Article(id=1284794259215724998, tenantId=1146029695717560320, journalId=1283840536528293913, issueId=1284794217658560734, articleNumber=null, orderNo=null, doi=10.19912/j.0254-0096.tynxb.2025-0147, pmid=null, cstr=null, oa=null, hot=0, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1737043200000, receivedDateStr=2025-01-17, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1784248421719, onlineDateStr=2026-07-17, pubDate=null, pubDateStr=null, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1784248421719, onlineIssueDateStr=2026-07-17, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1784248421719, creator=13701087609, updateTime=1784248421719, updator=13701087609, issue=Issue{id=1284794217658560734, tenantId=1146029695717560320, journalId=1283840536528293913, year='2026', volume='47', issue='6', pageStart='1', pageEnd='814', issueExtLink='null', onlineDate='null', pubDate='1783180800000', pubDateStr='2026-07-05', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=1, specialIssue=null, createTime=1784248411812, creator='13701087609', updateTime=1784252840208, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1284812791785689442, tenantId=1146029695717560320, journalId=1283840536528293913, issueId=1284794217658560734, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1284812791785689443, tenantId=1146029695717560320, journalId=1283840536528293913, issueId=1284794217658560734, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=361, endPage=371, ext={EN=ArticleExt(id=1284794259526103496, articleId=1284794259215724998, tenantId=1146029695717560320, journalId=1283840536528293913, language=EN, title=GRADIENT DYNAMIC SELECTIVE ALIGNMENT FOR DCFFC-FRNET WIND TURBINE ROLLING BEARING FAULT DIAGNOSIS, columnId=null, journalTitle=Acta Energiae Solaris Sinica, columnName=null, runingTitle=null, highlight=null, articleAbstract=A fault diagnosis method for wind turbine rolling bearings is proposed, which combines the gradient dynamic selective alignment （DSA） method, dual-channel feature fusion technique （DCFF）, and deep learning. Firstly, the acquired one-dimensional time series data are transformed into two different two-dimensional image representations using recursive mapping （RP） and Markov transfer field （MTF）. These transformed images are then input into an encoder for reconstruction into a low-dimensional representation. Next, a dual-channel feature fusion convolutional neural network （DCFFC） is constructed, embedding an improved attention mechanism within FRNet to extract in-depth feature information. A second-level feature fusion is performed to complete the feature classification. Finally, a gradient masking matrix is constructed using the data reconstruction loss of the decoder and the feature extraction network classification loss to achieve gradient dynamic self-adaptation throughout the fault diagnosis process. Experimental validation using the Paderborn dataset demonstrates that the proposed method achieves a diagnostic accuracy of 99%, effectively extracting fault-specific diagnostic information., authors=Yang Fulong1,2 , Gong Lijun1 , Wu Feng1 , Zhou Jinjin1 , Yang Qing1 , Zhang Teng1 , authorsList=Yang Fulong, Gong Lijun, Wu Feng, Zhou Jinjin, Yang Qing, Zhang Teng, authorCompany=1. School of Electrical Engineering and Information Engineering, Lanzhou University of Technology, Lanzhou 730050, China; 1,2 , 巩丽俊1 , 吴峰1 , 周津津1 , 杨庆1 , 张腾1 , authorsList=杨富龙, 巩丽俊, 吴峰, 周津津, 杨庆, 张腾, authorCompany=1.兰州理工大学电气工程与信息工程学院,兰州 730050;
