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Review and comparative evaluation of symbolic dynamic filtering for detection of anomaly patterns

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Abstract

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.

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Author notes

  1. Murat Yasar

    Present address: Techno-Sciences, Inc., Beltsville, MD, 20705-3194, USA

Authors and Affiliations

  1. Mechanical Engineering Department, The Pennsylvania State University, University Park, PA, 16802, USA

    Chinmay Rao, Asok Ray, Soumik Sarkar & Murat Yasar

Authors
  1. Chinmay Rao
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  2. Asok Ray
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  3. Soumik Sarkar
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  4. Murat Yasar
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Corresponding author

Correspondence to Asok Ray.

Additional information

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.

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Rao, C., Ray, A., Sarkar, S. et al. Review and comparative evaluation of symbolic dynamic filtering for detection of anomaly patterns. SIViP 3, 101–114 (2009). https://doi.org/10.1007/s11760-008-0061-8

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  • DOI: https://doi.org/10.1007/s11760-008-0061-8

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