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
2 Impact Technologies, LLC, Rochester, NY 14623, USA
3 MCT, Inc at NASA Ames Research Center,
MS 269-4, Moffett Field, CA. 94035, USA
4 NASA Ames Research Center,
MS 269-4, Moffett Field, CA. 94035, USA
5 School of Electrical and Computer Engineering, Georgia Institute of Technology,
Atlanta, GA 30332, USA
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.
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.
A number of approaches have been suggested in the recent years for uncertainty representation and management in prediction. Probabilistic, soft computing methods, and tools derived from evidential theory or Dempster-Shafer theory 1 have been explored for this purpose. Although probabilistic methods offer a mathematically rigorous methodology, they typically require a statistically sufficient database to estimate the required distributions. Soft-computing methods (fuzzy logic) offer an alternative when scarce data or contradictory data are available. Dempster’s rule of combination and similar concepts from evidential theory such as belief or plausibility (upper and lower bounds of probability) based on mass function calculations can support uncertainty representation and management tasks. Confidence Prediction Neural Networks (NN) 2 have also been used to represent and manage uncertainty using Parzen windows as the kernel and a structure based on Specht’s General Regression NN 3. For tuning of model hyper-parameters given observations, probabilistic reliability analysis tools employing an inner-outer loop Bayesian update scheme 4 have been employed.
Particle-filtering (PF) based prognostic algorithms 5-12 have been established as the de facto state of the art in failure prognosis. PF algorithms allow to avoid the assumption of Gaussian (or log-normal) probability density function (pdf) in nonlinear processes, with unknown model parameters, and simultaneously help to consider non-uniform probabilities of failure for particular regions of the state domain. Particularly, the authors in 6 have proposed a mathematically rigorous method (based on PF, function kernels, and outer correction loops) to represent and manage uncertainty in long-term predictions.
However, there are still unsolved issues regarding the proper representation for the probability of rare events and highly non-monotonic phenomena, since these events are associated to particles located at the tails of the predicted probability density functions.
This paper presents a solution for this problem. The paper is structured as follows: Section 2 introduces the basics of particle filtering (PF) and its application to the field of failure prognostics. Section 3 presents the proposed Risk-Sensitive PF (RSPF) framework and analyses the main advantages and disadvantages of its implementation, using actual failure data measuring battery capacity ([A-hr]). Section 4 utilizes performance metrics to assess prognostic results and evaluates the RSPF, when compared to the classic PF prognosis framework 5-10. Section 5 states conclusions.
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