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

Fusion of multi-view ultrasonic data for increased detection performance in non-destructive evaluation

State-of-the-art ultrasonic non-destructive evaluation (NDE) uses an array to rapidly generate multiple, information-rich views at each test position on a safety-critical component. However, the information for detecting potential defects is dispersed across views, and a typical inspection may involve thousands of test positions. Interpretation requires painstaking analysis by a skilled operator. In this paper, various methods for fusing multi-view data are developed. Compared with any one single view, all methods are shown to yield significant performance gains, which may be related to the general and edge cases for NDE. In the general case, a defect is clearly detectable in at least one individual view, but the view(s) depends on the defect location and orientation. Here, the performance gain from data fusion is mainly the result of the selective use of information from the most appropriate view(s) and fusion provides a means to substantially reduce operator burden. The edge cases are defects that cannot be reliably detected in any one individual view without false alarms. Here, certain fusion methods are shown to enable detection with reduced false alarms. In this context, fusion allows NDE capability to be extended with potential implications for the design and operation of engineering assets

Application of Machine Learning to Ultrasonic Nondestructive Evaluation

Machine learning (ML) techniques have the potential to provide automated data analysis for nondestructive evaluation (NDE) applications with human-level accuracy. This is of great value as the data gathered by NDE inspection is, increasingly, large and complex, making manual data analysis expensive, slow and sensitive to operator variability. However, there are three major barriers to the application of ML models to industrial NDE: sourcing useful training data, choosing informative features, and building trust in the model’s predictions. This thesis investigates how these barriers can be overcome by deep learning with simulated training sets, domain adaptation, uncertainty quantification, and improved interpretability. An example NDE use case is considered: defect sizing for ultrasonic inline pipe inspection. An inspection configuration is devised to closely match the conditions found in inline inspection of oil pipelines, resulting in ultrasonic plane wave images of surface breaking defects. These ultrasonic images are used as input to ML models to predict the size of the defects. A convolutional neural network (CNN) is trained to size defects, using a simulated data set, and applied to previously unseen experimental data. As the CNN takes ultrasonic images as input there is no need to manually select informative features. The CNN is compared to a traditional NDE sizing method, 6 dB drop, and demonstrates significantly better sizing accuracy. Further sizing accuracy improvements are achieved through the inclusion of a small amount of experimental data in the training procedure. This additional training data is included with the aim of reducing the effect that differences in simulated and experimental data have on sizing performance. An adversarial-based domain adaptation technique is found to be the optimal way to leverage small amounts of experimental training data. Building trust in the prediction of ML models is essential for qualifying them for use in NDE industry. Uncertainty quantification (UQ) is a significant part of this, as it is essential to the decision making for any automated data analysis. This thesis investigates two modern UQ techniques, finding deep ensembles to be an effective way to quantify the uncertainty of sizing predictions. Further trust is built by improving the interpretability and explainability of ML for NDE. This is achieved with a novel dimensionality reduction method: Gaussian feature approximation (GFA). GFA involves fitting a 2D gaussian to an ultrasonic image and storing the resulting seven parameters that describe it. These parameters can be used as input features for a ML model. As individual GFA features are meaningful to a human (unlike pixel intensities) the resulting model is implicitly more interpretable than one trained on raw images. Shapley additive explanations are used to indicate how each feature contributes to a crack size prediction. The results presented in this thesis indicate that it is possible to use ML to achieve automated data analysis for real-world industrial NDE applications