A novel pattern-frequency tree approach for transition analysis and anomaly detection in nonlinear and nonstationary systems

Cheng Bang Chen, Hui Yang, Soundar Kumara

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The failure to identify anomaly patterns in dynamic systems can cause catastrophic events and incur a high cost. Prior research efforts attempted to use multiple sensors for a closer monitoring of the system dynamics. However, realizing full utilization of multiple sensors without the normality assumptions and dimensionality reduction remains a research challenge to build control schemes. This paper presents a novel methodology of pattern-frequency tree for transition analysis and anomaly detection in nonlinear and nonstationary systems. First, we propose Hyperoctree State space Aggregation Segmentation (HSAS) to delineate the high-dimensional dynamic processes in a continuous state space. Then, we develop a pattern-frequency tree to characterize and model the pattern distribution. Finally, we leverage pattern-frequency distribution information to develop a k-Maximin deviation algorithm for effective and efficient detection of process anomalies. Experimental results demonstrate that the proposed method performs better than the conventional methods in multi-sensor settings and high-dimensional environments.

Original languageEnglish (US)
Title of host publication67th Annual Conference and Expo of the Institute of Industrial Engineers 2017
EditorsHarriet B. Nembhard, Katie Coperich, Elizabeth Cudney
PublisherInstitute of Industrial Engineers
Pages1264-1269
Number of pages6
ISBN (Electronic)9780983762461
StatePublished - 2017
Externally publishedYes
Event67th Annual Conference and Expo of the Institute of Industrial Engineers 2017 - Pittsburgh, United States
Duration: May 20 2017May 23 2017

Publication series

Name67th Annual Conference and Expo of the Institute of Industrial Engineers 2017

Other

Other67th Annual Conference and Expo of the Institute of Industrial Engineers 2017
Country/TerritoryUnited States
CityPittsburgh
Period5/20/175/23/17

Keywords

  • Anomaly detection
  • Dynamic transition analysis
  • Nonlinear dynamical system
  • State space representation
  • Tree algorithms

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering

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