An inference model for combustion diagnostics in an experimental oil furnace

Fleury, Agenor de Toledo; Trigo, F.C.; Pacifico, Antonio Luiz; Martins, Flavius Portella Ribas

doi:10.1111/exsy.12245

Cited by 6 publications

(8 citation statements)

References 23 publications

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“…This research triggered several articles that related flame images processed by computer vision techniques to aspects of the combustion process dynamics [23,24]. Recently, Fleury et al [25] proposed an inference model to correlate 33 flame image features with 5 physical input values, in order to construct a diagnostic system capable of classifying flames in 9 distinct categories. Using an inference engine based on the Dempster-Shafer method, the diagnostic system was able to identify most of the sudden changes in the combustion process resulting from the modification of physical parameters.…”

Section: Introductionmentioning

confidence: 99%

On the dynamics of flame images identified through computer vision and modal methods

Chui

Neto

Trigo

et al. 2020

J Braz. Soc. Mech. Sci. Eng.

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Combustion processes in industrial furnaces pose challenging difficulties when one attempts to model flame. Chemical and physical phenomena interact and affect flame behavior in such a complex way that several different approaches have been under development to tackle the modeling problem. Flame instabilities could potentially harm safety operation of the furnace. Ultimate goal of flame control is to develop means to actuate over the flame state in order to avoid dangerous situations. A critical step towards this objective is to come up with a reliable model for the flame, so that control theory could be applied to automatically maintain flame state within safe standards. This work proposes a methodology to model the dynamics of flames through computer vision to acquire and process flame images in order to extract image features evolution in time. Then, a dynamic model is identified through random decrement algorithm and Ibrahim time domain method, which are operational modal analysis techniques, together with a random contribution. Flames from seven different combustion conditions were modeled by such methodology, and their estimated values were compared with experimental data for validation. Results show that estimated and experimental data possess a high degree of correlation, thus confirming the viability of the proposed dynamic model of flames.

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Section: Introductionmentioning

confidence: 99%

On the dynamics of flame images identified through computer vision and modal methods

Chui

Neto

Trigo

et al. 2020

J Braz. Soc. Mech. Sci. Eng.

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“…A weak thruster fault detection method 63 is developed based on the combination of artificial immune system and single pre-processing. The architecture of an expert system 64 that uses flame images grabbed during the combustion process in an experimental oil furnace as input parameters is presented. An effective method 65 for precise fault diagnosis of planetary gearbox based on fusion of vibration and acoustic data using the DST is proposed.…”

Section: Introductionmentioning

confidence: 99%

Engine fault diagnosis based on sensor data fusion considering information quality and evidence theory

Zhang

Deng

2018

Advances in Mechanical Engineering

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Fault diagnosis is a problem processing variable information obtained from different sources in nature. Evidence theory, efficient to deal with information viewed as evidence, is widely used in fault diagnosis. However, a shortcoming of the existing fault diagnosis methods only gets probability distribution rather than the basic probability assignment. A novel method of generating basic probability assignment that takes information quality into account is proposed. The probability distribution is determined by the preliminary matrix and sampling matrix that are constructed by sensor data. And the quality of probability distribution is taken as the discount factor and the rest of belief is assigned to the universal set. Hence, the basic probability assignment is obtained. Then, basic probability assignment can be combined with Dempster and Shafer evidence theory to determine the status of the engine. An application of engine fault is shown to illustrate the practicability of the proposed method. Then by comparing the result of the method which takes information quality into account (the proposed method) and does not do it, the former is better than the latter. Finally, the reliability analysis shows that the proposed method has strong reliability because performance accuracy is 100% when the error rate is less than 10%.

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“…As the global demands for environmental protection, energy conservation, and emission reduction become higher, it is urgent to develop a new generation of biodiesel combustion monitoring technology [3]. Due to the complexity of the biodiesel combustion The aim of this paper is to study the nonlinear fluctuation characteristics of the gray scale of waste oil biodiesel combustion flame images with continuously changing oxygen content, paying attention to the relationship between image flame characteristics and combustion performance parameters [37][38][39][40].…”

Section: Introductionmentioning

confidence: 99%

Experimental Demonstration of Deterministic Chaos in a Waste Oil Biodiesel Semi-Industrial Furnace Combustion System

Gao

Xiao

et al. 2019

Energies

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In this paper, the nonlinear dynamic characteristics of the oxygen-enriched combustion of waste oil biodiesel in semi-industrial furnaces were tested by the power spectrum, phase space reconstruction, the largest Lyapunov exponents, and the 0-1 test method. To express the influences of the system parameters, experiments were carried out under different oxygen content conditions (21%, 25%, 28%, 31%, and 33%). Higher oxygen enrichment degrees contribute to finer combustion sufficiency, which produces flames with high luminance. Flame luminance and temperature can be represented by different gray scale values of flame images. The chaotic characteristics of gray scale time series under different oxygen enrichment degrees were studied. With increased oxygen content, the chaotic characteristics of flame gradually developed from weak chaos to strong chaos. Furthermore, the flame maintained a stable combustion process in a high-temperature region. The stronger the chaotic characteristics of the flame, the better the combustion effect. It can be seen that the change of initial combustion conditions has a great influence on the whole combustion process. The results of several chaotic test methods were consistent. Using chaotic characteristics to analyze the waste oil biodiesel combustion process can digitize the combustion process, find the best combustion state, optimize, and precisely control it.

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An inference model for combustion diagnostics in an experimental oil furnace

Cited by 6 publications

References 23 publications

On the dynamics of flame images identified through computer vision and modal methods

On the dynamics of flame images identified through computer vision and modal methods

Engine fault diagnosis based on sensor data fusion considering information quality and evidence theory

Experimental Demonstration of Deterministic Chaos in a Waste Oil Biodiesel Semi-Industrial Furnace Combustion System

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