Early detection of lean blow out (LBO) via generalized D-Markov machine construction

Soumalya Sarkar, Asok Ray, Achintya Mukhopadhyay, Rajendra R. Chaudhari, Swarnendu Sen

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

9 Scopus citations


This paper develops a method for early detection of lean-blow-out (LBO) in combustion systems by extracting low-dimensional features from chemiluminescence time series of optical sensor data. In the proposed method, symbol strings are generated by partitioning the (finite-length) time series to construct a special class of probabilistic finite state automata (PFSA), called D-Markov machines. These PFSA have a deterministic algebraic structure and their states are represented by symbol blocks of length D or less. The states of D-Markov machines are constructed in two steps: (i) state splitting, i.e., the states are split based on their information contents, and (ii) state merging, i.e., two or more states (of possibly different lengths) are merged together to form a new state without any significant loss of their embedded information. The modeling complexity (i.e., the number of states) of a D-Markov machine is observed to be drastically reduced as the combustion system approaches LBO. The prediction of LBO is posed as a pattern classification problem based on different ranges of equivalence ratio of the flame. It is shown that, over a wide range of air-fuel premixing, a generalized D-Markov machine (i.e., with D > 1) performs better than a D-Markov machine with D = 1 as a predictor of LBO.

Original languageEnglish (US)
Title of host publication2014 American Control Conference, ACC 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Print)9781479932726
StatePublished - 2014
Event2014 American Control Conference, ACC 2014 - Portland, OR, United States
Duration: Jun 4 2014Jun 6 2014

Publication series

NameProceedings of the American Control Conference
ISSN (Print)0743-1619


Other2014 American Control Conference, ACC 2014
Country/TerritoryUnited States
CityPortland, OR

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering


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