Dangerous gas leakage may cause severe consequences in
industrial
fields. Hence, it is significant to develop a reliable and rapid method
for gas leakage recognition. Infrared imaging technology has a privilege
of visibility for some invisible objects. However, infrared radiation
signals of the gases are extremely weak. Therefore, it is hard to
identify the gas leakage by normal infrared imaging device with wide
wavelength range in the condition of atmospheric dispersion. In this
research, a series of enhancement methods to detect the gas leakage
with conventional wide-wavelength thermal imaging device was proposed
by introducing auxiliary excitation including background enhancement
and external thermal pulse excitation to improve the infrared imaging
for gas leaking in the atmosphere. CO2 gas leakage was
tested with FLIR-DuoR infrared camera under different auxiliary excitation
modes. The experiments showed that it was impossible to recognize
the leaking gases by infrared imager without any auxiliary means even
there had large temperature difference between the leaking gases and
background. Proper backboard did make the gas leaking visible except
the white board for gas leaking at room temperature. However, the
auxiliary background board with different materials demonstrated varied
performance for imaging enhancement. Moreover, the auxiliary excitation
with light-thermal pulse excitation also enhanced the infrared imaging
for gas leakage. Finally, a leakage recognition method with machine
learning model was proposed based on infrared images obtained from
the experiments. The result indicated that overall prediction accuracy
was above 95%. Therefore, the gas leakage recognition method based
on normal infrared imaging with auxiliary excitation means and machine
learning model is a potentially good tool to detect and distinguish
the gas leakage.
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