Background

Yu (Eve) Zhang is a Senior Lecturer in Digital Engineering in the Department of Aeronautical and Automotive Engineering at Loughborough University from September 2019. Before joining in the AAE department at LU, she held various academic positions in the School of Engineering, University of Lincoln from 2011 to 2019. Her main research activities include condition monitoring, fault diagnosis and the development of data analysis and machine learning algorithms for industrial applications.

Qualifications

• PhD, Department of Civil Engineering, University of Nottingham, Nottingham, UK, 2011
• MSc, Department of Civil Engineering, University of Nottingham, Nottingham, UK, 2005
• BEng, School of Aerospace Engineering and Applied Mechanics, Tongji University, Shanghai, China, 2004

Key awards

• Patent, Analysis Method for a Gas Turbine (WO2019166214), 2019
• EPSRC New Investigator Award, 2018
• RAEng Academic Champion, 2016
• Balfour Beatty Civil Engineering Prize, Nottingham, 2005
• Gammon Skanska Scholarship Award, Hong Kong, 2002

Current teaching responsibilities

• TTA001 Engineering Mechanics
• TTB208 Structural Design Project
• TTC201 Machine Intelligence
• TTD003 Automotive Group Project
• Supervision of BEng, MEng and MSc Final Year Projects

Administrative responsibilities

• DIS Placement Tutor; Personal Tutor.

Outline of main research interests

• Condition Monitoring & Fault Diagnosis of Industrial Systems (e.g., automotive engines, electric drives & gas turbines)
• Data-driven & Grey-box Modelling (e.g., batteries & fuel cells)
• Aerodynamic Design Optimisation (e.g., aeronautical & automotive)
• Data Mining, Pattern Recognition & System Identification
• Artificial Intelligence & Machine Hearing

Grants and contracts

• Propulsion System Optimisation through Prediction and Machine Learning, 2020, APC ARMD, with Jaguar Land Rover.
• Aerodynamic Design Acceleration through Machine Learning, 2019, APC via IDE, with McLaren Automotive, CI.
• Evolutionary Virtual Expert System, 2018, EPSRC New Investigator Award.
• Dynamic Modelling of Industrial Gas Turbines for Fault Diagnosis, 2016, 2017, 2018, Siemens Industrial Turbomachinery, PI.
• Remote Condition Monitoring of Power Converters and Traction Drives for Railway Applications, 2017, Dynex Semiconductor, PI.
• Intelligent Sensor System for Engine Test of Industrial Gas Turbines, 2017, KTP, with Siemens Industrial Turbomachinery, Academic Supervisor.
• Gas Turbine Performance Diagnostics and Health Management, 2016, RAEng, Academic Champion.
• Machinery Diagnostic System, 2016, Lincoln, Research and Teaching Equipment.
• Research on Industrial Networks and Equipment Fault Diagnosis, International Cooperation Platform, 2016, Guangdong China, CI.
• The Development of a Dynamic Energy Control System for Food Retailing Refrigeration Systems, 2015, 2016, Innovate UK, CI.

Martínez-García, M., Zhang, Y., Suzuki, K. , Zhang, YD. (2020) Deep Recurrent Entropy Adaptive Model for System Reliability Monitoring. IEEE Transactions on Industrial Informatics, nan(nan). https://doi.org/10.1109/TII.2020.3007152

Zhang, Y., Martínez-García, M., Kalawsky, R.S., Latimer, A. (2020) Grey-box modelling of the swirl characteristics in gas turbine combustion system. Measurement: Journal of the International Measurement Confederation, 151. https://doi.org/10.1016/j.measurement.2019.107266

Martínez-García, M., Zhang, Y., Gordon, T. (2019) Memory Pattern Identification for Feedback Tracking Control in Human–Machine Systems. Human Factors, -. https://doi.org/10.1177/0018720819881008

Martínez-García, M., Zhang, Y., Gordon, T. (2016) Modeling Lane Keeping by a Hybrid Open-Closed-Loop Pulse Control Scheme. IEEE Transactions on Industrial Informatics, 12(6). https://doi.org/10.1109/TII.2016.2619064

Maleki, S., Bingham, C., Zhang, Y. (2016) Development and Realization of Changepoint Analysis for the Detection of Emerging Faults on Industrial Systems. IEEE Transactions on Industrial Informatics, 12(3). https://doi.org/10.1109/TII.2016.2558181

Zhang, Y., Bingham, C., Yang, Z., Ling, B.W.-K., Gallimore, M. (2014) Machine fault detection by signal denoising - With application to industrial gas turbines. Measurement: Journal of the International Measurement Confederation, 58. https://doi.org/10.1016/j.measurement.2014.08.020

External Collaborators

Industry:

• Uniper
• Jaguar Land Rover
• McLaren Automotive
• Siemens Industrial Turbomachinery
• Dynex Semiconductor

Academic:

• Lancaster University
• Coventry University
• University of Leicester
• University of Nottingham
• Queen Mary University of London
• Tongji University, Shanghai, China
• South China University of Technology, Guangzhou, China