2008.9-2013.7 清华大学热能工程系 动力工程及工程热物理 博士

2004.9-2008.7 华中科技大学能源与动力工程学院 热能与动力工程 学士

工作履历

2023.1-至今 清华大学，能源与动力工程系 副研究员

2017.8-2022.12 清华大学，能源与动力工程系 助理研究员

2015.8-2017.8 Iowa State University, Department of Mechanical Engineering, Postdoctoral Researcher

2013.7-2015.7 清华大学，机械工程系，博士后

学术兼职

中国振动工程学会转子动力学专业委员会委员

《热能动力工程》、《中国舰船研究》、《发电技术》等期刊青年编委

研究领域

· 能源动力系统状态评估与健康管理

· 信息物理系统(CPS)数据挖掘、时间序列分析

· 机器学习、深度学习方法与应用

· 旋转机械结构动力学、失效机理与预防

奖励与荣誉

2024年中国自动化学会科学技术进步奖二等奖

2021年中国可再生能源学会科学技术奖二等奖

2019年电力科技创新奖二等奖

2013年清华大学优秀博士学位论文

学术成果

1. Song, K., Liu, C., and Jiang, D. (2025). A positive-unlabeled learning approach for industrial anomaly detection based on self-adaptive training. Neurocomputing, page 130488

2. Fan, Y., Song, T., Feng, C., Song, K., Liu, C., and Jiang, D. (2024b). Fine-tuning pre-trained large time series models for prediction of wind turbine scada data. arXiv preprint arXiv:2412.00403

3. Huang, G., Liu, C., and Jiang, D. (2024a). Dynamic modeling and experimental modal analysis for the central rod-fastened rotor with hirth couplings based on fractal contact theory. Journal of Engineering for Gas Turbines and Power, 146(10)

4. Fan, Y., Feng, C., Wu, R., Liu, C., and Jiang, D. (2024a). Multiscale-attention masked autoencoder for missing data imputation of wind turbines. Knowledge-Based Systems, 299:112114

5. Feng, C., Liu, C., and Jiang, D. (2024). Root cause localization for wind turbines using physics guided multivariate graphical modeling and fault propagation analysis. Knowledge-Based Systems, 295:111838

6. Song, T., Fan, Y., Feng, C., Song, K., Liu, C., and Jiang, D. (2024). Domain-specific react for physics-integrated iterative modeling: A case study of llm agents for gas path analysis of gas turbines. arXiv preprint arXiv:2406.07572

7. Wu, R., Liu, C., and Jiang, D. (2024). Unsupervised bayesian change-point detection approach for reliable prognostics and health management of complex mechanical systems. Reliability Engineering & System Safety, 245:110037

8. Wang, H., Jiang, Z., Liu, C., Sarkar, S., Jiang, D., and Lee, Y. M. (2022). Asynchronous training schemes in distributed learning with time delay. arXiv preprint arXiv:2208.13154

9. Huang, G., Liu, C., Xie, W., and Jiang, D. (2024b). Tangential contact stiffness modeling between fractal rough surfaces with experimental validation. Archive of Applied Mechanics, 94(3):719–736

10. Feng, C., Liu, C., Jiang, D., Kong, D., and Zhang, W. (2023b). Multivariate anomaly detection and early warning framework for wind turbine condition monitoring using scada data. Journal of Energy Engineering, 149(6):04023040

11. Xie, W., Liu, C., Huang, G., Qin, Z., Zong, K., and Jiang, D. (2023). Trans-scale rough surface contact model based on molecular dynamics method: Simulation, modeling and experimental verification. European Journal of Mechanics-A/Solids, 100:105021

12. Feng, C., Liu, C., and Jiang, D. (2023a). Unsupervised anomaly detection using graph neural networks integrated with physical-statistical feature fusion and local-global learning. Renewable Energy, 206:309–323

13. Jia, K., Liu, C., Li, S., and Jiang, D. (2023). Modeling and optimization of a hybrid renewable energy system integrated with gas turbine and energy storage. Energy Conversion and Management, 279:116763

