Xiaodong Jia

Xiaodong Jia

Research Assistant Professor

Rhodes Hall

536

CEAS - Mechanical Eng - 0072

Education

Ph.D: University of Cincinnati Cincinnati, US, 2018 (MECH: Industrial Big Data, Prognostic and Health Management)

M.S.: Shanghai Jiaotong University Shanghai, China, 2014 (MECH: Turbomachinery)

B.S.: Central South University Changsha, China, 2008 (MECH)

Research and Practice Interests

- Smart Manufacturing and Maintenance
- Prognostics and Health Management (PHM)
- Data-driven Modeling and Intelligent Systems
- Advanced Process Control (APC)
- Machine Learning and Data Mining

Positions and Work Experience

07-2008 -01-2011 Project/Quality Management, Siemens, Shanghai, China

01-2019 -12-2019 Post-Doc Research Fellow, Center for Intelligent Maintenance Systems, ME Department, University of Cincinnati, Cincinnati, US

05-2017 -08-2017 Summer Intern, General Motors, Warren, MI, US

Research Support

Grant: #2020 Industry Sponsored Research Agreement Investigators:Jia, Xiaodong 04-01-2020 -09-30-2020 Hitachi High-Technologies Corporation Phase IV Recipe Optimization Based on Process Sensor Variable Profiles and Etch Rate Role:PI $54,578.00 Awarded Level:Foreign Industry

Grant: #2020 Industry Sponsored Research Agreement Investigators:Jia, Xiaodong; Kim, J. 03-01-2020 -05-31-2020 AU Optronics Corporation PHM Methods for Robot Arms and Pumps Role:Collaborator $31,619.00 Active Level:Foreign Industry

Grant: #2020 Industry Sponsored Program Agreement Investigators:Jia, Xiaodong 04-01-2020 -12-31-2020 Mitsubishi Electric Corporation Mitsubishi Electric Research 2020 Role:PI $91,801.00 Awarded Level:Industry

Grant: #2020 Training Agreement Investigators:Jia, Xiaodong 08-01-2020 -10-31-2020 Taiwan Semiconductor Manufacturing Company Ltd. TSMC Training Role:PI $26,324.00 Awarded Level:Foreign Industry

Grant: #2020 Teaming Agreement Investigators:Jia, Xiaodong 08-01-2020 -10-31-2020 Taiwan Semiconductor Manufacturing Company Ltd. TSMC Training Role:PI $.00 Active Level:Foreign Industry

Grant: #2020 Industry Sponsored Research Agreement Investigators:Jia, Xiaodong 11-01-2020 -03-31-2021 Hitachi High-Technologies Corporation Phase V: Chamber Matching based on Etching Digital Twin Model Role:PI $51,867.00 Awarded Level:Foreign Industry

Grant: #JDA-358 Investigators:Jia, Xiaodong 01-01-2021 -10-31-2021 Applied Materials, Inc. Multivariate Simulation Dataset Generation for Fault Detection with Semi-Automated Feature Extraction and Semi-supervised Limits Setting Applied to Semiconductor Manufacturing Processes Role:PI $37,942.00 Awarded Level:Industry

Grant: #2020 Industry Sponsored Research Agreement Investigators:Jia, Xiaodong 05-01-2020 -12-31-2021 Winbond Electronics Corporation Winbond Electrostatic (ESC) Chuck Remaining Useful Life Role:PI $99,280.00 Active Level:Foreign Industry

Grant: #2020 Industry Agreement Investigators:Jia, Xiaodong 11-01-2020 -04-30-2021 United Microelectronics Corporation Applications of Machine Learning Operation Techniques Role:PI 49665.00 Hold Level:Industry

Grant: #2021 Industry Sponsored Research Agreement Investigators:Jia, Xiaodong 04-01-2021 -09-30-2021 Power Solutions International Power Solutions International Inc. (Phase I) Role:PI 61051.00 Hold Level:Industry

