An Opening Profile Recognition Method for Magnetic Flux Leakage Signals of Defect

The defect opening profile recognition is of great concern in the magnetic flux leakage (MFL) measurement technique. The detected spatial MFL signal has three components: horizontal, vertical, and normal components. Horizontal and normal component signals are commonly used to estimate the defect profile, while the vertical component has always been neglected. With the development of the high resolution and the 3-D MFL testing techniques, the vertical component signal is becoming more available. This paper analyzes the essential right-angle features of the vertical component signal, which is useful for the defect opening profile recognition. After obtaining the initial profile from the horizontal or normal component, the types of the right angle is identified from the vertical component, and the opening profile is further optimized based on these right-angle features. The opening profile recognition method is put forward in this paper to improve the accuracy of the recognition result of the defect. Both simulation and experimental tests are conducted to verify the good performance of the proposed method. Compared with the opening profiles recognized merely by the horizontal component signal, the proposed method shows better recognition results, which also validates that the vertical component signal can also be a useful information for the defect estimation

Deep Learning for Ultrasonic Crack Characterization in NDE

Machine learning for nondestructive evaluation (NDE) has the potential to bring significant improvements in defect characterization accuracy due to its effectiveness in pattern recognition problems. However, the application of modern machine learning methods to NDE has been obstructed by the scarcity of real defect data to train on. This article demonstrates how an efficient, hybrid finite element (FE) and ray-based simulation can be used to train a convolutional neural network (CNN) to characterize real defects. To demonstrate this methodology, an inline pipe inspection application is considered. This uses four plane wave images from two arrays and is applied to the characterization of cracks of length 1-5 mm and inclined at angles of up to 20° from the vertical. A standard image-based sizing technique, the 6-dB drop method, is used as a comparison point. For the 6-dB drop method, the average absolute error in length and angle prediction is ±1.1 mm and ±8.6°, respectively, while the CNN is almost four times more accurate at ±0.29 mm and ±2.9°. To demonstrate the adaptability of the deep learning approach, an error in sound speed estimation is included in the training and test set. With a maximum error of 10% in shear and longitudinal sound speed, the 6-dB drop method has an average error of ±1.5 mmm and ±12°, while the CNN has ±0.45 mm and ±3.0°. This demonstrates far superior crack characterization accuracy by using deep learning rather than traditional image-based sizing

Domain Adapted Deep-Learning for Improved Ultrasonic Crack Characterization Using Limited Experimental Data

Deep learning is an effective method for ultrasonic crack characterization due to its high level of automation and accuracy. Simulating the training set has been shown to be an effective method of circumventing the lack of experimental data common to nondestructive evaluation (NDE) applications. However, a simulation can neither be completely accurate nor capture all variability present in the real inspection. This means that the experimental and simulated data will be from different (but related) distributions, leading to inaccuracy when a deep learning algorithm trained on simulated data is applied to experimental measurements. This article aims to tackle this problem through the use of domain adaptation (DA). A convolutional neural network (CNN) is used to predict the depth of surface-breaking defects, with in-line pipe inspection as the targeted application. Three DA methods across varying sizes of experimental training data are compared to two non-DA methods as a baseline. The performance of the methods tested is evaluated by sizing 15 experimental notches of length (1–5 mm) and inclined at angles of up to 20° from the vertical. Experimental training sets are formed with between 1 and 15 notches. Of the DA methods investigated, an adversarial approach is found to be the most effective way to use the limited experimental training data. With this method, and only three notches, the resulting network gives a root-mean-square error (RMSE) in sizing of 0.5 ± 0.037 mm, whereas with only experimental data the RMSE is 1.5 ± 0.13 mm and with only simulated data it is 0.64 ± 0.044 mm

Prediction of Stress Concentration Factor from In-Line Inspection Data Using Convolutional Neural Networks

It is becoming increasingly harder to install new oil and gas pipelines, the need for fuel at a reasonable cost is at an all-time high, and the public tolerance for pipeline failure is zero to none. To help existing pipelines maintain these rising energy demands and meet their fitness for service assessments, operators rely on in-line inspection (ILI) data to identify dent defects and calculate their stress concentration factor (SCF). These ILI runs typically identify a large number of dent defects, making it difficult for engineers to process individually. This study developed a Convolutional Neural Network (CNN) trained on 4,667 raw ILI data files containing dent shapes, along with a data pre-processing algorithm to convert ILI data into pseudo-images, to address the need of prioritizing dented pipeline segments based on their SCF. The final CNN model successfully predicted SCFs ranging from 1.04 to 10.69 with an RMSE of 0.418 and R2 of 0.929, therefore showing potential as the framework for a pre-assessment tool used by pipeline operators

Investigation of Tx-Rx mutual inductance eddy current system for high lift-off inspection

PhD ThesisEddy current (EC) testing is a popular inspection technique due to its harsh environment tolerance and cost-effectiveness. Despite the immense research in EC inspection, defect detection at high lift-off still poses a challenge. The weakening mutual coupling of EC probe and sample due to the increase in lift-off degrades signal strength and thus reduces the detection sensitivity. Although signal processing can be used to mitigate lift-off influence, it is laborious and time consuming. Therefore, in this study, a Tx-Rx probe system is proposed to deal with high lift-off inspection. The parts of the study of the Tx-Rx EC system includes optimisation of probe configuration, improvement of signal conditioning circuit and comparative study of excitation modes. In optimisation of probe configuration, lift-off and coil gap are optimized to mitigate the offset caused by the direct coupling of Tx-Rx coils. The optimum coil gaps of Tx-Rx probe for different lift-offs are found by observing the highest signal strength. The optimisation of coil gap against lift-off extends the detection sensitivity of the EC system to a lift-off of about 30 mm which is by far higher than 5 mm lift-off limit of a single-coil EC probe. In signal conditioning aspect, a modified Maxwell bridge circuit is designed to remove the offset due to self- impedance of the Rx coil. The proposed circuit mitigates the influence of the self-impedance of Rx coil and improves signal-to- noise ratio SNR. In the excitation mode, pulse and sweep frequency signals are compared to study detection sensitivity, SNR and crack quantification capability. The result of the comparative study reveals that pulse excitation is good for crack sizing while sweep frequency excitation is better for crack detection. Simulations and experimental studies are carried out to show the efficacy of the Tx-Rx EC system in high lift-off crack detection