Anomaly detection in nuclear power plants via symbolic dynamic filtering

Xin Jin, Yin Guo, Soumik Sarkar, Asok Ray, Robert M. Edwards

Research output: Contribution to journalArticlepeer-review

35 Scopus citations


Tools of sensor-data-driven anomaly detection facilitate condition monitoring of dynamical systems especially if the physics-based models are either inadequate or unavailable. Along this line, symbolic dynamic filtering (SDF) has been reported in literature as a real-time data-driven tool of feature extraction for pattern identification from sensor time series. However, an inherent difficulty for a data-driven tool is that the quality of detection may drastically suffer in the event of sensor degradation. This paper proposes an anomaly detection algorithm for condition monitoring of nuclear power plants, where symbolic feature extraction and the associated pattern classification are optimized by appropriate partitioning of (possibly noise-contaminated) sensor time series. In this process, the system anomaly signatures are identified by masking the sensor degradation signatures. The proposed anomaly detection methodology is validated on the International Reactor Innovative & Secure (IRIS) simulator of nuclear power plants, and its performance is evaluated by comparison with that of principal component analysis (PCA).

Original languageEnglish (US)
Article number5654618
Pages (from-to)277-288
Number of pages12
JournalIEEE Transactions on Nuclear Science
Issue number1 PART 2
StatePublished - Feb 2011

All Science Journal Classification (ASJC) codes

  • Nuclear and High Energy Physics
  • Nuclear Energy and Engineering
  • Electrical and Electronic Engineering


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