RETRACTED: Phased array ultrasonic test signal enhancement and classification using Empirical Wavelet Transform and Deep Convolution Neural Network

Irregular structure welds are used in numerous military industries. This paper is interested in how to utilize phased array ultrasonic testing (PAUT) to identify and locate irregular structure weld defects. In this paper, CIVA simulation results were compared with inspection results of known defects and were found to be in very good agreement. This shows the feasibility of CIVA simulations. The coupling, among process parameters, affects the amplitude drop value from the peak to the trough of defects signal response, which represents the lateral resolution of internal weld defects and affect the imaging quality directly. Thus, the proper setting of detection parameters is critical to improve the testing efficiency and the imaging quality. This paper uses the response surface methodology (RSM) to design the test and conduct a factorial test to analyze the effect of each parameter. Modelling of the parameters and the response is conducted through center composite design (CCD) to analyze the coupling among multiple parameters and the reliability of the model. In the process of PAUT, the response surface plot, contour plot and the initial design domain of process parameters were combined for weld defects to determine the optimal parameters rapidly and correctly. Besides, CIVA simulation could further verify optimized detection parameters and position in industry site. Finally, irregular structure weld defects were identified accurately with PAUT imaging.

ABSTRACTNondestructive evaluation (NDE) techniques of phased array ultrasonic testing (PAUT) and digital X-ray radiography were employed on friction stir (FS)-welded Aluminum Alloy (AA)-2219-T87 specimens. PAUT intricacies required for scanning of FS-welded specimens with a 10-MHz 32-element transducer are discussed. The time corrected gain (TCG) calibration is required for scanning with an increase in index offset to compensate for decrease in A-Scan signal peak amplitude. Calibration techniques to find small defects with appropriate size tolerances are also established. The NDE technique of digital X-ray radiography is compared to PAUT, where it was found that a calibrated PAUT system is able to discover defects less than 0.2 mm where X-ray radiography could not. Incomplete penetration (IP), wormhole (WH), surface cavity (SC), and internal void (IV) defects are analyzed. Furthermore, an online PAUT system for FSW has been developed and successfully tested. The work provided herein will provide a gateway for an ultimate goal of an automated PAUT online sensing system.

Aging offshore infrastructure—particularly in mature basins such as the North Sea and Gulf of Mexico—faces escalating integrity management challenges as assets operate well beyond their original design lives. While Risk-Based Inspection (RBI) has become the industry standard for prioritizing inspection efforts, a critical gap persists: conventional RBI implementations often lack a formal, auditable mechanism to leverage high-confidence data from advanced non-destructive testing (NDT) methods to dynamically optimize inspection intervals. This paper addresses that gap by proposing a structured governance framework that systematically integrates quantitative evidence from advanced NDT—such as phased array ultrasonic testing (PAUT), robotic corrosion mapping, and drone-based metrology—into the RBI reassessment process. The methodology comprises three core steps: (1) updating the Probability of Failure (PoF) by incorporating measured degradation rates and revising inspection effectiveness factors to reflect the superior Probability of Detection (POD) of advanced NDT; (2) quantifying epistemic and aleatory uncertainties through probabilistic methods such as Monte Carlo simulation to establish confidence bounds on the revised PoF; and (3) subjecting the technical findings to formal review by a cross-functional governance panel against predefined risk acceptance criteria. The framework’s efficacy is demonstrated through two real-world case studies: one involving external corrosion under insulation on a topside pressure vessel, and another addressing a fatigue crack indication in a subsea pipeline girth weld. In both cases, high-fidelity NDT data enabled defensible inspection interval adjustments—extending the interval by two years in the first case and transitioning to a targeted monitoring strategy in the second—while ensuring PoF remained within corporate and regulatory risk thresholds. The study delivers a transparent, repeatable, and regulatorily defensible methodology that empowers operators of aging assets to replace rigid, calendar-driven inspection schedules with evidence-based, risk-informed decisions, thereby enhancing safety assurance, operational efficiency, and compliance.

Aiming at the distributed generations (DGs) which have strong controllability or are equipped with energy storage devices when a microgrid is in islanded operation state, an adaptive inverse control strategy applied to active power conditioning based on P-f (active power versus frequency) droop characteristic is developed. Containing adaptive LMS (Least Mean Square) filter and P-f droop control module, and regarding the rated frequency as input and the active outputs of microsources as disturbances, this method adjusts P-f slope coefficient online for the frequency of microgrid to track the rated frequency. Given the situation that imbalance active power exists, this strategy can quickly contribute to the best P-f slope coefficient along the negative gradient direction according to the theory of steepest descent method, thus guaranteeing the frequency of microgrid maintain at the rated value and completing the distribution of imbalance active power between different microsources. Finally, with this control strategy applied to a two-DG system, simulation results from MATLAB/SIMULINK verify the strategy's validity and feasibility.

As part of the development of a non destructive testing (NDT) system combining complementary inspection techniques, an inspection system based on phased array ultrasonic testing (PAUT) for detection of transverse cracking in solid railway axles is being developed. This paper presents the initial study, which includes the PAUT experimental setup and the initial results obtained after testing the system on railway axle blocks. The inspection of solid axles from their axle end face is investigated.

