{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,5]],"date-time":"2026-04-05T05:26:54Z","timestamp":1775366814731,"version":"3.50.1"},"reference-count":44,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2023,12,24]],"date-time":"2023-12-24T00:00:00Z","timestamp":1703376000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"\u201cPioneer\u201d and \u201cLeading Goose\u201d R&amp;D Program of Zhejiang","award":["2023C01024"],"award-info":[{"award-number":["2023C01024"]}]},{"name":"\u201cPioneer\u201d and \u201cLeading Goose\u201d R&amp;D Program of Zhejiang","award":["61973102"],"award-info":[{"award-number":["61973102"]}]},{"name":"\u201cPioneer\u201d and \u201cLeading Goose\u201d R&amp;D Program of Zhejiang","award":["LGG22F030005"],"award-info":[{"award-number":["LGG22F030005"]}]},{"name":"\u201cPioneer\u201d and \u201cLeading Goose\u201d R&amp;D Program of Zhejiang","award":["U22A2047"],"award-info":[{"award-number":["U22A2047"]}]},{"name":"National Natural Science Foundation of China","award":["2023C01024"],"award-info":[{"award-number":["2023C01024"]}]},{"name":"National Natural Science Foundation of China","award":["61973102"],"award-info":[{"award-number":["61973102"]}]},{"name":"National Natural Science Foundation of China","award":["LGG22F030005"],"award-info":[{"award-number":["LGG22F030005"]}]},{"name":"National Natural Science Foundation of China","award":["U22A2047"],"award-info":[{"award-number":["U22A2047"]}]},{"name":"Basic Public Welfare Research Project of Zhejiang Province","award":["2023C01024"],"award-info":[{"award-number":["2023C01024"]}]},{"name":"Basic Public Welfare Research Project of Zhejiang Province","award":["61973102"],"award-info":[{"award-number":["61973102"]}]},{"name":"Basic Public Welfare Research Project of Zhejiang Province","award":["LGG22F030005"],"award-info":[{"award-number":["LGG22F030005"]}]},{"name":"Basic Public Welfare Research Project of Zhejiang Province","award":["U22A2047"],"award-info":[{"award-number":["U22A2047"]}]},{"name":"National Natural Science Foundation of China","award":["2023C01024"],"award-info":[{"award-number":["2023C01024"]}]},{"name":"National Natural Science Foundation of China","award":["61973102"],"award-info":[{"award-number":["61973102"]}]},{"name":"National Natural Science Foundation of China","award":["LGG22F030005"],"award-info":[{"award-number":["LGG22F030005"]}]},{"name":"National Natural Science Foundation of China","award":["U22A2047"],"award-info":[{"award-number":["U22A2047"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>As energy conversion systems continue to grow in complexity, pneumatic control valves may exhibit unexpected anomalies or trigger system shutdowns, leading to a decrease in system reliability. Consequently, the analysis of time-domain signals and the utilization of artificial intelligence, including deep learning methods, have emerged as pivotal approaches for addressing these challenges. Although deep learning is widely used for pneumatic valve fault diagnosis, the success of most deep learning methods depends on a large amount of labeled training data, which is often difficult to obtain. To address this problem, a novel fault diagnosis method based on the attention-weighted relation network (AWRN) is proposed to achieve fault detection and classification with small sample data. In the proposed method, fault diagnosis is performed through the relation network in few-shot learning, and in order to enhance the representativeness of feature extraction, the attention-weighted mechanism is introduced into the relation network. Finally, in order to verify the effectiveness of the method, a DA valve fault dataset is constructed, and experimental validation is performed on this dataset and another benchmark PU rolling bearing fault dataset. The results show that the accuracy of the network on DA is 99.15%, and the average accuracy on PU is 98.37%. Compared with the state-of-the-art diagnosis methods, the proposed method achieves higher accuracy while significantly reducing the amount of training data.<\/jats:p>","DOI":"10.3390\/e26010022","type":"journal-article","created":{"date-parts":[[2023,12,24]],"date-time":"2023-12-24T20:44:15Z","timestamp":1703450655000},"page":"22","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Few-Shot Fault Diagnosis Based on an Attention-Weighted Relation Network"],"prefix":"10.3390","volume":"26","author":[{"given":"Li","family":"Xue","sequence":"first","affiliation":[{"name":"HDU-ITMO Joint Institute, Hangzhou Dianzi University, Hangzhou 310018, China"}]},{"given":"Aipeng","family":"Jiang","sequence":"additional","affiliation":[{"name":"School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3377-1904","authenticated-orcid":false,"given":"Xiaoqing","family":"Zheng","sequence":"additional","affiliation":[{"name":"School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China"}]},{"given":"Yanying","family":"Qi","sequence":"additional","affiliation":[{"name":"School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China"}]},{"given":"Lingyu","family":"He","sequence":"additional","affiliation":[{"name":"School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China"}]},{"given":"Yan","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,12,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1016\/j.neucom.2018.06.078","article-title":"A survey on deep learning based bearing fault diagnosis","volume":"335","author":"Hoang","year":"2019","journal-title":"Neurocomputing"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"439","DOI":"10.1016\/j.cja.2019.07.011","article-title":"A new bearing fault diagnosis method based on modified convolutional neural networks","volume":"33","author":"Zhang","year":"2020","journal-title":"Chin. 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