赵明航 - 哈尔滨工业大学（威海）

个人简介

赵明航，1991年生，副教授，硕士生导师，威海校区机械工程系教师，美国马里兰大学帕克分校先进生命周期工程中心（CALCE）联合培养博士，主要从事复杂装备智能性能监控方面研究工作，主持国家自然科学基金青年项目、国家重点研发计划子课题、山东省自然科学基金青年项目等项目，发表SCI论文30余篇，其中第一/通讯作者18篇，中科院大类一区Top期刊10篇，IEEE Trans 5篇，高被引论文（全球前1%）4篇，热点论文（全球前0.1%）1篇，单篇论文（非综述）最高被引670余次，累计被引用1850余次。教育经历 2009.09-2013.06 重庆大学机械设计制造及其自动化本科 2013.09-2018.12 重庆大学机械工程博士 2016.09-2017.09 美国马里兰大学帕克分校联合培养工作经历 2022.12-至今哈工大威海校区副教授 2019.04-2022.12 哈工大威海校区讲师奖项荣誉全球前2%顶尖科学家“年度科学影响力排行榜”（2023年）威海校区青年拔尖副教授（2022年）全球前2%顶尖科学家“年度科学影响力排行榜”（2022年）威海市自然科学优秀学术成果三等奖（2021年）重庆市优秀博士学位论文（2020年）重庆大学优秀博士学位论文（2020年）重庆大学博士生国家奖学金（2017年）科研项目国家自然科学基金青年项目：热流固耦合作用下图注意力驱动的船舶燃烧室部件故障预测方法研究（主持，52105545，30万，2022.01-2024.12）山东省自然科学基金青年项目：深度幅频解调模式下航空发动机主轴轴承微弱故障诊断方法研究（主持，ZR2020QE156，10万，2021.01-2023.12）国家重点研发计划：制造大数据驱动的预测运行与精准服务技术及系统（参加，2019YFB1705300，2822万，2019.12-2022.11）