Large-field detection of Metal/CFRP hybrid composites based on air-coupled laser ultrasound

This study investigates microcracks and lack of sidewall fusion in API 5 L X70M grade submerged-arc welded (SAW) pipes using nondestructive testing (NDT) methods. Through metallographic analysis and advanced NDT techniques, including ultrasonic testing (UT), radiographic testing (RT), and phased array ultrasonic testing (PAUT), the origins and impacts of these defects on pipeline integrity are examined. The results reveal that slag entrapment and improper fusion, stemming from welding process inconsistencies, are primary causes. The findings provide actionable insights into prevention strategies, such as optimized welding parameters and enhanced quality assurance measures, for ensuring the reliability of high-strength pipeline steels.

9% Ni steel is commonly utilised in liquefied natural gas storage tanks due to its strong resistance to corrosion and oxidation. However, the welds in these tanks are prone to serious defects, necessitating thorough non-destructive testing. Unfortunately, when inspecting nickel-based alloy welds using phased array ultrasonic testing (PAUT) technology the ultrasonic waves are scattered and attenuated because of the coarse columnar grains. To address this challenge, this paper introduces a based on SVD enhancement directivity corrected circular coherence factor weighted TFM imaging algorithm (SVD-DC-CCF). Initially, the CCF matrix is corrected by the directivity and energy attenuation compensation factors, then weighted with the TFM pixel point amplitude. Subsequently, the weighted amplitude matrix undergoes singular value decomposition (SVD), followed by the establishment of an enhancement factor to reconstruct the amplitude matrix. The proposed algorithm is evaluated through experiments on three types of defects in a 30.0 mm thick 9% Ni steel T-shaped fillet weld test block. Results demonstrate superior signal-to-noise ratio (SNR) and enhanced image resolution. Compared to TFM results, the SNR increases by 4.97 dB, 4.25 dB, and 4.96 dB, respectively, while the array performance index (API) decreases by 9.80%, 34.39%, and 22.22%, respectively.

Although phased array ultrasonic testing (PAUT) technology has been widely employed in pipeline weld defect recognition, current defect recognition still relies on manual operation, which limits its efficiency. By combining deep learning with PAUT, this paper presents a new neural architecture based on YOLOv5 called DFW-YOLO that can call defect indications automatically. To address redundant defect feature maps, the FasterNet backbone is introduced to enhance the model’s feature extraction ability. Additionally, partial dilated convolution (PDConv) and partial convolution (PConv) with an atrous convolution branch are designed to extend the receptive field of the backbone network, mitigating the chance of feature loss. Furthermore, a weighted generalised feature pyramid network (WGFPN) structure is proposed as the neck network. This structure combines the reparameterized generalised feature pyramid network (RepGFPN) with the weighted aggregated concatenation (WAC) structure and eliminates inefficient branches to enhance the model’s feature fusion ability. The experimental results demonstrated that DFW-YOLO outperforms the original YOLOv5 in terms of detection performance. The precision, recall, mean average precision (mAP)@0.5, and mAP@0.5:0.95 are indicators for DFW-YOLO and reach 97.4%, 97.5%, 98.3%, and 68.2%, respectively, marking 4.8%, 5%, 2.7%, and 22.5% improvement compared to the original YOLOv5.

In Nuclear Construction, Larger Diameter Stainless Steel (SS) storage tanks are required to be fabricated in compliance with ASME Section III NCD Category. The standard Non-Destructive Testing (NDT) scope includes 100% Liquid Penetrant Examination (PT) and Radiographic Examination (RT) for butt welds on both longitudinal and circumferential seam joints. However, to accommodate a tight fabrication schedule and ensure continuous production during both day and night shifts, Phased Array Ultrasonic Testing (PAUT) was substituted for radiographic testing, in accordance with ASME Section III code cases for stainless steel weld joints. This change was made to avoid disruptions in production due to radiography exposure restrictions. This paper provides evidence of the enhanced Reliability of PAUT instead of RT in the inspection of SS welds of lower thickness.

For the thermal process is of special features, and it could be disturbed by uncertain factors easily, a novel modeling method based on least mean square (LMS) adaptive filter is presented. Based on the internal model control (IMC) principle, the sub-loop system and the inert area of the superheated steam system are considered as a generalized control object, an internal model-P cascade control system is designed. The models are built by the adaptive LMS filter, the corresponding internal controller is designed, and the self-adaptive control of the full operating condition is realized. The simulation result under typical operating condition in the superheated steam process of a supercritical once-through 600 MW boiler show that the method has a good regulating performance. The method is based on the online identification; it is adapted to the complex industrial process whose dynamic characters varied with different operating conditions.

