Friday , June 22 2018

Risk-Sensitive Particle-Filtering-based Prognosis Framework for Estimation of Remaining Useful Life in Energy Storage Devices

Dr. Marcos E. ORCHARD1, Liang TANG2, Bhaskar SAHA3, Kai GOEBEL4, Dr. George J. VACHTSEVANOS2,5

1 Electrical Engineering Department, Universidad de Chile
Santiago 8370451, Chile
morchard@ing.uchile.cl
2 Impact Technologies, LLC, Rochester, NY 14623, USA
liang.tang@impact-tek.com
3 MCT, Inc at NASA Ames Research Center,
MS 269-4, Moffett Field, CA. 94035, USA
bhaskar.saha@nasa.gov

4 NASA Ames Research Center,
MS 269-4, Moffett Field, CA. 94035, USA
kai.goebel@nasa.gov
5 School of Electrical and Computer Engineering, Georgia Institute of Technology,
Atlanta, GA 30332, USA
gjv@ece.gatech.eduA

Abstract: Failure prognosis, and particularly representation and management of uncertainty in long-term predictions, is a topic of paramount importance not only to improve productivity and efficiency, but also to ensure safety in the system’s operation. The use of particle filter (PF) algorithms – in combination with outer feedback correction loops – has contributed significantly to the development of a robust framework for online estimation of the remaining useful equipment life. This paper explores the advantages and disadvantages of a Risk-Sensitive PF (RSPF) prognosis framework that complements the benefits of the classic approach, by representing the probability of rare events and highly non-monotonic phenomena within the formulation of the nonlinear dynamic equation that describes the evolution of the fault condition in time. The performance of this approach is thoroughly compared using a set of ad hoc metrics. Actual data illustrating aging of an energy storage device (specifically battery capacity measurements [A-hr]) are used to test the proposed framework.

Keywords: Risk-sensitive particle filtering, failure prognosis, nonlinear state estimation, battery prognosis.

>Full text
CITE THIS PAPER AS:
Dr. Marcos E. ORCHARD, Liang TANG, Bhaskar SAHA, Kai GOEBEL, Dr. George J. VACHTSEVANOS, Risk-Sensitive Particle-Filtering-based Prognosis Framework for Estimation of Remaining Useful Life in Energy Storage Devices, Studies in Informatics and Control, ISSN 1220-1766, vol. 19 (3), pp. 209-218, 2010.