and the feature mapping relationship of the pre-normalized encoder was learned by maximizing the information from the latent code and the discriminative result. The encoding that represents specific features in the vibration signal was extracted from the random noise. Ultimately, after completing adversarial learning, G is capable of generating a simulated signal of the specified feature, and D can assess the interpretable features in the vibration signal. The effectiveness of the model is validated through three typical experimental cases. This method effectively separates the discrete and continuous feature coding in the signal.<\/jats:p>","DOI":"10.3390\/e26060480","type":"journal-article","created":{"date-parts":[[2024,5,31]],"date-time":"2024-05-31T06:35:32Z","timestamp":1717137332000},"page":"480","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Application of C-InGAN Model in Interpretable Feature of Bearing Fault Diagnosis"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-6207-5154","authenticated-orcid":false,"given":"Wanyi","family":"Yang","sequence":"first","affiliation":[{"name":"School of Artificial Intelligence, Hebei University of Technology, Tianjin 300130, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3751-7815","authenticated-orcid":false,"given":"Tao","family":"Liang","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Hebei University of Technology, Tianjin 300130, China"}]},{"given":"Jianxin","family":"Tan","sequence":"additional","affiliation":[{"name":"Hebei Jiantou New Energy Co., Ltd., Shijiazhuang 050018, China"}]},{"given":"Yanwei","family":"Jing","sequence":"additional","affiliation":[{"name":"Hebei Jiantou New Energy Co., Ltd., Shijiazhuang 050018, China"}]},{"given":"Liangnian","family":"Lv","sequence":"additional","affiliation":[{"name":"Goldwind Science & Technology Co., Ltd., Wulumuqi 830063, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,5,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"012003","DOI":"10.1088\/1742-6596\/2125\/1\/012003","article-title":"Rolling bearing fault diagnosis based on MEEMD sample entropy and SSA-SVM","volume":"2125","author":"Li","year":"2021","journal-title":"J. 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