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王荣喜

副研究员 硕士生导师

  • 所在单位: 机械工程学院
  • 学历: 博士研究生毕业
  • 学位: 博士

工业大数据下的智能诊断分析

当前位置: 中文主页 - 工业大数据下的智能诊断分析
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装备服役过程既是一个其功能满足企业生产实际需要的过程,也是一个围绕系统服役安全可靠性风险感知、状态管理、调控优化、应急保障不断循环的过程。从动态管控、持续改进、不断优化的广义质量管理哲理出发,整合装备全寿命、全过程、全特性的管控需求,融合安全机理与感知数据,提出了以“风险感知辨识、健康状态管理、综合应急保障、系统调控优化”为核心的“四元”服役安全可靠性风险管控范式和多主体、多线索闭环的技术体系架构。

 

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)