Defect assessment on radiant heaters using infrared thermography

González, Daniel A.; Saavedra, Francisco Javier Madruga; Quintela, M. A.; López‐Higuera, José Miguel

doi:10.1016/j.ndteint.2004.11.006

Cited by 13 publications

(7 citation statements)

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“…Instinctively, some defects can be classified and explained using the appropriate decision threshold. However, for others, their classification is left to operator's experience [2].…”

Section: Results Discussionmentioning

confidence: 99%

“…Under any excitation, the heating and cooling behaviours of any two parts of the heating wire should be the same. These behaviours can be observed by the photodetectors or thermal detectors of infrared cameras and, consequently, the heaters could be perfectly tested by using an infrared camera when enough spatial resolution is provided [2,3].…”

Section: Introductionmentioning

confidence: 99%

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Quality control on radiant heaters manufacture

et al. 2006

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An inspection process of radiant heaters is presented in this paper. The proposed non destructive testing and evaluation (NDT&E) technique for defect assessment of radiant heaters is based on infrared thermography images properly acquired and processed. The technique can be used in on-line fabrication quality control radiant heaters manufacturing processes. By exciting the heater with a very short electrical pulse, a sequence of thermographic images is captured by an infrared camera and then analyzed. Regardless of the electrical excitation applied to the heating element of the heater, the electrical power supplied will dissipate at the resistor. Provided enough spatial resolution, the heaters could be tested with an infrared camera capturing the radiated heat. The analysis of the heating wire during the heating flank shows differences among pixels corresponding to defective points and pixels belonging to non-defective areas of the wire. The automation is provided by the development of an algorithm that looks for the slope of the heating evolution of each pixel. A Radon Transform based algorithm is here proposed to reduce human intervention providing just one image where an operator could quickly locate possible defects.

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“…Instinctively, some defects can be classified and explained using the appropriate decision threshold. However, for others, their classification is left to operator's experience [2].…”

Section: Results Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Quality control on radiant heaters manufacture

et al. 2006

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“…Each kind of analyzed defect has a different thermal history after the electrical excitation because of its nature. The defects were represented in an image format in which the defects were spatially located by the optoelectronic unit [ 72 , 73 ]. This SPS-IRT-specific device was developed to perform an online fabrication quality control of vitro-ceramic ovens.…”

Section: Ten Significant Cases Of Photonic Sensorsmentioning

confidence: 99%

Sensing Using Light: A Key Area of Sensors

López‐Higuera

2021

Sensors

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This invited featured paper offers a Doctrinal Conception of sensing using Light (SuL) as an “umbrella” in which any sensing approach using Light Sciences and Technologies can be easily included. The key requirements of a sensing system will be quickly introduced by using a bottom-up methodology. Thanks to this, it will be possible to get a general conception of a sensor using Light techniques and know some related issues, such as its main constituted parts and types. The case in which smartness is conferred to the device is also considered. A quick “flight” over 10 significant cases using different principles, techniques, and technologies to detect diverse measurands in various sector applications is offered to illustrate this general concept. After reading this paper, any sensing approach using Light Sciences and Technologies may be easily included under the umbrella: sensing using Light or photonic sensors (PS).

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“…A diverse array of applications use this technology including quality control in the manufacturing sector [1,2], surveillance in the security industry [3,4], biomedical measurements for healthcare [5][6][7], and behavioral analysis [8][9][10]. Of key importance to all these computer vision applications is the ability to detect and track objects in video streams.…”

Section: Introductionmentioning

confidence: 99%