TY - GEN
T1 - Symbolic identification and anomaly detection in complex dynamical systems
AU - Chakraborty, Subhadeep
AU - Sarkar, Soumik
AU - Ray, Asok
N1 - Copyright:
Copyright 2008 Elsevier B.V., All rights reserved.
PY - 2008
Y1 - 2008
N2 - Symbolic dynamic filtering (SDF) has been reported in recent literature for early detection of anomalies (i.e., deviations from the nominal behavior) in complex dynamical systems. In this context, instead of solely relying on physics-based modeling that may be difficult to formulate and validate, this paper proposes data-driven modeling and system identification based on the concept of Symbolic Dynamics, Automata Theory, and Information Theory. For anomaly detection in inter-connected complex dynamical systems, with or without closed loop control, the input excitation to an individual component is likely to deviate from the nominal condition as a result of deterioration of some other component(s) or to accommodate disturbance rejection by feedback control actions. This paper presents a formal-language-based syntactic method of anomaly detection to account for deviations in the pertinent input excitation. A training algorithm is formulated to generate an automaton model of the underlying subsystem or component from a set of input-output combinations for different classes of inputs, where the objective is to detect (possibly gradually evolving) anomalies under different input conditions. The proposed method has been validated on a test apparatus of nonlinear active electronics.
AB - Symbolic dynamic filtering (SDF) has been reported in recent literature for early detection of anomalies (i.e., deviations from the nominal behavior) in complex dynamical systems. In this context, instead of solely relying on physics-based modeling that may be difficult to formulate and validate, this paper proposes data-driven modeling and system identification based on the concept of Symbolic Dynamics, Automata Theory, and Information Theory. For anomaly detection in inter-connected complex dynamical systems, with or without closed loop control, the input excitation to an individual component is likely to deviate from the nominal condition as a result of deterioration of some other component(s) or to accommodate disturbance rejection by feedback control actions. This paper presents a formal-language-based syntactic method of anomaly detection to account for deviations in the pertinent input excitation. A training algorithm is formulated to generate an automaton model of the underlying subsystem or component from a set of input-output combinations for different classes of inputs, where the objective is to detect (possibly gradually evolving) anomalies under different input conditions. The proposed method has been validated on a test apparatus of nonlinear active electronics.
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U2 - 10.1109/ACC.2008.4586916
DO - 10.1109/ACC.2008.4586916
M3 - Conference contribution
AN - SCOPUS:52449087509
SN - 9781424420797
T3 - Proceedings of the American Control Conference
SP - 2792
EP - 2797
BT - 2008 American Control Conference, ACC
T2 - 2008 American Control Conference, ACC
Y2 - 11 June 2008 through 13 June 2008
ER -