Comparative evaluation of Symbolic Dynamic Filtering for detection of anomaly patterns

Symbolic Dynamic Filtering (SDF) has been recently reported in literature as a pattern recognition tool for early detection of anomalies (i.e., deviations from the nominal behavior) in complex dynamical systems. This paper presents a comparative evaluation of SDF relative to other classes of pattern recognition tools, such as Bayesian Filters and Artificial Neural Networks, from the perspectives of: (i) Anomaly detection capability, (ii) Decision making for failure mitigation and (iii) Computational efficiency. The evaluation is based on analysis of time series data generated from a nonlinear active electronic system.