研究领域

热流固耦合分析及数据驱动的装备智能健康管理制造业信息化、工业知识图谱构建与大数据挖掘

近期论文

查看导师新发文章（温馨提示：请注意重名现象，建议点开原文通过作者单位确认）

B. Zhong, M. Zhao*, S. Zhong, L. Lin, Y. Zhang. Deep Exponential Excitation Networks: Towards Stronger Attention Mechanism for Weak Fault Diagnosis[J]. Structural Health Monitoring, Accepted.(JCR1区，IF=6.6) D. Liu, S. Zhong*, L. Lin, M. Zhao*, X. Fu, X. Liu. Feature-level SMOTE: Augmenting Fault Samples in Learnable Feature Space for Imbalanced Fault Diagnosis of Gas Turbines[J]. Expert Systems with Applications, 2024, 238(F): 122023.(中科院大类1区，Top期刊，IF=8.5) D. Liu, S. Zhong*, L. Lin, M. Zhao*, X. Fu, X. Liu. Deep attention SMOTE: Data augmentation with a learnable interpolation factor for imbalanced anomaly detection of gas turbines[J]. Computers in Industry, 2023, 151: 103972.(中科院大类1区，Top期刊，IF=10) D. Liu, S. Zhong*, L. Lin, M. Zhao*, X. Fu, X. Liu. CSiamese: a novel semi-supervised anomaly detection framework for gas turbines via reconstruction similarity[J]. Neural Computing and Applications, 2023, 35: 16403–16427. (JCR2区，IF=6) D. Liu, S. Zhong*, L. Lin, M. Zhao*, X. Fu, X. Liu. Highly imbalanced fault diagnosis of gas turbines via clustering-based downsampling and deep siamese self-attention network[J]. Advanced Engineering Informatics, 2022, 54: 101725. (中科院大类1区，Top期刊，IF=8.8) B. Zhong, M. Zhao*, S. Zhong, L. Lin, L. Wang. Mechanical compound fault diagnosis via suppressing intra-class dispersions: A deep progressive shrinkage perspective[J]. Measurement, 2022, 199: 111433.(JCR1区，IF=5.6) S. Zhong*, D. Liu, L. Lin, M. Zhao*, X. Fu, F. Guo. CAE-WANN: A novel anomaly detection method for gas turbines via search space extension[J]. Quality and Reliability Engineering International, 2022, 38(6): 3116-3134. (JCR2区，IF=2.3) M. Zhao*, X. Fu, Y. Zhang, L. Meng, B. Tang. Highly imbalanced fault diagnosis of mechanical systems based on wavelet packet distortion and convolutional neural networks[J]. Advanced Engineering Informatics, 2022, 51: 101535.(中科院大类1区，Top期刊，IF=8.8) M. Zhao*, X. Fu, Y. Zhang, L. Meng, S. Zhong. Data augmentation via randomized wavelet expansion and its application in few-shot fault diagnosis of aviation hydraulic pumps[J]. IEEE Transactions on Instrumentation and Measurement, 2022, 71: 3503213. (JCR1区，IF=5.6) L. Meng, M. Zhao*, Z. Cui, X. Zhang, S. Zhong. Empirical mode reconstruction: Preserving intrinsic components in data augmentation for intelligent fault diagnosis of civil aviation hydraulic pumps[J]. Computers in Industry, 2022, 134: 103557.(中科院大类1区，Top期刊，IF=10) S. Fu, Y. Zhang*, L. Lin, M. Zhao*, S. Zhong. Deep residual LSTM with domain-invariance for remaining useful life prediction across domains[J]. Reliability Engineering & System Safety, 2021, 216: 108012.(中科院大类1区，Top期刊，IF=8.1) M. Zhao*, S. Zhong, X. Fu, B. Tang, S. Dong, M. Pecht. Deep residual networks with adaptively parametric rectifier linear units for fault diagnosis[J]. IEEE Transactions on Industrial Electronics, vol. 68, no. 3, pp. 2587-2597, 2021.(中科院大类1区，Top期刊，IF=7.7，ESI高被引论文) M. Zhao*, S. Zhong, X. Fu, B. Tang, M. Pecht. Deep residual shrinkage networks for fault diagnosis[J]. IEEE Transactions on Industrial Informatics, vol. 16, no. 7, pp. 4681-4690, 2020. M. Zhao, B. Tang*, L. Deng, M. Pecht. Multiple wavelet regularized deep residual networks for fault diagnosis[J]. Measurement, 2020, 152: 107331.(JCR1区，IF=5.6) M. Zhao, M. Kang*, B. Tang, M. Pecht. Multiple wavelet coefficients fusion in deep residual networks for fault diagnosis[J]. IEEE Transactions on Industrial Electronics, 2019, 66(6): 4696-4706.(ESI高被引论文，中科院大类1区，Top期刊，IF=7.7) M. Zhao, M. Kang*, B. Tang, M. Pecht. Deep residual networks with dynamically weighted wavelet coefficients for fault diagnosis of planetary gearboxes[J]. IEEE Transactions on Industrial Electronics, 2018, 65(5): 4290-4300.(中科院大类1区，Top期刊，IF=7.7，ESI高被引论文，Google Scholar>300) M. Zhao, B. Tang*, Q. Tan. Bearing remaining useful life estimation based on time–frequency representation and supervised dimensionality reduction[J]. Measurement, vol. 86, pp. 41-55, 2016. (JCR1区，IF=5.6) M. Zhao, B. Tang*, Q. Tan. Fault diagnosis of rolling element bearing based on S transform and gray level co-occurrence matrix[J]. Measurement Science and Technology, 2015, 26(8): 085008.