Identification of the state-space dynamics of oil flames through computer vision and modal techniques
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2015-04-01
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SILVA, R. P.
FLEURY, A. T.
MARTINS F. P. R.
PONGE-FERREIRA, W. J. A.
TRIGO, F. C.
FLEURY, A. T.
MARTINS F. P. R.
PONGE-FERREIRA, W. J. A.
TRIGO, F. C.
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Expert Systems with Applications
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SILVA, R. P.; FLEURY, A. T.; MARTINS F. P. R.; PONGE-FERREIRA, W. J. A.; TRIGO, F. C. Identification of the state-space dynamics of oil flames through computer vision and modal techniques. Expert Systems with Applications, v. 42, n. 5, p. 2421-2428, apr. 2015.
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© 2014 Elsevier Ltd.In industrial oil furnaces, unstable flames can lead to potentially dangerous conditions. For this reason, elaborate control systems are used to monitor the various parameters of the process that could become the source of such problems. A current trend in research is the one that seeks to apply artificial intelligence techniques to efficiently identify a priory anomalous behavior of the flames, so as to help improving the time response of the automatic control. In system dynamics theory, it is common sense that an accurate modeling of the process under study directly affects the performance of the controlling apparatus. Unfortunately, due to the complexity of the process, physical models of flame propagation are still not as much faithful as they should to be used for control purposes. On the other hand, could the complex dynamics of flame propagation be described in terms of an identified assumed model, one would come up with a tool for the improvement of the control strategy. In this work, a new approach based on Operational Modal Analysis (OMA) tools is used to identify four degree-of-freedom second order state-space models of oil flame dynamics in a prototype furnace. Grabbed images of a CCD camera, after being processed through a computer vision method, provide sets of characteristic vectors which, then, serve as input data to an identification OMA algorithm based on the Ibrahim Time Domain Method. Models of unstable and stable flames are built and validated through spectral analysis of the reconstructed time-domain characteristic vectors. The truthfulness of the validation scheme was then confirmed by a quantitative modal assurance criterion modified to suit the current application. On the grounds of the results obtained, it is possible to assert that the proposed approach for the description of flame dynamics can likely predict the occurrence of unstable conditions, thus becoming another tool that might be used in an automated control system.