1. 研究方向：智能计算及应用

立足于“智能制造2025”、“30·60双碳目标”、“健康中国2030”等国家重大战略需求，以工业/医疗大数据为基础，重点开展多模态智能、可解释智能、可信智能、类脑智能、联邦智能等智能计算理论与方法基础研究及其在工业装备（过程）和人体（大脑）智能监控和健康管理领域的应用。具体包括两个方向：

l  工业人工智能：工业大数据智能解析、装备/过程智能监测诊断与预测

l  医学人工智能：多模态生物医学信息融合、脑疾病智能辅助诊断、脑机接口

2. 发表学术论文（详见ORCID:https://orcid.org/0000-0002-1813-8249）

【工业人工智能方向】

Refereed Journal Papers: 

[1]   Ruxu Yue, Guoqian Jiang (通讯作者), Xiaohang Jin, Qun He, Ping Xie. Wind turbine blade icing detection based on spatio-temporal feature alignment transfer learning with imbalanced SCADA data. IEEE Transactions on Instrumentation and Measurement. 2024, 73: 3507717.

[2]     Ruchun Zhao, Guoqian Jiang (通讯作者), Qun He, Xiaohang Jin, Ping Xie. Current-Aided Vibration Fusion Network for Wind Turbine Gearbox Fault Diagnosis. IEEE Transactions on Instrumentation and Measurement. 2024, 73: 3510010.

[3]     Guoqian Jiang, Jing Wang, Lijin Wang, Ping Xie, Yingwei Li, Xiaoli Li. An Interpretable Convolutional Neural Network with Multi-Wavelet Kernel Fusion for Intelligent Fault Diagnosis. Journal of Manufacturing Systems. 2023, 70: 18-30.

[4]     Guoqian Jiang, Ruxu Yue, Qun He, Ping Xie, Xiaoli Li. Imbalanced learning for wind turbine blade icing detection via spatio-temporal attention model with a self-adaptive weight loss function. Expert Systems with Applications. 2023, 229: 120428.

[5]     Lijin Wang, Weipeng Fan, Guoqian Jiang（通讯作者）, Qun He, Ping Xie. An Efficient Federated Transfer Learning Framework for Collaborative Monitoring of Wind Turbines in IoE-enabled Wind Farms. Energy. 2023, 284: 128518.

[6]     Lijin Wang, Guoqian Jiang (通讯作者), Jing Wang, Ping Xie. MVGNet: Multi-view Graph Network with Interactive Shared Fusion for Fault Diagnosis of Wind Turbines. IEEE Sensors Journal. 2023, 23(21): 26804-26819.

[7]     Jing Wang, Guoqian Jiang (通讯作者), Lijin Wang, Yingwei Li, Xiaoli Li, Ping Xie. A Novel Impulse Information Enhanced Semi-Supervised Learning for Few-Label Fault Diagnosis of Rotary Machines. IEEE Sensors Journal. 2023, 22(22): 27658 - 27669.

[8]     Qun He, Ruchun Zhao, Quoqian Jiang（通讯作者）, Ping Xie. Differential-Augmented Current Feature Learning Network With Multi-Information Interaction for Fault Diagnosis in Electromechanical Drive System. IEEE Sensors Journal. 2023, 23(4): 15942-15951.

[9]     Xiaohang Jin, Xiaoying Zhang, Xu Cheng, Guoqian Jiang, Lesedi Masisi, Wei Huang. A Physics-Based and Data-Driven Feature Extraction Model for Blades Icing Detecion of Wind Turbines. IEEE Sensors Journal. 2023, 23(4): 3944-3954.

[10]  Guoqian Jiang, Wenyue Li, Jiarong Bai, Qun He, Ping Xie. SCADA Data-Driven Blade Icing Detection for Wind Turbines: An Enhanced Spatio-Temporal Feature Learning Approach. Measurement Science and Technology. 2023, 34(5): 054004.

[11]  Ping Xie, Xingmin Zhang, Guoqian Jiang (通讯作者), Jian Cui, Qun He. Investigation of Deep Transfer Learning for Cross-Turbine Diagnosis of Wind Turbine Faults. Measurement Science and Technology. 2023, 34 (4): 044009.

