Staff Profile
Dr Zepeng Liu
Lecturer in Electrification
- Email: zepeng.liu@ncl.ac.uk
- Personal Website: https://www.researchgate.net/profile/Zepeng-Liu-5
- Address: Merz Court, E3.14
School of Engineering
Newcastle University
Newcastle upon Tyne
NE1 7RU, UK
Biography
Dr. Zepeng Liu is a lecturer in Electrification at Newcastle University since 2023. He received the B.Eng. degree in Electrical Engineering and Electronics from the University of Liverpool, Liverpool, U.K., in 2015, the M.S. degree in Power Systems Engineering from University College London, London, U.K., in 2016, and the Ph.D. degree in Electrical and Electronic Engineering from the University of Manchester, Manchester, U.K, in 2021. From 2021 to 2023, he was a Research Associate with the Department of Automatic Control and Systems Engineering, University of Sheffield, Sheffield, U.K.
Dr. Zepeng Liu awarded the Chinese Government Award for Outstanding Self-financed Students Abroad in 2021. Only 500 PhD students receive this Award per year all over the world. He has published numbers of high quality journals including IEEE Trans Industr Electron, IEEE Trans Industr Inform, IEEE Trans Instrum Meas, IEEE Trans Syst Man Cybern, etc. His research interests include machine learning, data-driven modelling, system identification, frequency analysis, time-frequency representation, sparse representation and nonlinear filtering. The application areas include condition monitoring and fault diagnosis for wind turbine components and systems, digital manufacturing, and structural health monitoring.
Qualifications
- PhD., University of Manchester, United Kindom, 2017-2020
- MSc., University College London, United Kindom, 2015-2016
- BEng., University of Liverpool, United Kindom, 2011-2015
Working Experience
- Research Associate, University of Sheffield, 2020-2023
- Research Associate, University of Manchester, 2020
Membership
- Associate Fellowship of the Higher Education Academy
- Member of the IEEE
Languages
English, Chinese
Links
Google Scholar: Click here.
ResearchGate: Click here.
LinkedIn: Click here.
Last updated: 18 Jun 2024
Dr Liu is a member of the Institute of Electrification and Sustainable Advanced Manufacturing (IESAM) and Electrical Power research group and his research publications and profile can be viewed on Google Scholar, and ResearchGate.
Opportunities
Dr Liu is looking for enthusiastic and highly motivated candidates to conduct PhD research. If you are interested in the areas listed in the Research Interests below, please feel free to contact zepeng.liu@newcastle.ac.uk for discussions.
Research Interests
System theory and frequency analysis:
· AI-based digital twin
· Machine and statistical learning, Neural networks
· Sparse representation and nonlinear filtering
· Nonlinear system modelling and analysis in the frequency domain
Modelling and analysis for complex systems:
· Condition monitoring and fault detection
· Non-Destructive Testing
· Structural Health Monitoring
· Cyber-physical systems
· Digital manufacturing
· Smart structures and systems
Smart devices
· Hardware and software design
Project Experience
· Institute of Electrification and Sustainable Advanced Manufacturing (IESAM) -Building Talent for Growth of North East PEMD Supply Chain (Inovative UK, £999,980, 2023 - present)
· AI-Enabled Condition Monitoring, Fault Diagnosis, and Resilient Control of Wind Energy Systems (£ 14,000, 2022-2023)
· Development and demonstration of methods and tools for large scale wind turbine pitch bearing condition assessment (DemoBearing) (EP/S017224/1, £169,123, 2019- 2021)
· Autonomous method for detecting cutting tool and machine tool anomalies in machining (EP/T024291/1, £1,033,385, 2020 - 2023)
Postgraduate Teaching
- EEE8097 - Individual Project (Project Supervisor)
Degree Apprenticeship
- ENG1501 Engineering Math I (Module Leader)
Continuing Professional Development
- Machinary Fault Diagnosis and Prognosis (Module Leader)
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Articles
- Liu Z, Lang Z, Gui Y, Zhu Y, Laalej H, Curtis D. Vibration Signal-based Tool Condition Monitoring Using Regularized Sensor Data Modelling and Model Frequency Analysis. IEEE Transactions on Instrumentation and Measurement 2024, 73, 3505313.
- Zhang B, Jin X, Liang W, Chen X, Li Z, Panoutsos G, Liu Z, Tang Z. TabNet: Locally Interpretable Estimation and Prediction for Advanced Proton Exchange Membrane Fuel Cell Health Management. Electronics 2024, 13(7), 1358.
- Zhao Y, Liu Z, Zhang H, Han Q, Liu Y, Wang X. On-line condition monitoring for rotor systems based on nonlinear data-driven modelling and model frequency analysis. Nonlinear Dynamics 2024, 112, 5229-5245.
- Gui Y, Tang X, Liu Z. Local regularization assisted split augmented Lagrangian shrinkage algorithm for feature selection in condition monitoring. Control Engineering Practice 2024, 147, 105923.
- Zhu Y-P, Liu Z, Zhang W, Zhang B. Fast evaluation of generalized associated linear equations (GALEs) for nonlinear systems characterization and compensation. Journal of the Franklin Institute 2024, 361(2), 944-957.
- Liu Z, Lang ZQ, Gui Y, Zhu YP, Laalej H. Digital twin-based anomaly detection for real-time tool condition monitoring in machining. Journal of Manufacturing Systems 2024, 75, 163-173.