(JCR2区，IF=2.4) Yan Zhang*, Haifeng Zhang, Q. Huang, Y. Han, M. Zhao. DsP-YOLO: An anchor-free network with DsPAN for small object detection of multiscale defects[J]. Expert Systems with Applications, Accepted.(中科院大类1区，Top期刊，IF=8.5) S. Fu*, L. Lin, Y. Wang, F. Guo, M. Zhao, B. Zhong, S. Zhong. MCA-DTCN: A novel dual-task temporal convolutional network with multi-channel attention for first prediction time detection and remaining useful life prediction[J]. Reliability Engineering & System Safety 241 (2024): 109696. (中科院大类1区，Top期刊，IF=8.1) K. Zhang*, Z. Li, Q. Zheng, G. Ding, B. Tang, M. Zhao. Fault diagnosis with bidirectional guided convolutional neural networks under noisy labels[J]. IEEE Sensors Journal, 2023, 23(16): 18810-18820. (JCR1区，IF=4.3) T. Zuo, K. Zhang*, Q. Zheng, X. Li, Z. Li, G. Ding, M. Zhao. A hybrid attention-based multi-wavelet coefficient fusion method in RUL prognosis of rolling bearings[J]. Reliability Engineering & System Safety, 2023, 237: 109337.(中科院大类1区，Top期刊，IF=8.1) Z. Yan, Z. Cui*, M. Zhao, S. Zhong, L. Lin. The carbon emission and maintenance-cost guided optimization of aero-engine clearance schedule[J]. The International Journal of Advanced Manufacturing Technology, 2023: 1-18.(JCR2区，IF=3.4) Q. Li, B. Tang*, L. Deng, P. Xiong, M. Zhao. Cross-attribute adaptation networks: Distilling transferable features from multiple sampling-frequency source domains for fault diagnosis of wind turbine gearboxes[J]. Measurement, 2022, 200: 111570.(JCR1区，IF=5.6) Z. Cui*, Z. Yan, M. Zhao, S. Zhong. Gas path parameter prediction of aero-engine based on an autoregressive discrete convolution sum process neural network[J]. Chaos, Solitons & Fractals, 2022, 154: 111627.(JCR1区，IF=7.8) Z. Yan, S. Zhong*, L. Lin, Z. Cui, M. Zhao. A step parameters prediction model based on transfer process neural network for exhaust gas temperature estimation after washing aero-engines[J]. Chinese Journal of Aeronautics, 2022, 35(3): 98-111. (中科院大类1区，Top期刊，IF=5.7) S. Fu, S. Zhong*, L. Lin, M. Zhao. A Novel Time-Series Memory Auto-Encoder With Sequentially Updated Reconstructions for Remaining Useful Life Prediction[J]. IEEE Transactions on Neural Networks and Learning Systems, 2022, 33(12): 7114-7125. (中科院大类1区，Top期刊，IF=10.4) B. Li, B. Tang*, L. Deng, M. Zhao. Self-Attention ConvLSTM and Its Application in RUL Prediction of Rolling Bearings[J]. IEEE Transactions on Instrumentation and Measurement, 2021, 70: 3518811.(JCR1区，IF=5.6) S. Fu, S. Zhong*, L. Lin, M. Zhao. A re-optimized deep auto-encoder for gas turbine unsupervised anomaly detection[J]. Engineering Applications of Artificial Intelligence, 2021, 101: 104199.(JCR1区，IF=8) P. Xiong, B. Tang*, L. Deng, M. Zhao, X. Yu. Multi-block domain adaptation with central moment discrepancy for fault diagnosis[J]. Measurement, 2021, 169: 108516.(JCR1区，IF=5.6) X. Zhou, X. Fu*, M. Zhao, S. Zhong. Regression model for civil aero-engine gas path parameter deviation based on deep domain-adaptation with Res-BP neural network[J]. Chinese Journal of Aeronautics, 2021, 34(1): 79-90.(中科院大类1区，Top期刊，IF=5.7) T. Song, B. Tang*, M. Zhao, L. Deng. An accurate 3-D fire location method based on sub-pixel edge detection and non-parametric stereo matching[J]. Measurement, 2014, 50: 160-171. (JCR1区，IF=5.6) X. Zhou, X. Fu*, M. Zhao, S. Zhong. Regression Model for Civil Aero-engine Gas Path Parameter Deviations Based on Res-BP Neural Network[C]//International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC), 2019: 188-196. S. Zhong, D. Liu, L. Lin, M. Zhao, X. Fu, F. Guo. A novel anomaly detection method for gas turbines using weight agnostic neural network search[C]//Asia-Pacific International Symposium on Advanced Reliability and Maintenance Modeling (APARM), 2020: 1-6. D. Liu, S. Zhong, L. Lin, M. Zhao, X. Xia, X. Fu, Z. Cui. A Novel Performance Prediction Method for Gas Turbines Using the Prophet Model[C]//International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC), 2021: 203-208. 中文论文截至目前，发表EI论文5篇，其中通讯作者2篇。王月, 赵明航*, 刘雪云, 林琳, 钟诗胜. 基于孪生减元注意力网络的航空发动机故障诊断[J]. 航空动力学报, 已录用. 钟诗胜, 陈曦, 赵明航*, 张永健. 引入词集级注意力机制的中文命名实体识别方法[J]. 吉林大学学报(工学版), 2022, 52(05): 1098-1105. 汤宝平*, 熊学嫣, 赵明航, 谭骞.多共振分量融合CNN的行星齿轮箱故障诊断[J]. 振动、测试与诊断, 2020, 40(3): 507-512. 熊鹏, 汤宝平*, 邓蕾, 赵明航. 基于动态加权密集连接卷积网络的变转速行星齿轮箱故障诊断[J]. 机械工程学报, 2019, 55(07): 52-57. 苏祖强, 汤宝平*, 赵明航, 秦毅. 基于多故障流形的旋转机械故障诊断[J]. 振动工程学报, 2015, 28(02): 309-315.