Russell, M. B. (2023). Generalizable and adaptable data-driven methods for overcoming barriers to practical industrial condition monitoring. [Doctoral dissertation, University of Kentucky] UKnowledge. https://uknowledge.uky.edu/ece_etds/190/

Journal Articles

  1. Russell, M., & Wang, P. (2023). Maximizing model generalization for machine condition monitoring with self-supervised learning and federated learning. Journal of Manufacturing Systems 71, 274-285. [Preprint, Publisher]

  2. Ippili, S., Russell, M. B., Wang, P., & Herrin, D. W. (2023). Deep learning-based mechanical fault detection and diagnosis of electric motors using directional characteristics of acoustic signals. Noise Control Engineering Journal 71(5), 384-389. [Publisher]

  3. Russell, M., Wang, P., Liu, S., & Jawahir, I. S. (2023). Mixed-up experience replay for adaptive online condition monitoring. IEEE Transactions on Industrial Electronics 71, 1979-1986. [Publisher, PDF]

  4. Russell, M., Kershaw, J., Xia, Y., Lv, T., Li, Y., Ghassemi-Armaki, H., Carlson, B. E., & Wang, P. (2023). Comparison and explanation of data-driven modeling for weld quality prediction in resistance spot welding. Journal of Intelligent Manufacturing 35, 1305-1319. [Publisher, Read-Only]

  5. Wang, P., Kershaw, J., Russell, M., Zhang, J., Zhang, Y., & Gao, R. X. (2022). Data-driven process characterization and adaptive control in robotic arc welding. CIRP Annals 71(1), 45–48. [Publisher, PDF]

  6. Russell, M., & Wang, P. (2022). Physics-informed deep learning for signal compression and reconstruction of big data in industrial condition monitoring. Mechanical Systems and Signal Processing 168, 108709. [Publisher, PDF]

  7. Russell, M. B., King, E. M., Parrish, C. A., & Wang, P. (2021). Stochastic modeling for tracking and prediction of gradual and transient battery performance degradation. Journal of Manufacturing Systems 59, 663–674. [Publisher, PDF]

  8. Russell, M., & Straub, J. (2017). Characterization of command software for an autonomous attitude determination and control system for spacecraft. International Journal of Computers and Applications 39(4), 198–209. [Publisher]

  9. Hamlet, C., Straub, J., Russell, M., & Kerlin, S. (2017). An incremental and approximate local outlier probability algorithm for intrusion detection and its evaluation. Journal of Cybersecurity Technology 1(2), 75–87. [Publisher]

Conference Papers

  1. Russell, M., & Wang, P. (2023). Normalizing flows for intelligent manufacturing. International Manufacturing Science & Engineering Conference (MSEC) 2023. [Publisher]

  2. Ippili, S. R., Russell, M., Wang, P., & Herrin, D. W. (2023, May 15-18). Deep learning based mechanical fault detection and diagnosis of electric motors using directional characteristics of acoustic signals [Paper presentation]. 2023 INCE-USA NOISE-CON, Grand Rapids, MI. [Publisher]

  3. Russell, M., & Wang, P. (2022). Improved representations for continual learning of novel motor health conditions through few-shot prototypical networks. 2022 IEEE 18th Conference on Automation Science and Engineering (CASE), 1551–1556. [Publisher, PDF]

  4. Wang, P., Russell, M., Kershaw, J., Xia, Y., Lv, T., Li, T., Ghassemi-Armaki, H., & Carlson, B. E. (2022). Interpretable data-driven prediction of resistance spot weld quality. 2022 International Symposium on Flexible Automation (ISFA). [PDF]

  5. Russell, M., Hong, P., Blakely, L., Kirkham, M., Enyoghasi, C., Wang, P., & Badurdeen, F. (2021). Smartphone app design for product use sustainability evaluation. EcoDesign 2021 International Symposium. [Publisher]

  6. Russell, M., & Wang, P. (2020). Domain adversarial transfer learning for generalized tool wear prediction. Annual Conference of the PHM Society 12, 1–8. [PDF]

  7. Russell, M., & Wang, P. (2020). Transferable deep learning for in-situ tool wear diagnosis. 2021 International Symposium on Flexible Automation (ISFA). [Publisher]