- Liu Z. A Nonlinear AutoRegressive-based Noise Cancellation Method for Real-time Fault Diagnosis of Rolling Bearings. IEEE Transactions on Instrumentation and Measurement 2024, 73, 3509212.
- Gui Z, Lang ZQ, Liu Z, Zhu Y, Laalej H, Curtis D. Unsupervised detection of tool breakage: A novel approach based on time and sensor domain data analysis. IEEE Transactions on Instrumentation and Measurement 2023, 72, 3524813.
- Gui Y, Lang Z, Liu Z, Laalej H. Tool Condition Monitoring Based on Nonlinear Output Frequency Response Functions and Multivariate Control Chart. Journal of Dynamics Monitoring and Diagnostics 2023, 2(4).
- Liu Z, Lang ZQ, Zhu YP, Gui Y, Laalej H, Stammers J. Sensor Data Modeling and Model Frequency Analysis for Detecting Cutting Tool Anomalies in Machining. IEEE Transactions on Systems, Man, and Cybernetics: Systems 2023, 53(5), 2641-2653.
- Qin X, Huang W, Wang X, Tang Z, Liu Z. Real-Time Remaining Useful Life Prediction of Cutting Tools Using Sparse Augmented Lagrangian Analysis and Gaussian Process Regression. Sensors 2023, 23(1), 413.
- Zhao Y, Liu Z, Lin J, Han Q, Liu Y. A novel nonlinear spectrum estimation method and its application in on-line condition assessment of bearing-rotor system. Measurement 2023, 221, 113497.
- Zhang C, Liu Z, Zhang L. Wind turbine blade bearing fault detection with Bayesian and Adaptive Kalman Augmented Lagrangian Algorithm. Renewable Energy 2022, 199, 1016-1023.
- Wang X, Liu Z, Zhang L, Health W. Wavelet Package Energy Transmissibility Function and Its Application to Wind Turbine Blade Fault Detection. IEEE Transactions on Industrial Electronics 2022, 69(12), 13597-13606.
- Zhu YP, Zhao Y, Lang Z, Liu Z, Liu Y. Online rotor systems condition monitoring using nonlinear output frequency response functions under harmonic excitations. IEEE Transactions on Industrial Informatics 2022, 18(10), 6798-6808.
- Liu Z, Yang B, Wang X, Zhang L. Acoustic Emission Analysis for Wind Turbine Blade Bearing Fault Detection Under Time-Varying Low-Speed and Heavy Blade Load Conditions. IEEE Transactions on Industry Applications 2021, 57(3), 2791-2800.
- Liu, Z, Tang, X, Wang, X, Mugica, J, Zhang, L. Wind Turbine Blade Bearing Fault Diagnosis Under Fluctuating Speed Operations via Bayesian Augmented Lagrangian Analysis. 2020.
- Liu Z, Zhang L, Carrasco J. Vibration analysis for large-scale wind turbine blade bearing fault detection with an empirical wavelet thresholding method. Renewable Energy 2020.
- Liu Z, Wang X, Zhang L. Fault Diagnosis of Industrial Wind Turbine Blade Bearing using Acoustic Emission Analysis. IEEE Transactions on Instrumentation and Measurement 2020.
- Liu Z, Zhang L. A review of failure modes, condition monitoring and fault diagnosis methods for large-scale wind turbine bearings. Measurement 2020.
- Liu Z, Zhang L. Naturally damaged wind turbine blade bearing fault detection using novel iterative nonlinear filter and morphological analysis. IEEE Transactions on Industrial Electronics 2019.
- Ma S, Zhang Y, Liu Z, Dai X, Huang J, Fan P, Xie B, Jiang S, Zhang H. Preparation and enhanced electric-field-induced strain of textured 91BNT–6BT–3KNN lead-free piezoceramics by TGG method. Journal of Materials Science: Materials in Electronics 2016.
- Fan P, Zhang Y, Huang J, Hu W, Huang D, Liu Z, Xie B, Li X. Constrained sintering and electrical properties of BNT–BKT lead-free piezoceramic thick films. Ceramics International 2016.
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Authored Book
- Liu Z. Wind Turbine Blade Bearing Fault Detection and Diagnosis Using Vibration and Acoustic Emission Signal Analysis [PhD thesis]. Manchester: Faculty of Science and Engineering, University of Manchester, 2021.
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Conference Proceedings (inc. Abstracts)
- Gui Y, Lang ZQ, Liu Z, Zhu Y, Laalej H. Time-sensor domain data decomposition and analysis for fault diagnosis of cutting tools. In: 2022 17th International Conference on Control, Automation, Robotics and Vision (ICARCV). 2022, Singapore: Institute of Electrical and Electronics Engineers.
- Wang X, Liu Z, Lu E. Remaining Useful Life Estimation of Cutting Tools Using Bayesian Augmented Lagrangian Algorithm. In: 2022 IEEE 31st International Symposium on Industrial Electronics (ISIE). 2022, Anchorage, AK, USA: Institute of Electrical and Electronics Engineers.
- Liu Z, Zhang L. Acoustic Emission Analysis for Wind Turbine Blade Bearing Fault Detection Using Sparse Augmented Lagrangian Algorithm. In: 2020 IEEE Applied Power Electronics Conference and Exposition (APEC). 2020.