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Wilson College of Textiles 
Mengmeng Zhu

Research

Overview

Our lab is dedicated to advancing the safety, reliability, operation readiness and assurance of Cyber-Physical Systems (CPS) through a multidisciplinary research approach. We focus on key areas such as energy storage systems (e.g., lithium-ion batteries), human-machine interaction, smart health technologies, and robust software development. Our work integrates theoretical and data-driven methodologies to develop innovative tools and testbeds that address the complex challenges of CPS. This includes ensuring reliable system functionalities, enhancing resilience to both accidental disruptions and intentional cyber-physical threats, and implementing advanced prognostics and health management strategies for early fault detection and mitigation. Our goal is to enable next-generation CPS that are adaptable, secure, and resilient in real-world applications.

Our current projects include

Explainable AI based Health Management of Energy Storage Systems

Stochastic Process based Reliability Analysis of Lithium-ion Batteries with Risk Considerations

Human In-the-loop Software Reliability Modeling, Algorithm, and Evaluation

Resilient Engineering System Design with Uncertainty

Publications

(+ denotes students under my supervision, * denotes corresponding author)

Journal Papers

  1. Lin, H+ & Zhu, M.* (2024). Damage-resistant CPS reliability modeling considering coupled system resistance effects. Accepted. Reliability Engineering and System Safety.
  2. Wang, R.+, Zhu, M.* & Zhang, X. (2024). Lifetime prediction and maintenance assessment of Lithium-ion batteries based on combined information of discharge voltage curves and capacity fade. Journal of Energy Storage, 81, 110376.
  3. Choi, J., Zhu, M., Kang, J. & Jeong, M. (2024). Convolutional neural network based multi-input multi-output model for multi-sensor multivariate virtual metrology in semiconductor manufacturing. Annals of Operations Research. Accepted.
  4. Hu, Y.+, Zhu, M.* & Lin, H.+ (2023). A nonlinear Wiener process degradation model with damage resistance for reliability analysis. Submitted.
  5. Wang, R+ & Zhu, M.* (2024). State-of-health prediction of Lithium-ion batteries at varying current rates based on differential voltage analysis and statistical methods. Submitted.
  6. Wang, R.+, Zhu, M.* & Choi, J. (2021). Deep Learning-based review prediction for smart health-monitoring wearable device. Submitted.
  7. Zhu, M.*(2023). Probabilistic-based approach of modeling complex engineering system resilience with application to Lithium-ion battery design. Submitted.
  8. Hu, Y.+, Wang, R.+, Zhu, M.* & Chen, K. B. (2023). Modeling human-machine interaction system reliability with multiple dependent degradation processes and situation awareness. International Journal of Reliability, Quality and Safety Engineering.  https://doi.org/10.1142/S0218539323500146.
  9. Wang, R.+, Zhu, M.*, Zhang, X. & Pham, H. (2023). Lithium-ion battery remaining useful life prediction using a two-phase degradation model with a dynamic change point. Journal of Energy Storage, 59, 106457.
  10. Wang, R.+ & Zhu, M.* (2022). Shock-loading based method for modeling dependent competing risks with degradation processes and random shocks. International Journal of Reliability, Quality and Safety Engineering, 29(03), 2250002.
  11. Zhu, M.*, Huang, X. & Pham, H. (2021). A random field environment-based multidimensional time-dependent resilience modeling of complex systems. IEEE Transactions on Computational Social Systems, doi: 10.1109/TCSS.2021.3083515.
  12. Zhu, M.* (2021). A new framework of complex system reliability with imperfect maintenance policy. Annals of Operations Research. Available online: https://doi.org/10.1007/s10479-020-03852-w.
  13. Zhu, M.* & Pham, H. (2020). A generalized multiple environmental factors software reliability model with stochastic fault detection process. Annals of Operations Research. https://doi.org/10.1007/s10479-020-03732-3.
  14. Zhu, M.* & Pham, H. (2020). An empirical study of factor identification in smart health monitoring wearable device. IEEE Transactions on Computational Social Systems, 7(2), 404-416.
  15. Zhu, M.*& Pham, H. (2019). A novel system reliability modeling of hardware, software, and interactions of hardware and software. Mathematics, 7(11), 1049.
  16. Zhu, M.*& Pham, H. (2018). A software reliability model incorporating martingale process with gamma-distributed environmental factors. Annals of Operations Research. Available online: https://doi.org/10.1007/s10479-018-2951-7.
  17. Zhu, M.*& Pham, H. (2018). A two-phase software reliability modeling involving with software fault dependency and imperfect fault removal. Computer Languages, Systems & Structures, 53, 27-42.
  18. Zhu, M. & Pham, H. (2018). A multi-release software reliability modeling for open source software incorporating dependent fault detection process. Annals of Operations Research, 269(1-2), 773 – 790.
  19. Zhu, M. & Pham, H.(2017). Environmental factors analysis and comparison affecting software reliability in development of multi-release software. Journal of Systems and Software, 132, 72-84.
  20. Zhu, M., Zhang, X. & Pham, H.(2015). A comparison analysis of environmental factors affecting software reliability. Journal of Systems and Software, 109, 150-160.
  21. Zhu, M. & Pham, H. (2016). A software reliability model with time-dependent fault detection and fault removal. Vietnam Journal of Computer Science, 3(2), 71-79.
  22. Fan, S., Ma, Y., Zhu, X., Xiong, R. & Zhu, M. (2008). Entropy-based performance evaluation of operation characteristic curve. Industrial Engineering and Management, China, 13(4), 99-101.

