Anomaly Detection Based on Information-Theoretic Measures and Particle Filtering Algorithms

Marcos E. Orchard, Benjamín Olivares, Matías Cerda, and Jorge F. Silva
Submission Type: 
Full Paper
Supporting Agencies (optional): 
Conicyt, Chile
phmc_12_102.pdf119.83 KBSeptember 11, 2012 - 4:43pm

This paper presents an anomaly detection module that uses information-theoretic measures to generate a fault indicator from a particle-filtering-based estimate of the posterior state pdf of a dynamic system. The selected measure allows isolating events where the particle filtering algorithm is unable to track the process measurements using a predetermined state transition model, which translates into either a sudden or a steady increment in the differential entropy of the state pdf estimate (evidence of an anomaly on the system). Anomaly detection is carried out by setting a threshold for the entropy value. Actual data illustrating aging of an energy storage device (specifically battery state-of-health (SOH) measurements [A-hr]) are used to test and validate the proposed framework.

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Submission Keywords: 
information theory
anomaly detection
particle filtering
Submission Topic Areas: 
Data-driven methods for fault detection, diagnosis, and prognosis

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