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

Marcos E. Orchard, Liang Tang, and George J. Vachtsevanos
Submission Type: 
Full Paper
phmc_11_013.pdf132.22 KBAugust 11, 2011 - 7:55pm

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 capacity measurements [A-hr]) are used to test the proposed framework.

Publication Year: 
Publication Volume: 
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Submission Keywords: 
Anomaly Detection; Failure Prognosis; Particle Filtering;
Submission Topic Areas: 
Component-level PHM
Data-driven methods for fault detection, diagnosis, and prognosis
Modeling and simulation

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