A Statistical Framework for Improved Automatic Flaw Detection in Nondestructive Evaluation Images

Nondestructive evaluation (NDE) techniques are widely used to detect flaws in critical components of systems like aircraft engines, nuclear power plants and oil pipelines in order to prevent catastrophic events. Many modern NDE systems generate image data. In some applications an experienced inspector performs the tedious task of visually examining every image to provide accurate conclusions about the existence of flaws. This approach is labor-intensive and can cause misses due to operator ennui. Automated evaluation methods seek to eliminate human-factors variability and improve throughput. Simple methods based on peak amplitude in an image are sometimes employed and a trained-operator-controlled refinement that uses a dynamic threshold based on signal-to-noise ratio (SNR) has also been implemented. We develop an automated and optimized detection procedure that mimics these operations. The primary goal of our methodology is to reduce the number of images requiring expert visual evaluation by filtering out images that are overwhelmingly definitive on the existence or absence of a flaw. We use an appropriate model for the observed values of the SNR-detection criterion to estimate the probability of detection. Our methodology outperforms current methods in terms of its ability to detect flaws

NDE: An effective approach to improved reliability and safety. A technology survey

Technical abstracts are presented for about 100 significant documents relating to nondestructive testing of aircraft structures or related structural testing and the reliability of the more commonly used evaluation methods. Particular attention is directed toward acoustic emission; liquid penetrant; magnetic particle; ultrasonics; eddy current; and radiography. The introduction of the report includes an overview of the state-of-the-art represented in the documents that have been abstracted

Transient thermography for flaw detection in friction stir welding : a machine learning approach

A systematic computational method to simulate and detect sub-surface flaws, through non-destructive transient thermography, in aluminium sheets and friction stir welded sheets is proposed. The proposed method relies on feature extraction methods and a data driven machine learning modelling structure. In this work, we propose the use of a multi-layer perceptron feed-forward neural-network with feature extraction methods to improve the flaw-probing depth of transient thermography inspection. Furthermore, for the first time, we propose Thermographic Signal Linear Modelling (TSLM), a hyper-parameterfree feature extraction technique for transient thermography. The new feature extraction and modelling framework was tested with out-of-sample experimental transient thermography data and results show effectiveness in sub-surface flaw detection of up to 2.3 mm deep in aluminium sheets (99.8 % true positive rate, 92.1 % true negative rate) and up to 2.2 mm deep in friction stir welds (97.2 % true positive rate, 87.8 % true negative rate)

New methods for statistical modeling and analysis of nondestructive evaluation data

Statistical methods have a long history of applications in physical sciences and engineering for design of experiments and data analyses. In nondestructive evaluation (NDE) studies, standard statistical methods are described in Military Handbook 1823A as guidelines to analyze the experimental NDE data both in carefully controlled laboratory setup and field studies. However complicated data structures often demand non-traditional statistical approaches. In this dissertation, with the inspiration and needs from actual NDE data applications, we introduced several statistical methods for better description of the problem and more appropriate modeling of the data. We also discussed the potential applications of those statistical methods to other research areas. The dissertation is organized as following. First a brief background introduction and overview are presented at Chapter 1. Then the complementary risk noise-interference model is discussed in Chapter 2 to better describe the noise and signal relation. In Chapter 3, a direct application of the noise interference model to vibrothermography NDE experiment scalar data is presented. In Chapter 4, the matched filter technique is used to increase signal-to-noise ratio for sequence of image analysis. In Chapter 5, the physical model assisted probability of detection analyses are introduced where the underlying physical mechanism plays an important role in the data interpretation. In Chapter 6, a bivariate normal Bayesian approach is studied to efficiently handle missing information. Finally we summarize these recent NDE developments at Chapter 7

Industrial X-ray Image Analysis with Deep Neural Networks Robust to Unexpected Input Data

X-ray inspection is often an essential part of quality control within quality critical manufacturing industries. Within such industries, X-ray image interpretation is resource intensive and typically conducted by humans. An increased level of automatization would be preferable, and recent advances in artificial intelligence (e.g., deep learning) have been proposed as solutions. However, typically, such solutions are overconfident when subjected to new data far from the training data, so-called out-of-distribution (OOD) data; we claim that safe automatic interpretation of industrial X-ray images, as part of quality control of critical products, requires a robust confidence estimation with respect to OOD data. We explored if such a confidence estimation, an OOD detector, can be achieved by explicit modeling of the training data distribution, and the accepted images. For this, we derived an autoencoder model trained unsupervised on a public dataset with X-ray images of metal fusion welds and synthetic data. We explicitly demonstrate the dangers with a conventional supervised learning-based approach and compare it to the OOD detector. We achieve true positive rates of around 90% at false positive rates of around 0.1% on samples similar to the training data and correctly detect some example OOD data

Ensemble Joint Sparse Low Rank Matrix Decomposition for Thermography Diagnosis System

Composite is widely used in the aircraft industry and it is essential for manufacturers to monitor its health and quality. The most commonly found defects of composite are debonds and delamination. Different inner defects with complex irregular shape is difficult to be diagnosed by using conventional thermal imaging methods. In this paper, an ensemble joint sparse low rank matrix decomposition (EJSLRMD) algorithm is proposed by applying the optical pulse thermography (OPT) diagnosis system. The proposed algorithm jointly models the low rank and sparse pattern by using concatenated feature space. In particular, the weak defects information can be separated from strong noise and the resolution contrast of the defects has significantly been improved. Ensemble iterative sparse modelling are conducted to further enhance the weak information as well as reducing the computational cost. In order to show the robustness and efficacy of the model, experiments are conducted to detect the inner debond on multiple carbon fiber reinforced polymer (CFRP) composites. A comparative analysis is presented with general OPT algorithms. Not withstand above, the proposed model has been evaluated on synthetic data and compared with other low rank and sparse matrix decomposition algorithms

Nondestructive Testing Methods and New Applications

Nondestructive testing enables scientists and engineers to evaluate the integrity of their structures and the properties of their materials or components non-intrusively, and in some instances in real-time fashion. Applying the Nondestructive techniques and modalities offers valuable savings and guarantees the quality of engineered systems and products. This technology can be employed through different modalities that include contact methods such as ultrasonic, eddy current, magnetic particles, and liquid penetrant, in addition to contact-less methods such as in thermography, radiography, and shearography. This book seeks to introduce some of the Nondestructive testing methods from its theoretical fundamentals to its specific applications. Additionally, the text contains several novel implementations of such techniques in different fields, including the assessment of civil structures (concrete) to its application in medicine