04-01-2021 -09-30-2021 Hitachi High-Technologies Corporation Phase 2021-1: Development of digital twin model for Hitachi plasma etching tool and calibration of tool performance shift Role:PI 0.00 Hold Level:Industry

04-01-2021 -09-30-2021 Hitachi High-Technologies Corporation Phase 2021-1: Development of digital twin model for Hitachi plasma etching tool and calibration of tool performance shift Role:PI 0.00 Hold Level:Industry

07-01-2021 -03-31-2022 Mitsubishi Electric Corporation Health Assessment & Fault Detection for Industrial Robots Role:PI 88332.00 Hold Level:Industry

07-01-2021 -03-31-2022 Mitsubishi Electric Corporation Health Assessment & Fault Detection for Industrial Robots Role:PI 88332.00 Hold Level:Industry

Grant: #Full Mbrship Agreement-UMC Investigators:Jia, Xiaodong 09-23-2021 -09-22-2022 United Microelectronics Corporation IMS Center Membership Agreement for United Microelectronics Corporation (UMC). Role:PI 40000.00 Hold Level:Industry

Grant: #Full Mbrship Agreement-TSMC Investigators:Jia, Xiaodong 09-23-2021 -09-22-2022 Taiwan Semiconductor Manufacturing Company Ltd. IMS Center Membership for Taiwan Semiconductor Manufacturing Corporation (TSMC) Role:PI 40000.00 Hold Level:Industry

Publications

Peer Reviewed Publications

Dai, Honghao; Jia, Xiaodong; Pahren, Laura; Lee, Jay; Foreman, Brandon (2020. ) Intracranial Pressure Monitoring Signals After Traumatic Brain Injury: A Narrative Overview and Conceptual Data Science Framework.Frontiers in neurology, , 11 ,959 More Information

Jia, X.; Jin, C.; Buzza, M.; Wang, W.; Lee, J. (2016. ) Wind turbine performance degradation assessment based on a novel similarity metric for machine performance curves .Renewable Energy, , 99 (7 ) ,1191-1201

Cai, Haoshu; Jia, Xiaodong; Feng, Jianshe; Li, Wenzhe; Pahren, Laura; Lee, Jay (2020. ) A similarity based methodology for machine prognostics by using kernel two sample test.ISA transactions, , 103 ,112-121 More Information

Jia, X.; Zhao, M.; Buzza, M.; Di, Y.; Lee, J. (2017. ) A geometrical investigation on the generalized lp/lq norm for blind deconvolution .Signal Processing, , 134 (7 ) ,63-69

Zhao, M.; Jia, X. (2017. ) A novel strategy for signal denoising using reweighted SVD and its applications to weak fault feature enhancement of rotating machinery .Mechanical Systems and Signal Processing, , 94 (7 ) ,129-147

Jia, X.; Zhao, M.; Di, Y.; Jin, C.; Lee, J. (2017. ) Investigation on the kurtosis filter and the derivation of convolutional sparse filter for impulsive signature enhancement .Journal of Sound and Vibration, , 386 (7 ) ,433-448

Jia, X.; Jin, C.; Buzza, M.; Di, Y.; Siegel, D.; Lee, J. (2018. ) A deviation based assessment methodology for multiple machine health patterns classification and fault detection .Mechanical Systems and Signal Processing, , 99 (7 ) ,244-261

Jia, X.; Di, Y.; Feng, J.; Yang, Q.; Dai, H.; Lee, J. (2018. ) Adaptive virtual metrology for semiconductor chemical mechanical planarization process using GMDH-type polynomial neural networks .Journal of Process Control, , 62 (7 ) ,44-54

Jia, X.; Zhao, M.; Di, Y.; Yang, Q.; Lee, J. (2018. ) Assessment of Data Suitability for Machine Prognosis Using Maximum Mean Discrepancy .IEEE Transactions on Industrial Electronics, , 65 (7 ) ,5872-5881

