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Below is a list of projects currently being researched. For more information on a specific project, please contact the respective Project Lead.

Project List (Active)

Battery Prognostics
Project Lead: Matthew Daigle
Develop algorithms that predict end-of-charge and end-of-life for batteries (prognosis) based on rapid assessment of state-of-charge (SOC) and state-of-health (SOH) (diagnosis) coupled with anticipated environmental and load conditions. Perform subscale experiments on batteries to demonstrate prognostic capability.
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Data-Driven Prognostics
Project Lead: Kai F Goebel, Ph.D.
Develop algorithms that are not based primarily on physics-based models but that instead learn remaining life from training data. Issues tackled here are the need to deal with sparse time series data, to provide a fair uncertainty estimate, and to deal with the validation problem.
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Electronics Prognostics
Project Lead: Chetan Kulkarni
Investigate damage propagation mechanisms for critical electrical components in select avionic equipment. Specifically, understand the impact of aging due to thermo-cycling, electric overstress, and vibration on MOSFETs, IGBTs, etc., and develop models for damage propagation.
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Prognostic Data Repository
Project Lead: Kai Goebel
The Prognostics Data Repository is a collection of data sets that have been donated by various universities, agencies, or companies. The data repository focuses exclusively on prognostic data sets, i.e., data sets that can be used for development of prognostic algorithms. Mostly these are time series of data from some nominal state to a failed state. Many data sets are from real systems.
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Real Time Monitoring and Prediction of National Airspace System Safety
Project Lead: Indranil Roychoudhury
To facilitate informed decision making on the part of NAS stakeholders, we are developing a model-based methodology for the automated real-time monitoring and prediction of safety of the NAS.
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Valve Prognostics
Project Lead: Matthew Daigle
Apply physics-based modeling methodologies for prognosis of control valves. Develop model-based prognostics algorithms for state and parameter estimation and end of life prediction. Perform comprehensive simulation studies.
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Uncertainty Management
Project Lead: Shankar Sankararaman
This project focuses on quantifying the different sources of uncertainty that affect prognostics and estimating their combined effect on prognostics by calculating the probability distribution of remaining useful life of different types of engineering components and systems. Since prognostics deals with the prediction of future, it may not be possible to precisely predict the future behavior of such engineering components and it is important to quantify the confidence in such predictions by estimating the uncertainty in such predictions. The estimated uncertainty can be useful for making risk-informed decisions with regard to several activities such as life extension, fault mitigation, mission re-planning, etc.
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Project List (Completed)

Automated Contingency Management (ACM)
Project Lead: Abhinav Saxena
The Automated Contingency Management (ACM) technology aims at accommodating impending failure conditions in an automated fashion. In general it performs a multi-objective constrained optimization problem for resource reallocation and system reconfiguration of low-level and high-level controllers in a hierarchical manner. The ACM considered here focuses in particular how prognostic information can be integrated and processed to carry out such reconfiguration tasks more efficiently.
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Actuator Prognostics
Project Lead: Edward Balaban
This project concentrates on detecting and classifing incipient fault conditions in Electro Mechanical Actuators (EMA) at any point during their lifetime which can be used to provide an acurate picture of EMA component health to maintenance crews, enabling on-demand, selective servicing.
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Distributed Prognostics
Project Lead: Sankalita Saha
Distributed prognostics is the next step in evolution of ISHM systems. They involve a coordinated health management of a system by using a distributed architecture of smart sensor devices. These devices monitor the health of individual subsystems. When any component/sub-system triggers the possibility of a failure requiring more attention, these devices work in coordination with each other to estimate the RUL (remaining useful life) and the health implications for the whole system.
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Prognostics Performance Evaluation
Project Lead: Abhinav Saxena
Develop universal performance metrics that allow objective assessment of prognostic algorithm performance.
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Solid Rocket Motor Failure Prediction
Project Lead: Vadim Smelyanskiy
Predict case breach of solid rocket motor by modeling physics-of-failure such that lead time of only a few seconds prior to case breach is guaranteed.
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Water Recycling System Prognostics
Project Lead: Indranil Roychoudhury
The water recycling system cleans graywater and recycles it into clean water. The underlying technology is based on forward osmosis. The work described here is developing a physics model of both nominal and faulty system behavior of the WRS for several different fault scenarios and employs diagnostic and prognostic techniques to detect faulty behavior and estimate remaining life.


Center Coordinator
Goebel, Kai, Ph.D.


Book published by CoE members: Prognostics: The Science of Predictions

Open Source Products Generalized Software Architecture for Prognostics; Prognostic Models; Prognostic Algorithms Random Variable Toolbox

Annual Conference of the PHM Society
Philadelphia, PA
Sept 24-27, 2018

Center Members
Bajwa, Anupa
Balaban, Edward
Banerjee, Portia
Corbetta, Matteo
Frost, Susan
Gorospe, George
Iverson, David
Kulkarni, Chetan
Martin, Rodney
McIntosh, Dawn
Nishikawa, David
O'Connor, Molly
Okolo, Wendy
Oza, Nikunj
Patterson-Hine, Ann
Poll, Scott
Roychoudhury, Indranil
Sweet, Adam
Teubert, Christopher
Timucin, Dogan
Wheeler, Kevin


Collaborations & Associations

Industry Day Presentations

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