14. Huang, G., Liu, C., Xie, W., and Jiang, D. (2023). Normal contact stiffness model for fractal surfaces considering scale dependence and friction behavior. Proceedings of the Institution of Mechanical Engineers, Part J: Journal of Engineering Tribology, 237(3):527–540

15. Wu, R., Liu, C., Han, T., Yao, J., and Jiang, D. (2022). A planetary gearbox fault diagnosis method based on time-series imaging feature fusion and a transformer model. Measurement Science and Technology, 34(2):024006

16. Xie, W., Liu, C., Huang, G., Jiang, D., and Jin, J. (2022c). Nano-sized single-asperity friction behavior: Insight from molecular dynamics simulations. European Journal of Mechanics-A/Solids, 96:104760

17. Xie, W., Jiang, D., Jin, J., and Liu, C. (2022a). Single-asperity failure mechanism driven by morphology and multiaxial loading using molecular dynamics simulation. Computational Materials Science, 213:111671

18. Yao, J., Liu, C., Wang, H., and Jiang, D. (2022). A low-frequency fault detection method for low-speed planetary gearbox based on acoustic signals. Applied Acoustics, 195:108838

19. Xie, W., Liu, C., Huang, G., and Jiang, D. (2022b). Numerical and experimental study on rod-fastened rotor dynamics using semi-analytical elastic-plastic model. Journal of Engineering for Gas Turbines and Power, 144(6):064501

20. Jin, Y., Liu, C., Tian, X., Huang, H., Deng, G., Guan, Y., and Jiang, D. (2022). A novel integrated modeling approach for filter diagnosis in gas turbine air intake system. Proceedings of the Institution of Mechanical Engineers, Part A: Journal of Power and Energy, 236(3):435–449

21. Li, X., Cao, J., Guo, J., Liu, C., Wang, W., Jia, Z., and Su, T. (2022). Multi-step forecasting of ocean wave height using gate recurrent unit networks with multivariate time series. Ocean Engineering, 248:110689

22. Song, T., Liu, C., Wu, R., Jin, Y., and Jiang, D. (2022). A hierarchical scheme for remaining useful life prediction with long short-term memory networks. Neurocomputing, 487:22–33

23. Jiang, Z., Liu, C., Lee, Y. M., Hegde, C., Sarkar, S., and Jiang, D. (2022). The stochastic augmented lagrangian method for domain adaptation. Knowledge-Based Systems, 235:107593

24. Yao, J., Liu, C., Song, K., Feng, C., and Jiang, D. (2021). Fault diagnosis of planetary gearbox based on acoustic signals. Applied Acoustics, 181:108151

25. Jin, Y., Liu, C., Tian, X., Huang, H., Deng, G., Guan, Y., and Jiang, D. (2021b). A novel integrated modeling approach for filter diagnosis in gas turbine air intake system. Proceedings of the Institution of Mechanical Engineers, Part A: Journal of Power and Energy, 09576509211044392

26. Guo, J., Liu, C., Cao, J., and Jiang, D. (2021). Damage identification of wind turbine blades with deep convolutional neural networks. Renewable Energy, 174:122–133

27. Xie, W., Liu, C., Jiang, D., and Jin, J. (2021). Inelastic contact behaviors of nanosized single-asperity and multi-asperity on α-Fe surface: Molecular dynamic simulations. International Journal of Mechanical Sciences, 106569

28. Jin, Y., Liu, C., Tian, X., Huang, H., Deng, G., Guan, Y., and Jiang, D. (2021a). A hybrid model of LSTM neural networks with thermodynamic model for condition-based maintenance of compressor fouling. Measurement Science and Technology

29. Wang, N., Liu, C., and Jiang, D. (2021b). Experimental analysis of dual-rotor-support-casing system with blade-casing rubbing. Engineering Failure Analysis, 123:105306

30. Han, T., Liu, C., Wu, R., and Jiang, D. (2021). Deep transfer learning with limited data for machinery fault diagnosis. Applied Soft Computing, 103:107150