太阳能学报
2026
, 47
(6) :
361
-371
梯度动态选择看齐的DCFFC-FRNet风电滚动轴承故障诊断
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杨富龙1,2 , 巩丽俊1 , 吴峰1 , 周津津1 , 杨庆1 , 张腾1
作者信息
1.兰州理工大学电气工程与信息工程学院,兰州 730050;
GRADIENT DYNAMIC SELECTIVE ALIGNMENT FOR DCFFC-FRNET WIND TURBINE ROLLING BEARING FAULT DIAGNOSIS
Yang Fulong1,2 , Gong Lijun1 , Wu Feng1 , Zhou Jinjin1 , Yang Qing1 , Zhang Teng1
Affiliations
1. School of Electrical Engineering and Information Engineering, Lanzhou University of Technology, Lanzhou 730050, China;
doi: 10.19912/j.0254-0096.tynxb.2025-0147
文章导航
提出一种梯度动态选择看齐（DSA）、双通道特征融合技术（DCFF）与深度学习相结合的风电滚动轴承故障诊断方法。首先,将采集到的一维时序数据采用递归图（RP）和马尔可夫转移场（MTF）转化为两种不同的二维图像数后据输入编码器重构为低维表示;然后,构建内嵌改进注意力机制FRNet的双通道特征融合卷积神经网络（DCFFC）提取深度特征信息,进行二级特征融合后完成特征分类;最后,利用解码器数据重构损失和特征提取网络分类损失构建梯度掩蔽矩阵,实现整个故障诊断过程梯度动态自适应。通过帕德伯恩数据集进行实验验证,结果表明该方法的诊断准确率为99%,能有效提取故障特征信息。
故障诊断
/
滚动轴承
/
风力发电机
/
卷积神经网络
/
梯度自适应
A fault diagnosis method for wind turbine rolling bearings is proposed, which combines the gradient dynamic selective alignment （DSA） method, dual-channel feature fusion technique （DCFF）, and deep learning. Firstly, the acquired one-dimensional time series data are transformed into two different two-dimensional image representations using recursive mapping （RP） and Markov transfer field （MTF）. These transformed images are then input into an encoder for reconstruction into a low-dimensional representation. Next, a dual-channel feature fusion convolutional neural network （DCFFC） is constructed, embedding an improved attention mechanism within FRNet to extract in-depth feature information. A second-level feature fusion is performed to complete the feature classification. Finally, a gradient masking matrix is constructed using the data reconstruction loss of the decoder and the feature extraction network classification loss to achieve gradient dynamic self-adaptation throughout the fault diagnosis process. Experimental validation using the Paderborn dataset demonstrates that the proposed method achieves a diagnostic accuracy of 99%, effectively extracting fault-specific diagnostic information.
fault diagnosis
/
rolling bearing
/
wind power generator
/
convolutional neural network
/
gradient adaptive
杨富龙, 巩丽俊, 吴峰, 周津津, 杨庆, 张腾.
梯度动态选择看齐的DCFFC-FRNet风电滚动轴承故障诊断.
太阳能学报,
2026
, 47
(6)
: 361
-371
.
DOI: 10.19912/j.0254-0096.tynxb.2025-0147
Yang Fulong, Gong Lijun, Wu Feng, Zhou Jinjin, Yang Qing, Zhang Teng.
GRADIENT DYNAMIC SELECTIVE ALIGNMENT FOR DCFFC-FRNET WIND TURBINE ROLLING BEARING FAULT DIAGNOSIS[J].
Acta Energiae Solaris Sinica ,
2026
, 47
(6)
: 361
-371
.
DOI: 10.19912/j.0254-0096.tynxb.2025-0147
参考文献
引证文献
[1] 余萍, 宋紫琼, 曹洁, 等. 基于BNN-RA模型的风电机组轴承故障诊断研究[J]. 太阳能学报, 2025, 46(3): 643-651.
2026年第47卷第6期
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doi: 10.19912/j.0254-0096.tynxb.2025-0147
接收时间：2025-01-17
首发时间：2026-07-17
https://castjournals.cast.org.cn/joweb/tynxb/CN/10.19912/j.0254-0096.tynxb.2025-0147
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2种不同金属材料的力学参数
科 属数 种数 占总种数比例 属 种数 占总种数比例 鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78 小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39 多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39 红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87 小菇属 Mycena 11 5.26 光柄菇属 Pluteus 5 2.39 红菇属 Russula 17 8.13 栓菌属 Trametes 5 2.39
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