Identification of statistical patterns in complex systems via symbolic time series analysis

Shalabh Gupta, Amol Khatkhate, Asok Ray, Eric Keller

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


Identification of statistical patterns from observed time series of spatially distributed sensor data is critical for performance monitoring and decision making in human-engineered complex systems, such as electric power generation, petrochemical, and networked transportation. This paper presents an information-theoretic approach to identification of statistical patterns in such systems, where the main objective is to enhance structural integrity and operation reliability. The core concept of pattern identification is built upon the principles of Symbolic Dynamics, Automata Theory, and Information Theory. To this end, a symbolic time series analysis method has been formulated and experimentally validated on a special-purpose test apparatus that is designed for data acquisition and real-time analysis of fatigue damage in polycrystalline alloys.

Original languageEnglish (US)
Pages (from-to)477-490
Number of pages14
JournalISA Transactions
Issue number4
StatePublished - Oct 2006

All Science Journal Classification (ASJC) codes

  • Instrumentation
  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Applied Mathematics


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