Analysis of signal processing techniques in pulsed thermography

2013 ◽  
Author(s):  
Fernando Lopez ◽  
Clemente Ibarra-Castanedo ◽  
Xavier Maldague ◽  
Vicente de Paulo Nicolau
Keyword(s):  
Signal Processing ◽  
Pulsed Thermography ◽  
Processing Techniques ◽  
Signal Processing Techniques

scholarly journals Latest Advances in Common Signal Processing of Pulsed Thermography for Enhanced Detectability: A Review

2021 ◽  
Vol 11 (24) ◽  
pp. 12168
Author(s):  
Yoonjae Chung ◽  
Seungju Lee ◽  
Wontae Kim
Keyword(s):  
Signal Processing ◽  
Infrared Thermography ◽  
Heat Diffusion ◽  
Thermal Excitation ◽  
Non Destructive Testing ◽  
Destructive Testing ◽  
Pulsed Thermography ◽  
Processing Techniques ◽  
Non Destructive ◽  
Signal Processing Techniques

Non-destructive testing (NDT) is a broad group of testing and analysis techniques used in science and industry to evaluate the properties of a material, structure, or system for characteristic defects and discontinuities without causing damage. Recently, infrared thermography is one of the most promising technologies as it can inspect a large area quickly using a non-contact and non-destructive method. Moreover, thermography testing has proved to be a valuable approach for non-destructive testing and evaluation of structural stability of materials. Pulsed thermography is one of the active thermography technologies that utilizes external energy heating. However, due to the non-uniform heating, lateral heat diffusion, environmental noise, and limited parameters of the thermal imaging system, there are some difficulties in detecting and characterizing defects. In order to improve this limitation, various signal processing techniques have been developed through many previous studies. This review presents the latest advances and exhaustive summary of representative signal processing techniques used in pulsed thermography according to physical principles and thermal excitation sources. First, the basic concept of infrared thermography non-destructive testing is introduced. Next, the principle of conventional pulsed thermography and signal processing technologies for non-destructive testing are reviewed. Then, we review advances and recent advances in each signal processing. Finally, the latest research trends are reviewed.

scholarly journals Thermographic Inspection of Internal Defects in Steel Structures: Analysis of Signal Processing Techniques in Pulsed Thermography

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6015
Author(s):  
Yoonjae Chung ◽  
Ranjit Shrestha ◽  
Seungju Lee ◽  
Wontae Kim
Keyword(s):  
Signal Processing ◽  
Power Plants ◽  
Principal Component ◽  
Steel Structures ◽  
High Energy ◽  
Internal Defects ◽  
Pulsed Thermography ◽  
Processing Techniques ◽  
Signal Processing Techniques ◽  
Defect Detectability

This study performed an experimental investigation on pulsed thermography to detect internal defects, the major degradation phenomena in several structures of the secondary systems in nuclear power plants as well as industrial pipelines. The material losses due to wall thinning were simulated by drilling flat-bottomed holes (FBH) on the steel plate. FBH of different sizes in varying depths were considered to evaluate the detection capability of the proposed technique. A short and high energy light pulse was deposited on a sample surface, and an infrared camera was used to analyze the effect of the applied heat flux. The three most established signal processing techniques of thermography, namely thermal signal reconstruction (TSR), pulsed phase thermography (PPT), and principal component thermography (PCT), have been applied to raw thermal images. Then, the performance of each technique was evaluated concerning enhanced defect detectability and signal to noise ratio (SNR). The results revealed that TSR enhanced the defect detectability, detecting the maximum number of defects, PPT provided the highest SNR, especially for the deeper defects, and PCT provided the highest SNR for the shallower defects.

Automotive Radars

2017 ◽  
Author(s):  
Sujeet Patole ◽  
Murat Torlak ◽  
Dan Wang ◽  
Murtaza Ali
Keyword(s):  
Signal Processing ◽  
Pedestrian Detection ◽  
Radar Signal ◽  
Automotive Radar ◽  
Estimation Algorithms ◽  
Radar Systems ◽  
Starting Point ◽  
Processing Techniques ◽  
Automotive Radars ◽  
Signal Processing Techniques

Automotive radars, along with other sensors such as lidar, (which stands for “light detection and ranging”), ultrasound, and cameras, form the backbone of self-driving cars and advanced driver assistant systems (ADASs). These technological advancements are enabled by extremely complex systems with a long signal processing path from radars/sensors to the controller. Automotive radar systems are responsible for the detection of objects and obstacles, their position, and speed relative to the vehicle. The development of signal processing techniques along with progress in the millimeter- wave (mm-wave) semiconductor technology plays a key role in automotive radar systems. Various signal processing techniques have been developed to provide better resolution and estimation performance in all measurement dimensions: range, azimuth-elevation angles, and velocity of the targets surrounding the vehicles. This article summarizes various aspects of automotive radar signal processing techniques, including waveform design, possible radar architectures, estimation algorithms, implementation complexity-resolution trade-off, and adaptive processing for complex environments, as well as unique problems associated with automotive radars such as pedestrian detection. We believe that this review article will combine the several contributions scattered in the literature to serve as a primary starting point to new researchers and to give a bird’s-eye view to the existing research community.

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