@inbook{aff6da48053b4ef78f4cbf5e35e1a560,
title = "Real-Time Monitoring and Diagnostics of Anomalous Behavior in Dynamical Systems",
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.",
author = "Sudeepta Mondal and Chandrachur Bhattacharya and Ghalyan, {Najah F.} and Asok Ray",
note = "Publisher Copyright: {\textcopyright} 2020, Springer Nature Singapore Pte Ltd.",
year = "2020",
doi = "10.1007/978-981-15-0536-2_14",
language = "English (US)",
series = "Energy, Environment, and Sustainability",
publisher = "Springer Nature",
pages = "301--327",
booktitle = "Energy, Environment, and Sustainability",
address = "United States",
}