向玲(1971-),女,湖北随州人,教授,博士,研究方向为电力装备状态监测与智能故障诊断,E-mail:ncepuxl@163.com。
纸质出版:2026
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向玲, 韩忠泉, 邴汉昆, 倪玮, 叶锋. 基于多尺度注意力域适应网络的跨域转子故障迁移诊断方法[J]. 动力工程学报, 2026, 46(4): 138-148.
向玲, 韩忠泉, 邴汉昆, 倪玮, 叶锋. 基于多尺度注意力域适应网络的跨域转子故障迁移诊断方法[J]. 动力工程学报, 2026, 46(4): 138-148. DOI: 10.19805/j.cnki.jcspe.2026.240805.
DOI: 10.19805/j.cnki.jcspe.2026.240805
针对源域和目标域数据分布差异大及现场故障标签样本难获取而导致诊断精度低的问题
提出了基于多尺度注意力域适应网络(MADAN)的跨域转子故障迁移诊断方法。使用多尺度卷积层直接提取振动信号的深层故障特征;利用空间和通道注意力捕捉重要特征的所属空间位置和通道
并增大其过程传递的权重
进一步增强特征的表达能力;依据域适应方法优化源域与目标域提取特征分布的差异问题
以提高迁移诊断性能。结果表明:相比于其他诊断方法
MADAN方法在跨实验台转子故障迁移任务中具有更高的诊断精度
为跨设备转子故障迁移诊断提供了新的参考。
To address the issue of significant differences in data distribution between source domain and target domain
as well as the difficulty in obtaining on-site fault label samples
which lead to low diagnostic accuracy
a cross-domain rotor fault transfer diagnosis method based on multi-scale attention domain adaptation network (MADAN) was proposed. Multiscale convolutional layers were used to directly extract deep fault features of vibration signals. Spatial and channel attention were employed to capture the spatial positions and channels where important features located
and to increase the weights of process transmission
thereby further enhancing the expression ability of the features. Based on the domain adaptation method
the differences in the feature distribution extracted from the source domain and the target domain were optimized to improve the performance of transfer diagnosis. Results show that compared to other diagnostic methods
the MADAN approach achieves higher diagnostic accuracy in cross-rig rotor fault transfer tasks
providing new reference value for cross-device rotor fault transfer diagnosis.
伍济钢, 文港, 杨康. 改进注意力机制的航空发动机试验转子系统智能故障诊断[J]. 振动与冲击, 2024, 43(4): 261-269.WU Jigang, WEN Gang, YANG Kang. Improved attention mechanism for intelligent fault diagnosis of experimental rotor systems in aero engines[J]. Journal of Vibration and Shock, 2024, 43(4): 261-269.
唐贵基, 李树才, 卢盛阳, 等. 基于CERLMDAN的转子故障诊断[J]. 动力工程学报, 2020, 40(9): 720-727.TANG Guiji, LI Shucai, LU Shengyang, et al. Rotor fault diagnosis based on CERLMDAN[J]. Journal of Chinese Society of Power Engineering, 2020, 40(9): 720-727.
夏亚磊, 张文涛, 卢一兵, 等. 弯曲与不平衡耦合作用下转子振动响应特性分析[J]. 动力工程学报, 2018, 38(10): 815-819, 854.XIA Yalei, ZHANG Wentao, LU Yibing, et al. Rotor vibration analysis under the coupled effect of shaft bend and mass unbalance[J]. Journal of Chinese Society of Power Engineering, 2018, 38(10): 815-819, 854.
左刚奥, 包文杰, 陈志昊, 等. 转子裂纹深度自适应预测与故障诊断[J]. 振动、测试与诊断, 2024, 44(4): 660-667.ZUO Gangao, BAO Wenjie, CHEN Zhihao, et al. Adaptive prediction of rotor crack depth and fault diagnosis[J]. Journal of Vibration, Measurement & Diagnosis, 2024, 44(4): 660-667.
宫文峰, 陈辉, 张泽辉, 等. 基于改进卷积神经网络的滚动轴承智能故障诊断研究[J]. 振动工程学报, 2020, 33(2): 400-413.GONG Wenfeng, CHEN Hui, ZHANG Zehui, et al. Intelligent fault diagnosis for rolling bearing based on improved convolutional neural network[J]. Journal of Vibration Engineering, 2020, 33(2): 400-413.
邓林峰, 王琦, 郑玉巧. 基于改进残差网络的风电轴承故障迁移诊断方法[J]. 振动工程学报, 2024, 37(2): 356-364.DENG Linfeng, WANG Qi, ZHENG Yuqiao. A fault transfer diagnosis method for wind turbine bearings based on improved residual neural networks[J]. Journal of Vibration Engineering, 2024, 37(2): 356-364.
周陈林, 董绍江, 李玲, 等. 滚动轴承多状态特征信息的改进型卷积神经网络故障诊断方法[J]. 振动工程学报, 2020, 33(4): 854-860.ZHOU Chenlin, DONG Shaojiang, LI Ling, et al. Method to improve convolutional neural network in rolling bearing fault diagnosis with multi-state feature information[J]. Journal of Vibration Engineering, 2020, 33(4): 854-860.
向玲, 张兴宇, 胡爱军, 等. ResNet-LSTM并行网络转子故障迁移诊断方法[J]. 动力工程学报, 2023, 43(1): 41-47.XIANG Ling, ZHANG Xingyu, HU Aijun, et al. Rotor fault transferable diagnosis method for ResNet-LSTM parallel networks[J]. Journal of Chinese Society of Power Engineering, 2023, 43(1): 41-47.
JIANG Guoqian, HE Haibo, YAN Jun, et al. Multiscale convolutional neural networks for fault diagnosis of wind turbine gearbox[J]. IEEE Transactions on Industrial Electronics, 2019, 66(4): 3196-3207.
XIAO Dengyu, HUANG Yixiang, ZHAO Lujie, et al. Domain adaptive motor fault diagnosis using deep transfer learning[J]. IEEE Access, 2019, 7: 80937-80949.
TANG Shengnan, MA Jingtao, YAN Zhengqi, et al. Deep transfer learning strategy in intelligent fault diagnosis of rotating machinery[J]. Engineering Applications of Artificial Intelligence, 2024, 134: 108678.
MISBAH I, LEE C K M, KEUNG L K. Fault diagnosis in rotating machines based on transfer learning: literature review[J]. Knowledge-Based Systems, 2024, 283: 111158.
LIU Zhaohua, LU Biliang, WEI Hualiang, et al. A stacked auto-encoder based partial adversarial domain adaptation model for intelligent fault diagnosis of rotating machines[J]. IEEE Transactions on Industrial Informatics, 2021, 17(10): 6798-6809.
YANG Bingru, LI Qi, CHEN Liang, et al. Bearing fault diagnosis based on multilayer domain adaptation[J]. Shock and Vibration, 2020: 8873960.
JIN Tongtong, YAN Chuliang, CHEN Chuanhai, et al. New domain adaptation method in shallow and deep layers of the CNN for bearing fault diagnosis under different working conditions[J]. The International Journal of Advanced Manufacturing Technology, 2023, 124(11): 3701-3712.
SHAO Xiaorui, KIM C S. Adaptive multi-scale attention convolution neural network for cross-domain fault diagnosis[J]. Expert Systems with Applications, 2024, 236: 121216.
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