Skip to main content
Wilson College of Textiles 
Mengmeng Zhu

Research

Overview

Our lab focuses on system design and safety assurance of Cyber-Physical Systems (CPS) using approaches across multiple disciplines with applications to software development, energy storage systems (Lithium-ion batteries), human-machine interaction, and smart health. We develop theoretical and data-driven methods and testbeds for complex system and software reliability ensuring desirable system functionalities, system resilience analysis against unintentional and intentional disruptions, and system prognostics and health management for early fault detection and undesirable event mitigation.

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. 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.
  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. 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.
  4. Hu, Y.+, Zhu, M.* & Lin, H.+ (2023). A nonlinear Wiener process degradation model with damage resistance for reliability analysis. Submitted.
  5. Lin, H+ & Zhu, M.* (2024). Damage resistance system reliability modeling considering coupled system resistance and system health. To be submitted in Feb 2024.
  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. 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.
  2. 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.
  3. 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.
  4. 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.
  5. 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.
  6. 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.
  7. 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.