An information-theoretic measure for anomaly detection in complex dynamical systems

This paper presents information-theoretic analysis of time-series data to detect slowly evolving anomalies (i.e., deviations from a nominal operating condition) in dynamical systems. A measure for anomaly detection is formulated based on the concepts derived from information theory and statistical thermodynamics. The underlying algorithm is first tested on a low-dimensional complex dynamical system with a known structure—the Duffing oscillator with slowly changing dissipation. Then, the anomaly detection tool is experimentally validated on test specimens of 7075-T6 aluminum alloy under cyclic loading. The results are presented for both cases and the efficacy of the proposed method is thus demonstrated for systems of known and unknown structures.