Symbolic time-series analysis for anomaly detection in mechanical systems

Amol Khatkhate, Asok Ray, Eric Keller, Shalabh Gupta, Shin C. Chin

Research output: Contribution to journalArticlepeer-review

38 Scopus citations


This paper examines the efficacy of a novel method for anomaly detection in mechanical systems, which makes use of a hidden Markov model, derived from the time-series data of pertinent measurement(s). The core concept of the anomaly detection method is symbolic time-series analysis that is built upon the principles of Automata Theory, Information Theory, and Pattern Recognition. The performance of this method is compared with that of other existing pattern-recognition techniques from the perspective of early detection of small fatigue cracks in ductile alloy structures. The experimental apparatus, on which the anomaly detection method is tested, is a multi-degree-of-freedom mass-beam structure excited by oscillatory motion of two electromagnetic shakers. The evolution of fatigue crack damage at one or more failure sites are detected from symbolic time-series analysis of displacement sensor signals.

Original languageEnglish (US)
Pages (from-to)439-446
Number of pages8
JournalIEEE/ASME Transactions on Mechatronics
Issue number4
StatePublished - 2006

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Computer Science Applications
  • Electrical and Electronic Engineering


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