31. Yao, J., Liu, C., Song, K., Feng, C., and Jiang, D. (2021). Fault diagnosis of planetary gearbox based on acoustic signals. Applied Acoustics, 181:108151

32. Wang, H., Liu, C., Jiang, D., and Jiang, Z. (2021a). Collaborative deep learning framework for fault diagnosis in distributed complex systems. Mechanical Systems and Signal Processing, 156:107650

33. Liu, C., Lore, K. G., Jiang, Z., and Sarkar, S. (2021). Root-cause analysis for time-series anomalies via spatiotemporal graphical modeling in distributed complex systems. Knowledge-Based Systems, 211:106527

34. Liu, C. and Jiang, D. (2020). Torsional vibration characteristics and experimental study of cracked rotor system with torsional oscillation. Engineering Failure Analysis, 116:104737

35. Jiang, Z., Liu, C., Ganapathysubramanian, B., Hayes, D. J., and Sarkar, S. (2020). Predicting county-scale maize yields with publicly available data. Scientific Reports, 10(1):1–12

36. Yang, Y., Liu, C., Jiang, D., and Behdinan, K. (2020). Nonlinear vibration signatures for localized fault of rolling element bearing in rotor-bearing-casing system. International Journal of Mechanical Sciences, 173:105449

37. Saha, H., Liu, C., Jiang, Z., and Sarkar, S. (2020). Data-driven performance monitoring of dynamical systems using Granger causal graphical models. Journal of Dynamic Systems, Measurement, and Control, 142(8)

38. Han, T., Liu, C., Yang, W., and Jiang, D. (2020). Deep transfer network with joint distribution adaptation: A new intelligent fault diagnosis framework for industry application. ISA Transactions, 97:269–281

39. Liu, C., Zhao, M., Sharma, A., and Sarkar, S. (2019). Traffic dynamics exploration and incident detection using spatiotemporal graphical modeling. Journal of Big Data Analytics in Transportation, 1(1):37–55

40. Wang, N., Liu, C., and Jiang, D. (2019a). Prediction of transient vibration response of dual-rotor-blade-casing system with blade off. Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, 0954410019839884

41. Han, T., Liu, C., Yang, W., and Jiang, D. (2019b). Learning transferable features in deep convolutional neural networks for diagnosing unseen machine conditions. ISA Transactions, 93:341–353

42. Yang, Y., Liu, C., and Jiang, D. (2019). Vibration propagation identification of rotor-bearing-casing system using spatiotemporal graphical modeling. Mechanism and Machine Theory, 134:24–38

43. Han, T., Liu, C., Yang, W., and Jiang, D. (2019c). A novel adversarial learning framework in deep convolutional neural network for intelligent diagnosis of mechanical faults. Knowledge-Based Systems, 165:474–487

44. Lei, J., Liu, C., and Jiang, D. (2019). Fault diagnosis of wind turbine based on long short-term memory networks. Renewable Energy, 133:422–432

45. Wang, N., Liu, C., Jiang, D., and Behdinan, K. (2019b). Casing vibration response prediction of dual-rotor-blade-casing system with blade-casing rubbing. Mechanical Systems and Signal Processing, 118:61–77

46. Han, T., Liu, C., Wu, L., Sarkar, S., and Jiang, D. (2019a). An adaptive spatiotemporal feature learning approach for fault diagnosis in complex systems. Mechanical Systems and Signal Processing, 117:170–187

47. Ott, A., Schnable, J. C., Yeh, C.-T., Wu, L., Liu, C., Hu, H.-C., Dalgard, C. L., Sarkar, S., and Schnable, P. S. (2018). Linked read technology for assembling large complex and polyploid genomes. BMC Genomics, 19(1):651

48. Yang, W., Liu, C., and Jiang, D. (2018). An unsupervised spatiotemporal graphical modeling approach for wind turbine condition monitoring. Renewable Energy, 127:230–241

