研究方向介绍

       以核电及超超临界汽轮机、海上风机和高端压缩机组为代表的复杂机电系统为研究对象，以现场的海量、高维状态监测信号大数据为基础，从复杂系统的动力学特征及装备运行状态智能监测、智能分析、智能溯源和智能运维的集成控制思路出发，融合工业互联网、物联网、工业大数据分析等技术，以互联、物联、云计算等为支撑，按照“模型驱动、物网融合、预控协同、动态改进”的理念，以“企业问题引领—链条集成化设计—关键技术突破—系统平台支撑”为技术主线，重点针对装备运行过程中的智能诊断分析问题，从“集成高效智能检验监测、复杂状态智能协同控制、多重闭环智能综合保障、工业安全组态软件”等方面开展研究。


项目支撑：
 
国家重点研发计划、国家自然科学基金、中国博士后科学基金、企业项目
 
代表性成果：

 

[20]  A Generative Adversarial Networks Based Methodology for Imbalanced Multidimensional Time-series Augmentation of Complex Electromechanical Systems. Applied Soft Computing, 2024,153:111301. (Indexed by SCI and EI, IF:8.7)

[19]  An Optimization Framework for Enterprise Quality Infrastructure System under Coupling Constraints. International Journal of Production Economics, 2023,262:108897. (Indexed by SCI and EI, IF:11.275)

[18]  A novel self-learning framework for fault identification of wind turbine drive bearings. Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering, 2023. (Indexed by SCI and EI, IF:1.623)

[17]  Uncertain Texture Features Fusion Based Method for Performance Condition Evaluation of Complex Electromechanical Systems. ISA Transactions, 2020, 108: 1-28. (Indexed by SCI and EI, IF:5.911)

[16]  A new method for multivariable nonlinear coupling relations analysis in complex electromechanical system. Applied Soft Computing, 2020, 94: 106457. (Indexed by SCI and EI, IF:8.263)

[15]  Condition-Based Dynamic Supportability Mechanism for the Performance Quality of Large-Scale Electromechanical Systems. IEEE Access, 2020(8): 117036-117050. (Indexed by SCI and EI, IF:4.098)

[14]  Fault recognition using an ensemble classifier based on Dempster–Shafer Theory. Pattern Recognition, 2020(99): 107079. (Indexed by SCI and EI, IF:8.518)【TOP期刊】

[13]  A Dilated Convolution Network Based LSTM Model for Multi-step Prediction of Chaotic Time-series. Computational and Applied Mathematics, 2020, 39:30. (Indexed by SCI and EI, IF:2.998)

[12]   An Artificial Immune and Incremental Learning Inspired Novel Framework for Performance Pattern Identification of Complex Electromechanical Systems. Science China-Technological Sciences, 2020, 63(1): 1-13. (Indexed by SCI and EI, IF:3.903)

[11]   Classification of weld defects based on the analytical hierarchy process and Dempster–Shafer evidence theory. Journal of Intelligent Manufacturing, 2019.30(4): p. 2013-2024. (Indexed by SCI and EI, IF:7.136)

[10]  Data fusion combined with echo state network for multivariate time series prediction in complex electromechanical system. Computational & Applied Mathematics, 2018. 37(5): p. 5920-5934. (Indexed by SCI and EI, IF:2.998)

[9]   An information transfer based novel framework for fault root cause tracing of complex electromechanical systems in the processing industry. Mechanical Systems and Signal Processing, 2018. 101: p. 121-139. (Indexed by SCI and EI, IF:8.934)

[8]   Analysis of multifractality of multivariable coupling relationship of complex electromechanical system in process industry. Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering, 2017.231(6): p. 1085-1100. (Indexed by SCI and EI, IF:1.822)

[7]  Interaction analysis-based information modeling of complex electromechanical systems in the processing industry. Proceedings of the Institution of Mechanical Engineers Part I-Journal of Systems and Control Engineering, 2017. 231(8): p. 638-651. (Indexed by SCI and EI, IF:1.71)

[6]  Evidence fusion-based framework for condition evaluation of complex electromechanical system in process industry. Knowledge-Based Systems, 2017. 124: p. 176-187. (Indexed by SCI and EI, IF:8.139)

[5]  Coupling analysis-based false monitoring information identification of production system in process industry. Science China-Technological Sciences, 2017. 60(6): p. 807-817. (Indexed by SCI and EI, IF:3.903)

[4]  Complex network theory-based condition recognition of electromechanical system in process industry. Science China Technological Sciences, 2016. 59(4): p. 604-617. (Indexed by SCI and EI, IF:3.903)

[3]  Fault mode prediction based on decision tree. in 2016 IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2016, October 3, 2016 - October 5, 2016. 2016. Xi'an, China: Institute of Electrical and Electronics Engineers Inc. (Indexed by EI)

[2]   Data fusion based phase space reconstruction from multi-time series. International Journal of Database Theory and Application, 2015. 8(6): p. 101-110. (Indexed by EI)

[1]   Hilbert-Huang Transform Based Pseudo-Periodic Feature Extraction of Nonlinear Time Series. in Measuring Technology and Mechatronics Automation (ICMTMA), 2015 Seventh International Conference on. 2015. (Indexed by EI)