[12]  Pengfei Liang, Bin Wang, Guoqian Jiang, Na Li, Lijie Zhang. Unsupervised fault diagnosis of wind turbine bearing via a deep residual deformable convolution network based on subdomain adaptation under time-varying speeds. Engineering Applications of Artificial Intelligence, 2023, 118:105656.

[13]  Guoqian Jiang, Wenyue Li, Weipeng Fan, Qun He, Ping Xie. TempGNN: A Temperature-based Graph Neural Network Model for System-level Monitoring of Wind Turbines with SCADA Data. IEEE Sensors Journal. 2022, 22(23): 22894 - 22907.

[14]  Guoqian Jiang, Shiqiang Nie, Ping Xie, Yingwei Li, Xiaoli Li. Multiscale One-Class Classification Network for Machine Health Monitoring. IEEE Sensors Journal. 2022, 22(3):13043-13054.

[15]  Jian Cui, Ping Xie, Xiao Wang, Jing Wang, Qun He, Guoqian Jiang (通讯作者). M2FN: An End-to-End Multi-task and Multi-sensor Fusion Network for Intelligent Fault Diagnosis. Measurement. 2022, 204: 112085.

[16]  Guoqian Jiang, Wenda Zhou, Qi Chen, Qun He, Ping Xie. Dual Residual Attention Network for Remaining Useful Life Prediction of Bearings. Measurement, 2022, 199: 111424.

[17]  Guoqian Jiang, Weipeng Fan, Wenda Zhou, Ping Xie, Qun He, Xiaoli Li. DeepFedWT: A Federated Deep Learning Framework for Fault Detection of Wind Turbines. Measurement, 2022, 199: 111529.

[18]  Guoqian Jiang, Chenling Jia, Shiqiang Nie, Xin Wu, Qun He, Ping Xie. Multiview enhanced fault diagnosis for wind turbine gearbox bearings with fusion of vibration and current signals. Measurement, 2022, 196: 111159.

[19]  Xiao Wang, Zheng Zheng, Guoqian Jiang（通讯作者）, Qun He, Ping Xie, Xin Wu, Xiaoli Li. Detecting Wind Turbine Blade Icing with a Multiscale Long Short-Term Memory Network. Energies. 2022, 15(8):2864.

[20]  Qun He, Yanhua Pang, Guoqian Jiang, Ping Xie. A Spatio-Temporal Multiscale Neural Network Approach for Wind Turbine Fault Diagnosis with Imbalanced SCADA Data. IEEE Transactions on Industrial Informatics. 2021,17(10): 6875-6884.

[21]  Qun He, Jingyi Zhao, Guoqian Jiang（通讯作者）, Ping Xie. An Unsupervised Multi-view Sparse Filtering Approach for Current-based Wind Turbine Gearbox Fault Diagnosis. IEEE Transactions on Instrumentation and Measurement, 2020, 69(8): 5569-5578.

[22]  Yanhua Pang, Qun He, Guoqian Jiang, Ping Xie. Spatio-temporal fusion neural network for multi-class fault diagnosis of wind turbines based on SCADA data. Renewable Energy. 2020, 161: 510-524.

[23]  Hong Wang, Hongbin Wang, Guoqian Jiang, Yueling Wang, Shuang Ren. A Multiscale Spatio-Temporal Convolutional Deep Belief Network for Sensor Fault Detection of Wind Turbine. Sensors, 2020, 20, 3580.

[24]  Guoqian Jiang, Haibo He, Jun Yan, Ping Xie. Multiscale convolutional neural networks for fault diagnosis of wind turbine gearbox. IEEE Transactions on Industrial Electronics. IEEE Transactions on Industrial Electronics, 2019, 66 (4): 3196-3207. (ESI高被引论文)

[25]  Xin Wu, Guoqian Jiang(通讯作者), Xiao Wang, Ping Xie, Xiaoli Li. A multi-level denoising autoencoder approach for wind turbines fault detection. IEEE Access, 2019, 7: 59376-59387.

