A Combined Anomaly Detection and Failure Prognosis Approach for Estimation of Remaining Useful Life in Energy Storage Devices

##plugins.themes.bootstrap3.article.main##

##plugins.themes.bootstrap3.article.sidebar##

Marcos E. Orchard Liang Tang George Vachtsevanos

Abstract

Failure prognosis and uncertainty representation in long- term predictions are topics of paramount importance when trying to ensure safety of the operation of any system. In this sense, 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 of using a combination of PF-based anomaly detection and prognosis approaches to isolate rare events that may affect the understanding about how the fault condition evolves in time. The performance of this framework is thoroughly compared using a set of ad hoc metrics. Actual data illustrating aging of an energy storage device (specifically battery state-of-health (SOH) measurements [A-hr]) are used to test the proposed framework.

How to Cite

E. Orchard, M. ., Tang, L. ., & Vachtsevanos, G. (2011). A Combined Anomaly Detection and Failure Prognosis Approach for Estimation of Remaining Useful Life in Energy Storage Devices. Annual Conference of the PHM Society, 3(1). https://doi.org/10.36001/phmconf.2011.v3i1.2014
Abstract 108 | PDF Downloads 90

##plugins.themes.bootstrap3.article.details##

Keywords

Anomaly Detection, Failure Prognosis, Particle Filtering

References
Andrieu, C., A. Doucet, E. Punskaya, (2001). “Sequential Monte Carlo Methods for Optimal Filtering,” in Sequential Monte Carlo Methods in Practice, A. Doucet, N. de Freitas, and N. Gordon, Eds. NY: Springer-Verlag.

Arulampalam, M.S., S. Maskell, N. Gordon, T. Clapp, (2002). “A Tutorial on Particle Filters for Online Nonlinear/Non-Gaussian Bayesian Tracking,” IEEE Transactions on Signal Processing, vol. 50, no. 2, pp. 174 – 188.

Doucet, A., (1998). “On sequential Monte Carlo methods for Bayesian Filtering,” Technical Report, Engineering Department, Univ. Cambridge, UK.

Doucet, A., N. de Freitas, N. Gordon, (2001). “An introduction to Sequential Monte Carlo methods,” in Sequential Monte Carlo Methods in Practice, A. Doucet, N. de Freitas, and N. Gordon, Eds. NY: Springer-Verlag.

Orchard, M., G. Kacprzynski, K. Goebel, B. Saha, G. Vachtsevanos, (2008). “Advances in Uncertainty Representation and Management for Particle Filtering Applied to Prognostics,” 2008 International Conference on Prognostics and Health Management PHM 2008, Denver, CO, USA.

Orchard, M., (2009). On-line Fault Diagnosis and Failure Prognosis Using Particle Filters. Theoretical Framework and Case Studies, Publisher: VDM Verlag Dr. Müller Aktiengesellschaft & Co. KG, Saarbrücken, Germany, 108 pages. Atlanta: The Georgia Institute of Technology, Diss., 2007.

Orchard, M. G. Vachtsevanos, (2009). “A Particle Filtering Approach for On-Line Fault Diagnosis and Failure Prognosis,” Transactions of the Institute of Measurement and Control, vol. 31, no. 3-4, pp. 221- 246.

Orchard, M., F. Tobar, G. Vachtsevanos, (2009). “Outer Feedback Correction Loops in Particle Filtering-based Prognostic Algorithms: Statistical Performance Comparison,” Studies in Informatics and Control, vol.18, Issue 4, pp. 295-304.

Orchard, M., L. Tang, K. Goebel, G. Vachtsevanos, (2009). “A Novel RSPF Approach to Prediction of High-Risk, Low-Probability Failure Events,” First Annual Conference of the Prognostics and Health Management Society, San Diego, CA, USA.

Patrick, R., M. Orchard, B. Zhang, M. Koelemay, G. Kacprzynski, A. Ferri, G. Vachtsevanos, (2007). “An Integrated Approach to Helicopter Planetary Gear
Fault Diagnosis and Failure Prognosis,” 42nd Annual Systems Readiness Technology Conference, AUTOTESTCON 2007, Baltimore, USA.

Verma, V., G. Gordon, R. Simmons, S. Thrun, (2004). “Particle Filters for Rover Fault Diagnosis,” IEEE Robotics & Automation Magazine, pp. 56 – 64.

Vachtsevanos, G., F.L. Lewis, M.J. Roemer, A. Hess, B. Wu, (2006). Intelligent Fault Diagnosis and Prognosis for Engineering Systems, Hoboken, NJ, John Wiley and Sons.

Zhang, B., T. Khawaja, R. Patrick, M. Orchard, A. Saxena, G. Vachtsevanos, (2009). “A Novel Blind Deconvolution De-Noising Scheme in Failure Prognosis,” IEEE Transactions on Instrumentation and Measurement, vol. 58, no. 2, pp. 303-310.
Section
Technical Papers

Most read articles by the same author(s)