Book Chapters

  1. Zhu, M.* & Pham, H. (2022). Software reliability modeling and methods: A state of the art review. Optimization Problems in Software Reliability, 1-29.
  2. Hu, Y.+, & Zhu, M.* (2023). System reliability models with random shocks and uncertainty: A state-of-the-art review. Predictive Analytics in System Reliability, 19-38.

Conference Papers

  1. Lin, H+ & Zhu, M.* (2024). Function based damage resistance modeling for multi-phase CPS system reliability. Proceedings of the 29th ISSAT International Conference on Reliability and Quality in Design, Miami, FL, 200-204.
  2. Wang, R+ & Zhu, M.* (2023). Experimental analysis of Lithium-ion battery degradation with varying discharge rates. Proceedings of the 28th ISSAT International Conference on Reliability and Quality in Design, San Francisco, CA, 37-41.
  3. Hu, Y.+, & Zhu, M.* (2022). Reliability modeling for shock-degradation-dependent process considering damage resistance. Proceedings of the 27thISSAT International Conference on Reliability and Quality in Design (Virtual), 281-284.
  4. Zhu, M.* (2021). Probabilistic-based general framework of modeling engineering system resilience. Proceedings of the 26th ISSAT International Conference on Reliability and Quality in Design (Virtual), 284-288.
  5. Zhu, M.* & Pham, H. (2019). System reliability modeling of hardware, software, and interactions of hardware and software. Proceedings of the 25th ISSAT International Conference on Reliability and Quality in Design, Las Vegas, NV, 73-77.
  6. Zhu, M.* & Pham, H. (2018). A generalized martingale-based software reliability model considering multiple environmental factors. Proceedings of the 24th ISSAT International Conference on Reliability and Quality in Design, Toronto, Canada, 36-40.
  7. Zhu, M. & Pham, H. (2017). Software reliability modeling with considerations of two-phase imperfect debugging and fault removal. Proceedings of the 23rd ISSAT International Conference on Reliability and Quality in Design, Chicago, IL, 69-73.
  8. Zhu, M. & Pham, H. (2016). Multi-release software reliability modeling and analysis incorporating dependent software fault detection process. Proceedings of the 22nd ISSAT International Conference on Reliability and Quality in Design, Los Angeles, CA, 122-126.