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A diagnoser algorithm for anomaly detection in DEDS under partial and unreliable observations: characterization and inclusion in sensor configuration optimization

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Abstract

Complex engineering systems have to be carefully monitored to meet demanding performance requirements, including detecting anomalies in their operations. There are two major monitoring challenges for these systems. The first challenge is that information collected from the monitored system is often partial and/or unreliable, in the sense that some occurred events may not be reported and/or may be reported incorrectly (e.g., reported as another event). The second is that anomalies often consist of sequences of event patterns separated in space and time. This paper introduces and analyzes a diagnoser algorithm that meets these challenges for detecting and counting occurrences of anomalies in engineering systems. The proposed diagnoser algorithm assumes that models are available for characterizing plant operations (via stochastic automata) and sensors (via probabilistic mappings) used for reporting partial and unreliable information. Methods for analyzing the effects of model uncertainties on the diagnoser performance are also discussed. In order to select configurations that reduce sensor costs, while satisfying diagnoser performance requirements, a sensor configuration selection algorithm developed in previous work is then extended for the proposed diagnoser algorithm. The proposed algorithms and methods are then applied to a multi-unit-operation system, which is derived from an actual facility application. Results show that the proposed diagnoser algorithm is able to detect and count occurrences of anomalies accurately and that its performance is robust to model uncertainties. Furthermore, the sensor configuration selection algorithm is able to suggest optimal sensor configurations with significantly reduced costs, while still yielding acceptable performance for counting the occurrences of anomalies.

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Notes

  1. Note that the addition of AP 1 and AP 2 in Eq. 7.1 does not change the behavior described by UO 1 through UO 6. The only effect is that f 1 and f 2 are executed if and only if A1 and A2 occur, respectively.

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Acknowledgements

The research reported in this paper was supported by the U.S. Department of Energy contract DE-AC07-05ID14517. The authors would also like to acknowledge the comments provided by the reviewers to improve the quality of this paper.

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Authors and Affiliations

  1. Monitoring and Decision Systems Group, Idaho National Laboratory, Idaho Falls, ID, 83415-3675, USA

    Wen-Chiao Lin & Humberto E. Garcia

  2. Pyroprocessing Department, Idaho National Laboratory, Idaho Falls, ID, 83415-6180, USA

    Tae-Sic Yoo

Authors
  1. Wen-Chiao Lin
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  2. Humberto E. Garcia
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  3. Tae-Sic Yoo
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Correspondence to Humberto E. Garcia.

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Lin, WC., Garcia, H.E. & Yoo, TS. A diagnoser algorithm for anomaly detection in DEDS under partial and unreliable observations: characterization and inclusion in sensor configuration optimization. Discrete Event Dyn Syst 23, 61–91 (2013). https://doi.org/10.1007/s10626-011-0128-5

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  • DOI: https://doi.org/10.1007/s10626-011-0128-5

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