Real-Time Monitoring and Diagnostics of Anomalous Behavior in Dynamical Systems

Sudeepta Mondal, Chandrachur Bhattacharya, Najah F. Ghalyan, Asok Ray

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

Real-time condition monitoring of complex dynamical systems is of critical importance for predictive maintenance. This chapter focuses on data-driven techniques of fault diagnostics with an emphasis on real-time detection of anomalous behavior in combustion systems. It presents the applications of well-known statistical learning techniques such as D-Markov modeling and hidden Markov modeling (HMM) as possible data-driven solutions for anomaly detection in combustion systems. From the perspective of real-time monitoring and diagnostics, such statistical tools are applicable to stochastic dynamical systems in general. Both D-Markov and HMM algorithms have been validated on experimental data from a laboratory apparatus, which is an electrically heated Rijke tube.

Original languageEnglish (US)
Title of host publicationEnergy, Environment, and Sustainability
PublisherSpringer Nature
Pages301-327
Number of pages27
DOIs
StatePublished - 2020

Publication series

NameEnergy, Environment, and Sustainability
ISSN (Print)2522-8366
ISSN (Electronic)2522-8374

All Science Journal Classification (ASJC) codes

  • Renewable Energy, Sustainability and the Environment
  • Automotive Engineering
  • Environmental Engineering

Fingerprint Dive into the research topics of 'Real-Time Monitoring and Diagnostics of Anomalous Behavior in Dynamical Systems'. Together they form a unique fingerprint.

Cite this