Zhao, M.; Jia, X.; Lin, J.; Lei, Y.; Lee, J. (2018. ) Instantaneous speed jitter detection via encoder signal and its application for the diagnosis of planetary gearbox .Mechanical Systems and Signal Processing, , 98 (4 ) ,16-31

Li, P.; Jia, X.; Feng, J.; Davari, H.; Qiao, G.; Hwang, Y.; Lee, J. (2018. ) Prognosability study of ball screw degradation using systematic methodology .Mechanical Systems and Signal Processing, , 109 (4 ) ,45-57

Jia, X.; Huang, B.; Feng, J.; Cai, H.; Lee, J. (2018. ) Review of PHM data competitions from 2008 to 2017: Methodologies and analytics .Mechanical Systems and Signal Processing, , 99 (7 ) ,244-261

Jia, X.; Zhao, M.; Di, Y.; Li, P.; Lee, J. (2018. ) Sparse filtering with the generalized lp/lq norm and its applications to the condition monitoring of rotating machinery .Mechanical Systems and Signal Processing, , 102 (7 ) ,198-213

Cai, H.; Jia, X.; Feng, J.; Yang, Q.; Hsu, Y. M.; Chen, Y.; Lee, J. (2019. ) A combined filtering strategy for short term and long term wind speed prediction with improved accuracy .Renewable Energy, , 136 (4 ) ,1082-1090

Jia, X.; Duan, S.; Lee, C.; Radecki, P.; Lee, J. (2019. ) A methodology for the early diagnosis of vehicle torque converter clutch degradation .IEEE transactions on semiconductor manufacturing, , 2019-August (2 ) ,529-534

Li, P.; Jia, X.; Sumiya, M.; Kamaji, Y.; Ishiguro, M.; Pahren, L.; Lee, J. (2019. ) A novel method for deposit accumulation assessment in dry etching chamber .IEEE transactions on semiconductor manufacturing, , 32 (2 ) ,183-189

Jia, X.; Cai, H.; Hsu, Y.; Li, W.; Feng, J.; Lee, J. (2019. ) A novel similarity-based method for remaining useful life prediction using kernel two sample test .IEEE transactions on semiconductor manufacturing, , 11 (2 ) ,529-534

Feng, J.; Jia, X.; Zhu, F.; Yang, Q.; Pan, Y.; Lee, J. (2019. ) An intelligent system for offshore wind farm maintenance scheduling optimization considering turbine production loss .Journal of Intelligent and Fuzzy Systems, , 37 (5 ) ,6911-6923

Feng, J.; Jia, X.; Zhu, F.; Moyne, J.; Iskandar, J.; Lee, J. (2019. ) An online virtual metrology model with sample selection for the tracking of dynamic manufacturing processes with slow drift .IEEE transactions on semiconductor manufacturing, , 32 (4 ) ,574-582

Azamfar, M.; Jia, X.; Pandhare, V.; Singh, J.; Davari, H.; Lee, J. (2019. ) Detection and diagnosis of bottle capping failures based on motor current signature analysis .Procedia Manufacturing, , 34 (4 ) ,840-846

Pan, Y.; Hong, R.; Chen, J.; Singh, J.; Jia, X. (2019. ) Performance degradation assessment of a wind turbine gearbox based on multi-sensor data fusion .Mechanism and Machine Theory, , 137 ,509-526

Li, P.; Jia, X.; Feng, J.; Zhu, F.; Miller, M.; Chen, L. Y.; Lee, J. (2020. ) A novel scalable method for machine degradation assessment using deep convolutional neural network .Measurement: Journal of the International Measurement Confederation, , 151 ,235-247

Cai, H.; Jia, X.; Feng, J.; Li, W.; Pahren, L.; Lee, J. (2020. ) A similarity based methodology for machine prognostics by using kernel two sample test .Isa Transactions, , 103 ,112-121

Cai, H.; Jia, X.; Feng, J.; Li, W.; Hsu, Y. M.; Lee, J. (2020. ) Gaussian Process Regression for numerical wind speed prediction enhancement .Renewable Energy, , 146 ,2112-2123

