Fractal Dimension Analysis for Robust Ultrasonic Non-Destructive Evaluation (NDE) of Coarse Grained Materials

Over the recent decades, there has been a growing demand on reliable and robust non-destructive evaluation (NDE) of structures and components made from coarse grained materials such as alloys, stainless steels, carbon-reinforced composites and concrete; however, when inspected using ultrasound, the flaw echoes are usually contaminated by high-level, time-invariant, and correlated grain noise originating from the microstructure and grain boundaries, leading to pretty low signal-to-noise ratio (SNR) and the flaw information being obscured or completely hidden by the grain noise. In this paper, the fractal dimension analysis of the A-scan echoes is investigated as a measure of complexity of the time series to distinguish the echoes originating from the real defects and the grain noise, and then the normalized fractal dimension coefficients are applied to the amplitudes as the weighting factor to enhance the SNR and defect detection. Experiments on industrial samples of the mild steel and the stainless steel are conducted and the results confirm the great benefits of the method

Deep learning in automated ultrasonic NDE -- developments, axioms and opportunities

The analysis of ultrasonic NDE data has traditionally been addressed by a trained operator manually interpreting data with the support of rudimentary automation tools. Recently, many demonstrations of deep learning (DL) techniques that address individual NDE tasks (data pre-processing, defect detection, defect characterisation, and property measurement) have started to emerge in the research community. These methods have the potential to offer high flexibility, efficiency, and accuracy subject to the availability of sufficient training data. Moreover, they enable the automation of complex processes that span one or more NDE steps (e.g. detection, characterisation, and sizing). There is, however, a lack of consensus on the direction and requirements that these new methods should follow. These elements are critical to help achieve automation of ultrasonic NDE driven by artificial intelligence such that the research community, industry, and regulatory bodies embrace it. This paper reviews the state-of-the-art of autonomous ultrasonic NDE enabled by DL methodologies. The review is organised by the NDE tasks that are addressed by means of DL approaches. Key remaining challenges for each task are noted. Basic axiomatic principles for DL methods in NDE are identified based on the literature review, relevant international regulations, and current industrial needs. By placing DL methods in the context of general NDE automation levels, this paper aims to provide a roadmap for future research and development in the area.Comment: Accepted version to be published in NDT & E Internationa

An artificial bee colony optimization based matching pursuit approach for ultrasonic echo estimation

Ultrasonic echo estimation is important in ultrasonic non-destructive evaluation and material characterization. Matching pursuit is one of the most popular methods for the purpose of estimating ultrasonic echoes. In this paper, an artificial bee colony optimization based matching pursuit approach (ABC-MP) is proposed specifically for ultrasonic signal decomposition by integrating the artificial bee colony algorithm into the matching pursuit method. The optimal atoms are searched from a continuous parameter space over a tailored Gabor dictionary in ABC-MP instead of a discrete parameter space in matching pursuit. As a result, echoes characterized by a set of physical parameters can be estimated accurately and efficiently. The performance of ABC-MP is tested using both simulated signals and real ultrasonic signals, and compared with matching pursuit. Results clearly demonstrate the superior performance of the proposed ABC-MP approach over matching pursuit in ultrasonic echo estimation in terms of the shape and amplitude of the recovered echoes and the reconstructed signal, and the residue signal

Chirplet Transform in Ultrasonic Non-Destructive Testing and Structural Health Monitoring: A Review

Ultrasonic non-destructive testing signal can be decomposed into a set of chirplet signals, which makes the chirplet transform a fitting ultrasonic signal analysis and processing method. Moreover, compared to wavelet transform, short-time Fourier transform and Gabor transform, chirplet transform is a comprehensive signal approximation method, nevertheless, the former methods gained more popularity in the ultrasonic signal processing research. In this paper, the principles of the chirplet transform are explained with a simplified presentation and the studies that used the transform in ultrasonic non-destructive testing and in structural health monitoring are reviewed to expose the existing applications and motivate the research in the potential ones

Understanding System Complexity in the Non-Destructive Testing of Advanced Composite Products

Non-destructive testing (NDT) is a quality control measure designed to ensure the safety of products according to established variability thresholds. With the development of advanced technologies and a lack of formalised knowledge of the state-of-the-art, the National Composites Centre, Bristol, has identified that the increasing complexity of composite products will lead to some severe inspection challenges. To address the apparent knowledge gap and understand system complexity, a formulaic approach to introduce intelligence and improve the robustness of NDT operations is presented. The systemic development of a high-fidelity knowledge base (KB) involves the establishment of a capability matrix that maps material, component, and defect configuration to the capabilities and limitations of selected detection methods. Population and validation are demonstrated through the experimental testing of reference standards and evaluated against an assessment criteria. System complexity in ultrasonic testing operations focusses on capturing the inherent risks in inspection and the designation of evidence-based path plans for automation platforms. Anticipated deployment of the validated applicability data within the KB will allow for road-mapping of the inspection technique development and will provide opportunities for knowledge-based decision making. Moreover, the KB highlights the need for Design for Inspection, providing measurable data that the methodology should not be ignored