Laser ultrasonic scanning for detection of damage in copper pipelines using blind compressive sensing and the adjacent area difference coefficient

Abstract Due to its good thermal conductivity and corrosion resistance, copper has become a common material for transmission pipelines. It is necessary to detect the early signs of damage in copper pipelines effectively and quickly. Laser ultrasound scanning is a non-contact and non-destructive damage identification method, which can realize high-precision, non-contact detection. At the same time, with the progress of internet technology, traditional damage testing has begun to use advanced technologies such as the internet of things and cloud computing to promote an upgrade of the testing industry from an offline industry to an online industry. However, obtaining a large amount of wavefield vibration data is time consuming. In this paper, we present a laser ultrasonic scanning cloud platform damage detection method for copper pipelines based on alternating learning blind compressive sensing (BCS) and the adjacent area difference coefficient (AADC); this approach can improve real-time performance and detection accuracy. First, the damage detection method is introduced in detail. BCS is used to compress the laser scanning signal at the data acquisition terminal, and then transmitted to a data processing cloud platform for reconstruction. Copper pipeline damage imaging is realized by taking the AADC value of each detection point as the pixel value. The simulated detection data of the copper pipeline are then obtained through a finite element model, and the weighted vectors of the AADC are determined by a genetic algorithm. Finally, experimental data are used to verify the effectiveness of this method, and the experimental results are analyzed and discussed. The AADC and other distance damage imaging methods are compared. The results demonstrate that this method can compress the wavefield data to 13% of the original data, and the detection of crack damage is realized.