Pressure pipeline testing is crucial for ensuring the reliability of industrial production, requiring high standards of safety, convenience, and efficiency. To extend pipeline service life and prevent safety accidents, regular and effective testing and maintenance are necessary. However, due to the confined space inside pipelines and the harmful nature of the transmitted medium, manual testing was difficult. Traditional sampling testing and excavation methods have issues with stability, efficiency, and accuracy. This paper developed an ultrasonic phased array scanning testing system for the internal testing of pipelines based on crawling robot technology, addressing problems such as large equipment size, high cost, poor mobility, and inadequate coupling. A ring-shaped phased array probe, combined with a membrane water bag coupling device, was designed and tested in actual industrial pipelines. Results showed that this probe had high sensitivity, good signal-to-noise ratio in thick-walled pipes, and can accurately detect the location and size of defects. This system improved the efficiency and accuracy of pipeline inspection, meeting the high demands of industrial production.

In order to enhance the accuracy of X-ray image recognition for weld seam defects, it is essential to employ effective image enhancement techniques. this study investigates investigated the impact of different image enhancement techniques on the quality of weld seam images. The parametersEvaluation metrics such as Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), Structural Clarity, and Information Entropy wereare employed to assess the quality of image enhancement. Experimental results indicated that histogram equalization (HE) and contrast-limited adaptive histogram equalization (CLAHE) exhibited superior contrast enhancement effects, while non-local means filtering (NLM) and discrete wavelet transform (DWT) performed well in noise reduction. Image enhancement processing based on CLAHE-NLM proveds to be more effective in assisting deep learning models for weld seam defect classification and recognition. The accuracy and F₁ score of weld seam defect classification reached 97.6% and 96.93%, respectively, representing an improvement of 3.2% and 5.23% compared to the data set without enhancement preprocessing.

Nondestructive testing is the main method for detecting the quality of welded joints and providing important data for evaluating the quality of welded joints. This paper reviewed the application status of nondestructive testing standards for welds both domestically and internationally from three aspects: the current status of international standard revision, advanced standard revision abroad, and national standard revision. It also introduced the progress of national standardization work for nondestructive testing of welds, aiming to provide useful references for domestic nondestructive testing workers.

In order to understand the effect and characteristic of phased array Phase Coherence Imaging(PCI) inspection technique, the basic theory of ultrasonic phased array TFM (total focus method) and PCI technique was introduced, the difference of TFM and PCI was analyzed detailly. According to the test results of point tip defects, planar defects, volumetric defects with PCI technique, the effect and characteristic of PCI technique were described, to promote the correct application of PCI technique in the field of industrial nondestructive testing.

This paper introduced the experimental research on the application of ultrasonic phased array detection in the detection of medium and thick plates. Experiments were conducted at different frequencies to compare the sensitivity of longitudinal wave detection, transverse wave detection, blind spots on the upper and lower surfaces of longitudinal wave detection, and blind spots on the edges of transverse wave detection on medium thick plates. The experimental results showed that ultrasonic phased array detection technology can 100% cover all defects in the center of the steel plate, with high detection accuracy, low false alarm rate, and good repeatability; The blind spot on the upper and lower surfaces of the thickness detection steel plate was ≤1.5 mm, and the minimum blind spot on the edge of the plate was ≤3 mm. The detection speed was fast and the cost was low. It can meet the highest requirements of current plate inspection standards.