This paper introduces the design methodology and development of an inspection robot for submerged structures in high‐flow aquatic environments. The robotic system employs dual counter‐rotating vortex suction cups for surface adhesion and achieves omnidirectional mobility through four integrated steering‐drive joints. Its reconfigurable articulated structure enables adaptive conformation to diverse structural geometries. Current regulatory frameworks explicitly mandate non‐destructive testing (NDT) requirements for underwater infrastructure maintenance. Conventional suspended underwater robots face challenges in maintaining stable positional control and consistent surface contact, while existing adhesion‐based systems demonstrate deficiencies in flow‐field adaptability, maneuverability, and transitional capability within complex hydrodynamic conditions. Through systematic exploration of robotic design principles balancing adsorption forces and hydrodynamic resistance under intense flow disturbances, we prototyped and validated the system via experimental campaigns in both dam environments and controlled hydrodynamic test basins. Experimental results demonstrate the robot's capability to execute high‐dexterity maneuvers including in‐situ rotation, comb‐pattern scanning, zigzag trajectories, and intersection curve paths. The system proves effective for inspecting both large‐curvature‐radius surfaces (e.g., dam facades) and small‐diameter tubular structures (e.g., jacket node welds). Field trials confirm that even with simplified control architecture requiring minimal operator intervention, the robot successfully acquires high‐resolution continuous imaging on dam surfaces and obtains valid phased array ultrasonic testing (PAUT) signals for weld inspection, demonstrating detectable sensitivity to artificially induced defects.

The marriage of welding and Non-Destructive Testing (NDT) processes at the point of manufacture has enabled the detection and correction of defects during the welding process. This has demonstrated clear financial and production benefits by reducing weld rework and ensuring schedule certainty, however this is yet to be demonstrated for use with narrow-groove welding practises. Narrow-groove welds are notoriously difficult to inspect using traditional Phased Array Ultrasonic Testing (PAUT) techniques due to large thicknesses and the vertical nature of Lack-of-Sidewall Fusion (LOSWF) defects. This is further complicated by the presence of partially-filled weld geometries during in-process inspection, which cause geometric reflections which can mask or falsely indicate the presence of a defect. A solution to this is proposed in this work, by adapting a dual-tandem phased array imaging system for the imaging of LOSWF defects in a partial weld geometry. This considers a two array system utilising a phased array probe on each weld side, coupled with an advanced dual-aperture Full Matrix Capture (FMC) acquisition technique. Advanced multi-mode image processing algorithms such as the Total Focusing Method (TFM) and Phase Coherence Imaging (PCI), with adaptive delay law calculation, have shown high sensitivity to LOSWF defects in a mock partial weld geometry. Additionally, an adaptive Probe Centre Spacing (PCS) technique is defined for in-process inspection based on amplitude and phase coherence sensitivity in partial weld geometries, with the effects of partial weld reflections analysed and discussed. These results have demonstrated the effectiveness of a dual-tandem phased array approach to imagine LOSWF defects during the in-process inspection of narrow-gap welds.

Advanced ultrasonic techniques are now widely adopted in industry, though they present a risk of operators misunderstanding key features and misinterpreting results. The new phased array post-processing techniques, such as the total focusing method (TFM) and phase coherent imaging (PCI), have been implemented in the absence of agreed standards and consistent training. As human factors play a crucial role in the reliability of non-destructive testing (NDT), the effectiveness of these advanced techniques depends heavily on the proficiency of the inspector. The development of new equipment and techniques must be accompanied by appropriate training to mitigate the risk of human error. An assessment of inspectors’ perceptions regarding the reliability and implementation of phased array ultrasonic testing (PAUT) in weld inspection, with a specific focus on TFM and PCI, was conducted. The primary objective was to evaluate the industry’s proficiency, challenges and constraints in applying these advanced methods. Surveying Level 2 inspectors predominantly in the oil and gas sector, 76% of respondents have utilised TFM/PCI within the industry. However, 74% expressed little to no confidence in their ability to use these methods effectively. A significant concern highlighted was the inadequate training available, with over 75% of respondents indicating they had received little to no formal instruction on these techniques. This lack of comprehensive training underscores the importance of addressing human factors to enhance NDT reliability. Respondents also expressed a strong need for more detailed guidelines and standardisation across original equipment manufacturers (OEMs), specifically regarding pre-system set-up, including defining the region of interest (ROI) and selecting the correct wave set. Limited understanding of relevant codes and standards results in a shortage of qualified written instructions and structured procedures, while the high cost of training remains a major concern.

Thin-wall austenitic stainless steel welds, widely used in fusion reactors and other advanced industrial applications, present significant challenges for nondestructive testing (NDT) due to their high attenuation, anisotropy, and structural noise. This study investigates the effectiveness of phased array ultrasonic testing (PAUT) with a dual-element phased array (DEPA) probe for discontinuity detection in these welds. Numerical simulations were performed using CIVA software to model ultrasonic wave propagation, optimize inspection parameters, and analyze sound-field characteristics. Experimental validation, performed under identical conditions with specimens containing manufactured discontinuities, confirmed the accuracy of the simulation results. The findings indicate that longitudinal waves outperform transverse waves in beam coverage and sound pressure, making them the optimal wave mode for inspecting thin-wall welds. At a frequency of 5 MHz, a balance between resolution and penetration was achieved, reducing structural noise interference and ensuring accurate imaging across the weld depth.