49. Liu, C., Akintayo, A., Jiang, Z., Henze, G. P., and Sarkar, S. (2018a). Multivariate exploration of non-intrusive load monitoring via spatiotemporal pattern network. Applied Energy, 211:1106–1122

50. Wu, L., Liu, C., Huang, T., Sharma, A., and Sarkar, S. (2018). Traffic sensor health monitoring using spatiotemporal graphical modeling. International Journal of Prognostics and Health Management, 9(1):022

51. Huang, T., Liu, C., Sharma, A., and Sarkar, S. (2018). Traffic system anomaly detection using spatiotemporal pattern networks. International Journal of Prognostics and Health Management, 9:003

52. Jiang, Z., Liu, C., Akintayo, A., Henze, G. P., and Sarkar, S. (2017). Energy prediction using spatiotemporal pattern networks. Applied Energy, 206:1022–1039

53. Liu, C., Ghosal, S., Jiang, Z., and Sarkar, S. (2017a). An unsupervised anomaly detection approach using energy-based spatiotemporal graphical modeling. Cyber-Physical Systems, 1–37

54. Liu, C. and Jiang, D. (2017b). Influence analysis of nonlinear stress–strain behavior in pulverizing wheel of fan mill. Journal of Failure Analysis and Prevention, 17(3):571–580

55. Liu, C., Gong, Y., Laflamme, S., Phares, B., and Sarkar, S. (2017b). Bridge damage detection using spatiotemporal patterns extracted from dense sensor network. Measurement Science and Technology, 28(1):014011

56. Liu, C. and Jiang, D. (2017a). Dynamics of slant cracked rotor for steam turbine generator system. Journal of Engineering for Gas Turbines and Power, 139(6):062502

57. Xie, X., Zhang, C., Liu, H., Liu, C., Jiang, D., and Zhou, B. (2016). Continuous-mass-model-based mechanical and electrical co-simulation of SSR and its application to a practical shaft failure event. IEEE Transactions on Power Systems, 31(6):5172–5180

58. Jiang, D., Liu, C., Yang, W., and Kang, W. (2015). Progress and trend on prognostics and health management (PHM) of heavy-duty gas turbine. Journal of Engineering for Thermal Energy and Power (in Chinese), 30(2):173–179

59. Liu, C., Jiang, D., and Chu, F. (2015a). Influence of alternating loads on nonlinear vibration characteristics of cracked blade in rotor system. Journal of Sound and Vibration, 353:205–219

60. Liu, C. and Jiang, D. (2014). Crack modeling of rotating blades with cracked hexahedral finite element method. Mechanical Systems and Signal Processing, 46(2):406–423

61. Liu, C., Jiang, D., and Chen, J. (2014a). Coupled torsional vibration and fatigue damage of turbine generator due to grid disturbance. Journal of Engineering for Gas Turbines and Power, 136(6):062501

62. Liu, C., Jiang, D., and Yang, W. (2014e). Global geometric similarity scheme for feature selection in fault diagnosis. Expert Systems with Applications, 41(8):3585–3595

63. Liu, C., Jiang, D., Chu, F., and Chen, J. (2014d). Crack cause analysis of pulverizing wheel in fan mill of 600 MW steam turbine unit. Engineering Failure Analysis, 42:60–73

64. Liu, C., Jiang, D., Chen, J., and Chen, J. (2012). Torsional vibration and fatigue evaluation in repairing the worn shafting of the steam turbine. Engineering Failure Analysis, 26:1–11

65. An, X., Jiang, D., Zhao, M., and Liu, C. (2012). Short-term prediction of wind power using EMD and chaotic theory. Communications in Nonlinear Science and Numerical Simulation, 17(2):1036–1042

66. An, X., Jiang, D., Liu, C., and Zhao, M. (2011). Wind farm power prediction based on wavelet decomposition and chaotic time series. Expert Systems with Applications, 38(9):11280–11285

67. Jiang, D. and Liu, C. (2011b). Machine condition classification using deterioration feature extraction and anomaly determination. IEEE Transactions on Reliability, 60(1):41–48