A Quantitative Framework to Assess Tradeoffs in Alternative Models and Algorithms for Prognostics and Health Management

In this work, we consider measures based on concepts from maintenance theory and prognostics to provide a framework for quantitative assessment of degradation models and predictive algorithms over multiple maintenance intervals. This approach quantifi es the impact of prognostic distance on cost-based measures, such as average cost per cycle, utilization, safety, and availability. The proposed method is applied to lithium-ion batteries. The framework and measures are generalized to accommodate a wide range of future models, algorithms, and systems to which PHM methods are applied, such as high fi delity physics of failure models and RUL prediction based on deep learning. The work was done in collaboration with the Center for Advanced Lifecycle Engineering, University of Maryland College Park. A part of this work has been published in IEEE International conference on Prognostics and Health Management, San Francisco, CA, July 2019.

Relevant Publications

1. S Bhattacharya, L Fiondella, S Saxena, Y Xing, M Pecht, Quantifying the Impact of Prognostic Distance on Average Cost per Cycle, In Proc. IEEE International conference on Prognostics and Health Management, San Francisco, CA, July 2019.
2. S. Bhattacharya and L. Fiondella, A Fault-tolerant Classi er for Prognostics and Health Management considering Correlated Failures, In Proc. of Annual Reliability and Maintainability Symposium, Tucson, AZ, Jan 2016.