[26]  Xin Wu, Hong Wang, Guoqian Jiang(通讯作者), Ping Xie, Xiaoli Li. Monitoring Wind Turbine Gearbox with Echo State Network Modeling and Dynamic Threshold Using SCADA Vibration Data. Energies, 2019, 12(6): 982.

[27]  Hong Wang, Hongbin Wang, Guoqian Jiang, Yuling Wang. Early Fault Detection of Wind Turbines Based on Operational Condition Clustering and Optimized Deep Belief Network Modeling. Energies. 2019, 12(6):984.

[28]  Jimeng Li, Ming Li, Jinfeng Zhang, Guoqian Jiang. Frequency-shift multiscale noise tuning stochastic resonance method for fault diagnosis of generator bearing in wind turbine. Measurement, 2019, 133: 421-432.

[29]  Guoqian Jiang, Ping Xie, Haibo He, Jun Yan. Wind turbine fault detection using a denoising autoencoder with temporal information. IEEE/ASME Transactions on Mechatronics, 2018, 23(1): 89-100.

[30] Guoqian Jiang, Haibo He, Ping Xie, Yufei Tang. Stacked multilevel denoising autoencoders: a new representation learning approach for wind turbine gearbox fault diagnosis. IEEE Transactions on Instrumentation and Measurement, 2017, 66(9):2391-2402. (ESI高被引论文)

[31]  Guoqian Jiang, Ping Xie, Xiao Wang, et al. Intelligent fault diagnosis of rotary machinery based on unsupervised multiscale representation learning. Chinese Journal of Mechanical Engineering, 2017, 30(6): 1314-1324.

[32]  Guoqian Jiang, Ping Xie, Shuo Du, et al. A new fault diagnosis model for rotary machines based on MWPE and ELM. Insight-Non-Destructive Testing and Condition Monitoring, 2017, 59(12): 644-652.

[33] Qun He, Xiaoru Ren, Guoqian Jiang(通讯作者), Ping Xie. A hybrid feature extraction methodology for gear pitting fault detection using motor stator current signal. Insight, 2014, 56(6): 326-333.

中文期刊

[1]     江国乾，徐向东，白佳荣，何群，谢平. 基于多粒度时间卷积网络的超短期风功率预测. 太阳能学报. 已录用.

[2]     江国乾，周俊超，武鑫，徐向东，何群，谢平. 基于空洞因果卷积网络的风电机组异常检测. 太阳能学报，2023, 44(5): 368-375.

[3]     江国乾，白佳荣，李文悦，何群，谢平. 基于卷积自编码器的风电机组变桨轴承故障预警与定位. 可再生能源. 2023, 41(11): 1469-1475.

[4]     何群，李晔阳，江国乾（通讯作者），苏楠，谢平，武鑫. 基于条件卷积自编码高斯混合模型的风电齿轮箱健康评估. 太阳能学报. 2023, 44(12): 214-220.

[5]     江国乾，王景，张敬超，李陈，李小俚，李英伟. 无人直升机尾桨轴承超声信号故障映射模型. 测控技术. 2023, 42(5): 112-119.

[6]     姜浪朗, 张敬超, 江国乾, 苏连成, 李英伟. 基于超声解调信号多特征融合的轴承故障识别[J]. 燕山大学学报, 2022,46(06): 484-491+560.

[7]     何 群，赵婧怡，江国乾，贾晨凌，谢 平. 基于电流信号稀疏滤波特征融合的齿轮箱故障诊断方法. 电网技术, 2020, 44(5): 1964-1971.

[8]     何群，尹飞飞，武鑫，谢平，江国乾. 基于长短时记忆网络的风电齿轮箱故障预测. 计量学报, 2020, 41(10): 1284-1290.

[9]     王霄，谢平，郭源耕，武鑫，江国乾，何群. 基于多字典-共振稀疏分解的脉冲故障特征提取. 中国机械工程，2019，30(20)：2456-2462.

[10]  李继猛，黄梦君，谢平，江国乾，陈萌，何群. 同步压缩-交叉小波变换及滚动轴承故障特征增强. 计量学报, 2018, 39(2):237-241.

[11]  何群，王红，江国乾，谢平，李继猛，王腾超. 基于相关主成分分析和极限学习机的风电主轴承状态监测研究. 计量学报, 2018, 39(1): 89-93.

