Description

Resilience engineering is a complex, hierarchal and multi disciplinary, finding applications in a diverse spectrum of engineering and social science domains, in which the performance level is degraded due to aging or externals shocks but is proactively maintained to preserve nominal performance equivalent to the fully operational state in reliability modeling. For decades, a variety of resilience metrics to quantify the resilience of systems enables retrospective analysis to assess how well a system performed under stress and inform future design and operational decisions. This project is developing predictive models to project when the system will recover to a specified level of performance and what actions to take in order to reach a target level of performance quickly and cost effectively. To promote the application of predictive models, an open-source tool is also being developed which will help researchers and organizations track and predict resilience by including different activities that contributes positively and negatively to system performance. This tool will not require any coding knowledge or mathematical background.

Publications

4. A. Jin, L. Hogewood, S. Fries, J. Lambert, L. Fiondella, A. Strelzoff, J. Boone, K. Fleckner, and I. Linkov, Resilience of Cyber-Physical Systems: Role of AI, Digital Twins and Edge Computing, Engineering Management Review, 2022. DOI: https://doi.org/10.1109/EMR.2022.3172649

3. L. Fiondella, L. Hogewood, A. Ligo, and I. Linkov, Edge Computing as an Enabler of Energy and Water System Resilience, Engineering Management Review, 2023. DOI: https://doi.org/10.1109/EMR.2023.3320876

2. K. da Mata, P. Silva, and L. Fiondella, Predicting Resilience with Neural Networks, In Proc. International Conference on Reliability and Quality in Design (ISSAT), San Francisco, CA, August 2023.

1. P. Silva, M. Hermosillo Hidalgo, I. Linkov, and L. Fiondella, Predictive Resilience Modeling, In Proc. Resilience Week, Oct 2022.

Invited Talks

Presentations and Tutorials

9. P. Silva and L. Fiondella, Predictive Resilience Modeling, National Defense Industrial Association (NDIA) Annual Systems Engineering Conference, Norfolk, VA, October 2023.

8. P. Silva, M. Hermosillo Hidalgo, M. Hotchkiss, I. Linkov, L. Dharmasena, and L. Fiondella, Predictive Resilience Modeling, American Society for Quality (ASQ) Fall Technical Conference, Raleigh, NC, October 2023.

7. P. Silva, M. Hermosillo Hidalgo, M. Hotchkiss, I. Linkov, L. Dharmasena, and L. Fiondella, Predictive Resilience Modeling, Reliability, Maintenance & Managing Risk Conference (RMMR), Minneapolis, MN, July 2023.

6. P. Silva and L. Fiondella, Predictive Resilience Modeling, Presented to WG17 Logistics, Reliability and Maintainability, 91st Military Operations Research Symposium (MORS 2023), West Point, NY, June 2023.

5. P. Silva and L. Fiondella, Predictive Resilience Modeling, American Society for Quality (ASQ) World Conference on Quality & Improvement (WCQI), Philadelphia, PA, May 2023.

4. Z. Faddi, K. da Mata, P. Silva, V. Nagaraju, S. Ghosh, and L. Fiondella, Application of Reliability and Resilience Models to Machine Learning, Defense and Aerospace Test and Analysis (DATA) Workshop, Alexandria, VA, April 2023. Outstanding poster presentation.

3. K. da Mata, P. Silva, and L. Fiondella, Neural Network for Quantitative Resilience Prediction, Defense and Aerospace Test and Analysis (DATA) Workshop, Alexandria, VA, April 2023.

2. P. Silva, A. Bajumpaa, D. Borden, C. Taylor, and L. Fiondella, Covariate Resilience Modeling, Defense and Aerospace Test and Analysis (DATA) Workshop, Alexandria, VA, April 2023.

1. L. Fiondella, A. Ligo, L. Hogewood, and I. Linkov, Edge Computing as an Enabler of Energy and Water System Resilience, In Proc. Society for Risk Analysis (SRA) Annual Meeting, December 2022.