Li, X.; Jia, X. D.; Zhang, W.; Ma, H.; Luo, Z.; Li, X. (2020. ) Intelligent cross-machine fault diagnosis approach with deep auto-encoder and domain adaptation .Neurocomputing, , 383 ,235-247

Dai, H.; Jia, X.; Pahren, L.; Lee, J.; Foreman, B. (2020. ) Intracranial Pressure Monitoring Signals After Traumatic Brain Injury: A Narrative Overview and Conceptual Data Science Framework .Frontiers in Neurology, , 11 ,235-247

Zhang, W.; Li, X.; Jia, X. D.; Ma, H.; Luo, Z.; Li, X. (2020. ) Machinery fault diagnosis with imbalanced data using deep generative adversarial networks .Measurement: Journal of the International Measurement Confederation, , 152 ,242-253

Li, X.; Jia, X.; Yang, Q.; Lee, J. (2020. ) Quality analysis in metal additive manufacturing with deep learning .Journal of Intelligent Manufacturing, , 383 ,235-247

Li, W.; Jia, X.; Li, X.; Wang, Y.; Lee, J. (2021. ) A Markov model for short term wind speed prediction by integrating the wind acceleration information .Renewable Energy, , 164 ,242-253

Cai H.; Jia X.; Feng J.; Yang Q.; Li W.; Li F.; Lee J. (11-01-2021. ) A unified Bayesian filtering framework for multi-horizon wind speed prediction with improved accurac.Renewable Energy, , 178 ,709-719 More Information

Wang C.; Dani J.; Li X.; Jia X.; Wang B. (04-26-2021. ) Adaptive Fingerprinting: Website Fingerprinting over Few Encrypted Traffic.CODASPY 2021 - Proceedings of the 11th ACM Conference on Data and Application Security and Privacy, , 149-160 More Information

Zhu F.; Jia X.; Miller M.; Li X.; Li F.; Wang Y.; Lee J. (02-01-2021. ) Methodology for Important Sensor Screening for Fault Detection and Classification in Semiconductor M.IEEE Transactions on Semiconductor Manufacturing, , 34 (1 ) ,65-73 More Information

Pandhare V.; Li X.; Miller M.; Jia X.; Lee J. (01-01-2021. ) Intelligent Diagnostics for Ball Screw Fault through Indirect Sensing Using Deep Domain Adaptation.IEEE Transactions on Instrumentation and Measurement, , 70 , More Information

Jia X.; Li W.; Wang W.; Li X.; Lee J. (11-03-2020. ) Development of multivariate failure threshold with quantifiable operation risks in machine prognosti.Proceedings of the Annual Conference of the Prognostics and Health Management Society, PHM, , 12 (1 ) , More Information

Li X.; Jia X.; Wang Y.; Yang S.; Zhao H.; Lee J. (10-01-2020. ) Industrial Remaining Useful Life Prediction by Partial Observation Using Deep Learning with Supervis.IEEE/ASME Transactions on Mechatronics, , 25 (5 ) ,2241-2251 More Information

Peres R.S.; Jia X.; Lee J.; Sun K.; Colombo A.W.; Barata J. (01-01-2020. ) Industrial Artificial Intelligence in Industry 4.0 -Systematic Review, Challenges and Outlook.IEEE Access, , More Information

Wang Y.; Jia X.; Li X.; Yang S.; Zhao H.; Lee J. (01-01-2020. ) A machine vision based monitoring system for the LCD panel cutting wheel degradation.48 ,49-53 More Information

Yang S.; Li X.; Jia X.; Wang Y.; Zhao H.; Lee J. (01-01-2020. ) Deep learning-based intelligent defect detection of cutting wheels with industrial images in manufac.48 ,902-907 More Information

Honors and Awards

1st Place in the 1st Foxconn industrial AI Data Challenge 2020

3rd Place in Aramis Challenge 2020: Prognostics and Health Management in Evolving Environments

1st Place in the PHM Data Challenge 2016, Hosted by the PHM society