[12]  江国乾，任孝儒，何群，谢平，等. 一种计及负荷突变的异步电机转子断条故障在线检测新方法. 计量学报, 2018, 39(5): 721-725.

[13]  江国乾, 谢平, 王霄, 何群, 李继猛. 基于排序模式相异性分析的轴承健康监测. 中国机械工程, 2017, 28(6):714-722.

[14]  谢平，王一凡，江国乾，等. 基于样本熵的风力发电机故障检测. 计量学报. 2017, 38(5): 626-630.

[15]  何群, 赵文爽, 江国乾, 谢平. 基于EEMD与AR建模的风电场风速预测. 计量学报, 2015, 36(2): 181-186.

[16]  何群, 李磊, 江国乾, 谢平. 基于 PCA 和多变量极限学习机的轴承剩余寿命预测. 中国机械工程 2014;25(7): 984-988.

[17]  谢平，杨玉昕，江国乾，李小俚，李兴林. 基于局部均值分解的滚动轴承故障诊断新方法. 计量学报, 2014, 35(1): 73-77.

[18]  谢平, 江国乾, 武鑫, 李小俚. 基于多尺度熵和距离评估的轴承故障诊断. 计量学报, 2013, 34(6):548-553.

【医学交叉智能方向】

[1]     Wanyu Hu, Guoqian Jiang（通讯作者）, Junxia Han, Xiaoli Li, Ping Xie. Regional-Asymmetric Adaptive Graph Convolutional Neural Network for Diagnosis of Autism in Children With Resting-State EEG. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2024, 32: 200-211.

[2]     Junxia Han, Guoqian Jiang, Gaoxiang Ouyang, Xiaoli Li. A Multimodal Approach for Identifying Autism Spectrum Disorders in Children. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2022, 30: 2003-2011.

[3]     Qun He, Lufeng Feng, Guoqian Jiang（通讯作者）, Ping Xie. Multi-Modal Multi-Task Neural Network for Motor Imagery Classification with EEG and fNIRS signals. IEEE Sensors Journal. 2022, 22(21):20695-20706.

[4]     Chunyao Xu, Chao Sun, Guoqian Jiang, Xiaoling Chen, Qun He, Ping Xie. Two-level multi-domain feature extraction on sparse representation for motor imagery classification. Biomedical Signal Processing and Control, 2020, 62, 102160.

[5]     谢平，胡锦城，江国乾，王鹏宇，门延帝. 基于多任务学习的快速序列视觉呈现脑电图分类[J]. 仪器仪表学报, 2023, 44(11): 215-223.

[6]     何群，李冉冉，付子豪，江国乾，谢平. 基于改进MEDA算法的脑电情绪识别. 仪器仪表学报，2021,42(12): 157-166.

[7]     何群，杜硕，张园园，江国乾，谢平. 融合单通道框架及多通道框架的运动想象分类. 仪器仪表学报，2018，39(9): 20-29.

[8]     何群，徐香院，江国乾，童云杰，谢平. 融合脑电与近红外脑地形图特征学习的多模式分类. 中国生物医学工程学报, 2023, 42(03): 301-310.

3. 申请/授权发明专利

[1]     江国乾，贾晨凌，谢平，等. 一种融合振动与电流信号协同学习的齿轮箱故障诊断方法，发明专利，专利号：ZL202010534929.8，授权日期：2021.06.22

[2]     江国乾，周文达，谢平，等. 一种齿轮箱轴承剩余寿命的预测方法.专利号：ZL202110461409.3，授权日期：2022.06.07

[3]     江国乾，范伟鹏，谢平，等. 一种风电机组联邦故障诊断方法及系统. 发明专利，专利号：202110520120.4，授权日期：2022.12.02

[4]     江国乾，周文达，谢平，等. 一种基于深度注意力网络的轴承剩余寿命预测方法. 发明专利，专利号：ZL202110955129.8，授权日期：2023.08.01

[5]     江国乾，王崑宇，谢平，等. 一种柔性驱动的可穿戴手部康复机器人. 发明专利，专利号：ZL202210006785.8，授权日期：2023.02.14

