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Project List (Active)

Actuator Prognostics
Project Lead: Edward Balaban
This project concentrates on detecting and classifying incipient fault conditions in Electro Mechanical Actuators (EMA) at any point during their lifetime which can be used to provide an accurate picture of EMA component health to maintenance crews, enabling on-demand, selective servicing.
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Battery Prognostics
Project Lead: Abhinav Saxena
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 Goebel
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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Diagnostic Algorithm Benchmarking
Project Lead: Scott Poll
A framework for systematic benchmarking of diagnostic technologies has been developed and implemented using the Electrical Power System testbed in the ADAPT lab. Our benchmarking approach generates realistic data sets for diagnostic benchmarking and emphasizes the use of standardized vocabularies and protocols which together enable “apples to apples” assessments of the effectiveness of different diagnostic technologies. The framework has been used in two diagnostic competitions hosted at the International Workshop on Principles of Diagnosis (DX).
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Electronics Prognostics
Project Lead: Jose Celaya
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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HyDE Diagnostics
Project Lead: Sriram Narasimhan
Develop a model-based diagnosis engine that uses candidate generation and consistency checking to diagnose discrete faults in stochastic hybrid systems. The system uses hybrid (combined discrete and continuous) models and sensor data to deduce the evolution of the state of the system over time, including changes in state indicative of faults.
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Project List (Completed)

Diagnostic Inference using Probabilistic Computation
Project Lead: Ole J. Mengshoel
The goal of this project is to investigate probabilistic approaches to diagnostic inference. Our research includes the development of new methods and algorithms as well as development of cutting edge applications and demonstrations of importance to NASA.We investigate a real-world electrical power system (EPS), namely the Advanced Diagnostics and Prognostics Testbed, which is representative of EPSs found in aerospace vehicles, and demonstrate how probabilistic approaches to diagnostic inference offer a scalable approach with potential for real-time evaluation in aircraft and spacecraft.
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Members

Group Lead
Scott Poll

Lead Scientist
Jose Celaya

Group Members
Edward Balaban
Brian Bole
Matt Daigle
Susan Frost
George Gorospe
Chetan Kulkarni
Sriram Narasimhan
David Nishikawa
Indranil Roychoudhury
Kristin Rozier
Abhinav Saxena
Shankar Sankararaman
Adam Sweet
Chris Teubert

Collaborations
Auburn University
Clarkson University
Dell
Georgia Tech
Global Technology
Idaho National Lab
Impact Technologies
Iowa State University
ISO
JPL/Caltech
Montana Tech
Moog
Penn State ARL
Qualtech Systems, Inc.
Ridgetop
Scientific Monitoring, Inc.
Sentient Corporation
Stanford University
Syracuse University
UCLA
University of Connecticut
USAF
University of Maryland
Vanderbilt University

Alumni
Chris Bond
Jonny da Silva
Bhaskar Saha
Ole Mengshoel
David Garcia
Tolga Kurtoglu
Peter Liu
Greg Sonnenfeld
Sankalita Saha

Current Interns

Fomer Interns
Reem Ketait
Omer Ayubi
Kamal Al Mazam
Mona Fahimi
Hahna Alexander
-Carnegie Mellon
Prasun Bansal
-Stanford
Mark Barycza
-Santa Clara U.
Joe Calderon
-San Jose State Gilbert Castillo
-Florida Inst. of Tech.
Derek Chen
-San Jose State
Julian Corona
-Notre Dame
George Gorospe
-U. of New Mexico
Craig Harrison -U. of Maine
Steve Hine -UC Santa Barbara
Patrick Ho
-U. of Virginia
Eric Kato
-Purdue
Anish Kumar
-San Jose State
Cyril Lan
-MIT
Iris Lee
-San Jose State
Alexander Mentch
-Washington U. (St. Louis)
Jeremy Nelson
-UC Riverside
Shompa Mahiuddin
-San Jose State
Nicholas Myers
-MIT
Nishad Patil
-U. of Maryland
Godohaldo Perez
-U. of Puerto Rico
Brian Ricks
-U. of Texas, Dallas
Indranil RoyChoudhury
-Vanderbilt
Jeffrey Spraker
-CalTech
Christian Talmage
-Dartmouth
Roseleigh Taylor
-MIT
Trevor Tetzlaff
-San Francisco State
Caroline Uriarte
-Col. School of Mines
Gayathri Varadarajan
-Arizona State
Stephanie Wright
-Vanderbilt
Justin Yu
-Cornell
Yuliya Zabiyaka
-UCLA

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