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Along with improvement of technology and need for access to energy resources, existence of pipelines such as gas, oil and water pipes is vital for our lives. These pipes will be eroded and damaged over time. With the prediction of the defects and tracking of pipeline paths, the probability of sudden damages is greatly reduced. In this paper, at first various non-destructive methods of monitoring the pipelines are investigated and it is shown that the laser method is the most comprehensive and non-destructive inspection method and then the background of the chosen method is examined. Also, the hardware aspect of the system and the proper layout of the laser sensors are determined on the system. After that a complete mathematical model and an algorithm is proposed for it which can be used to analyze the data obtained from the simulation of laser sampling creates image of the pipe internal surface and using this method identifies the defects found at the pipe surface. In the fourth section, a pipe with specific geometric deflection is examined based on the proposed method and algorithm and its results show the correctness of the proposed method.

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In thermal barrier coatings (TBC), surface cracks, debonding, and thickness degradation may occur during the manufacturing process or life cycle, leading to poor performance and ultimately a dangerous system failure. The main goal of non-destructive testing of thermal barrier coatings is to detect these defects and determine the health of the coating. Various non-destructive inspection methods have been proposed to evaluate thermal barrier coatings, and due to the numerous advantages of thermography, including high speed, low cost, safety, no need for direct contact, automation capability, and inspection of a large area of ​​the part, this method has received special attention from researchers. This study will present a method for manufacturing samples with different diameters of artificial separation defects. The following is the equipment's arrangement and the sample's thermography process. It was concluded that blackening the surface of the sample by increasing the amount of thermal energy absorption increased the ability to identify separation defects and increased the signal-to-noise ratio by 257%. Finally, by implementing different filters on the recorded raw thermal images, it has been shown that in both cases the best filter in terms of SNR is the median filter and then the Gaussian filter. The background removal filter also had no noticeable effect on increasing the signal-to-noise ratio and acted as a complement to the median and Gaussian filters by reducing the fixed error