[6]     江国乾，贾晨凌，谢平，等. 基于多视图关联特征学习的轴承故障诊断方法. 申请号：202110473166.5，申请日：2021.04.29，申请公布号：CN113255458A，申请公布日：2021.08.13

[7]     江国乾，聂世强，谢平，等. 多尺度单分类卷积网络的风电齿轮箱故障检测方法. 申请号：202110547118.6，申请日期：2021.05.19，申请公布号：CN 113240022A，申请公布日：2021.08.10

[8]     江国乾，聂世强，谢平，等. 一种基于半监督单分类网络的变工况故障检测方法. 申请号：202110971115.5，申请日期：2021.08.24，申请公布号：CN113673442A，申请公布日：2021.11.19

[9]     江国乾，李文悦，谢平，等. 一种基于图神经网络的风电机组故障预警方法. 申请号：202111478422.6，申请日期：2021.12, 申请公布号：CN114372504A，申请公布日：2022.04.19

[10]  江国乾，王俪瑾，陈琦，等. 一种融合多源异构监测数据的风电传动系统健康评估方法. 申请号：202111508338.4，申请日期：2021.12.10, 申请公布号：CN114358511A，申请公布日：2022.04.15

[11]  江国乾，周文达，李小俚，等. 一种基于多级典型相关分析网络的轴承剩余寿命预测方法. 申请号：202210799982.X，申请日：2022.07.06, 申请公布号：CN115270859A，申请公布日：2022.11.01

[12]  江国乾，王景，李英伟，等. 自适应参数高斯卷积核神经网络的滚动轴承故障诊断方法. 申请号：202210857939.4，申请日期：2022.07.20, 申请公布号：CN115184015A，申请公布日：2022.10.14

[13]  江国乾，王俪瑾，范伟鹏，等. 一种基于多视角图神经网络的风电机组故障诊断方法. 申请号：202210841156.7，申请日期：2022.07.18, 申请公布号：CN115272811A，申请公布日：2022.11.01

[14]  江国乾，徐向东，谢平，等. 一种基于图网络的多机组风功率预测方法. 申请号：202210840900.1，申请日期：2022.07.18, 申请公布号：CN115238981A，申请公布日：2022.10.25

[15]  江国乾，岳儒旭，谢平，等. 一种基于自适应权重损失函数的风电机组叶片结冰状态检测方法. 申请号：202210853322.5, 申请日期：2022.07.08, 申请公布号：CN115270945A，申请公布日：2022.11.01

[16]  江国乾，易子宸，谢平，等. 基于可解释性图神经网络的风电机组异常检测与定位方法. 申请号：202210873216.3，申请日期：2022.07.21, 申请公布号：CN115329986A，申请公布日：2022.11.11

[17]  江国乾，范伟鹏，王俪瑾，等. 一种高效的分布式风电机组联邦迁移故障诊断方法，申请号：202211012318.2，申请日期：2022年8月23日, 申请公布号：CN115481675A，申请公布日：2022.12.16

[18]  江国乾, 周俊超, 谢平, 等. 基于局部-全局分层特征建模的风电机组故障诊断方法. 申请号：202211326195.X，申请日期：2022.10.27，申请公布号：CN115750226A，公布日期：2023.03.07

[19]  江国乾，白佳荣，苏楠，等. 一种风电机组变桨系统故障预警方法. 申请号：202211369319.2，申请日期：2022.10.28，申请公布号：CN115750228A，公布日期：2023.03.07

[20]  江国乾，马珍珍，何群，等. 基于超维计算的小样本故障诊断方法. 申请号：202311508085.X，申请日期：2023.11.14

[21]  江国乾，胡婉玉，等. 一种孤独症儿童左右脑差异性建模的脑电分析方法. 申请号：20231163597.8，申请日期：2023.12.01

     4. 软件著作权

[1]     风电大数据智能分析系统软件V1.0. 登记号：2022SR0851214，登记日期：2022年6月27日

[2]     风电传动系统故障预警软件 V1.0，登记号：2022SRF1366110，登记日期：2022年9月21日

[3]     风功率预测系统软件V1.0. 登记号：2023SR0936615，登记日期：2023年8月15日