Review and 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 review of SDF and its performance evaluation 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. This work has been supported in part by the U.S. Army Research Laboratory and the U.S. Army Research Office under Grant No. W911NF-07-1-0376, by the U.S. Office of Naval Research under Grant No. N00014-08-1-380, and by NASA under Cooperative Agreement No. NNX07AK49A.