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

Ultrasonic guided wave tomography of pipes: A development of new techniques for the nondestructive evaluation of cylindrical geometries and guided wave multi-mode analysis

This dissertation concentrates on the development of two new tomographic techniques that enable wide-area inspection of pipe-like structures. By envisioning a pipe as a plate wrapped around upon itself, the previous Lamb Wave Tomography (LWT) techniques are adapted to cylindrical structures. Helical Ultrasound Tomography (HUT) uses Lamb-like guided wave modes transmitted and received by two circumferential arrays in a single crosshole geometry. Meridional Ultrasound Tomography (MUT) creates the same crosshole geometry with a linear array of transducers along the axis of the cylinder. However, even though these new scanning geometries are similar to plates, additional complexities arise because they are cylindrical structures. First, because it is a single crosshole geometry, the wave vector coverage is poorer than in the full LWT system. Second, since waves can travel in both directions around the circumference of the pipe, modes can also constructively and destructively interfere with each other. These complexities necessitate improved signal processing algorithms to produce accurate and unambiguous tomographic reconstructions. Consequently, this work also describes a new algorithm for improving the extraction of multi-mode arrivals from guided wave signals. Previous work has relied solely on the first arriving mode for the time-of-flight measurements. In order to improve the LWT, HUT and MUT systems reconstructions, improved signal processing methods are needed to extract information about the arrival times of the later arriving modes. Because each mode has different through-thickness displacement values, they are sensitive to different types of flaws, and the information gained from the multi-mode analysis improves understanding of the structural integrity of the inspected material. Both tomographic frequency compounding and mode sorting algorithms are introduced. It is also shown that each of these methods improve the reconstructed images both qualitatively and quantitatively

30th International Conference on Condition Monitoring and Diagnostic Engineering Management (COMADEM 2017)

Proceedings of COMADEM 201

Novel Approaches for Structural Health Monitoring

The thirty-plus years of progress in the field of structural health monitoring (SHM) have left a paramount impact on our everyday lives. Be it for the monitoring of fixed- and rotary-wing aircrafts, for the preservation of the cultural and architectural heritage, or for the predictive maintenance of long-span bridges or wind farms, SHM has shaped the framework of many engineering fields. Given the current state of quantitative and principled methodologies, it is nowadays possible to rapidly and consistently evaluate the structural safety of industrial machines, modern concrete buildings, historical masonry complexes, etc., to test their capability and to serve their intended purpose. However, old unsolved problematics as well as new challenges exist. Furthermore, unprecedented conditions, such as stricter safety requirements and ageing civil infrastructure, pose new challenges for confrontation. Therefore, this Special Issue gathers the main contributions of academics and practitioners in civil, aerospace, and mechanical engineering to provide a common ground for structural health monitoring in dealing with old and new aspects of this ever-growing research field

A Survey on Audio-Video based Defect Detection through Deep Learning in Railway Maintenance

Within Artificial Intelligence, Deep Learning (DL) represents a paradigm that has been showing unprecedented performance in image and audio processing by supporting or even replacing humans in defect and anomaly detection. The Railway sector is expected to benefit from DL applications, especially in predictive maintenance applications, where smart audio and video sensors can be leveraged yet kept distinct from safety-critical functions. Such separation is crucial, as it allows for improving system dependability with no impact on its safety certification. This is further supported by the development of DL in other transportation domains, such as automotive and avionics, opening for knowledge transfer opportunities and highlighting the potential of such a paradigm in railways. In order to summarize the recent state-of-the-art while inquiring about future opportunities, this paper reviews DL approaches for the analysis of data generated by acoustic and visual sensors in railway maintenance applications that have been published until August 31st, 2021. In this paper, the current state of the research is investigated and evaluated using a structured and systematic method, in order to highlight promising approaches and successful applications, as well as to identify available datasets, current limitations, open issues, challenges, and recommendations about future research directions

Advances in Sensors and Sensing for Technical Condition Assessment and NDT

The adequate assessment of key apparatus conditions is a hot topic in all branches of industry. Various online and offline diagnostic methods are widely applied to provide early detections of any abnormality in exploitation. Furthermore, different sensors may also be applied to capture selected physical quantities that may be used to indicate the type of potential fault. The essential steps of the signal analysis regarding the technical condition assessment process may be listed as: signal measurement (using relevant sensors), processing, modelling, and classification. In the Special Issue entitled “Advances in Sensors and Sensing for Technical Condition Assessment and NDT”, we present the latest research in various areas of technology

Machine learning approach to thermite weld defects detection and classification.

Masters Degree. University of KwaZulu- Natal, Durban.The defects formed during the thermite welding process between two sections of rails require the welded joints to be inspected for quality purpose. The commonly used non-destructive method for inspection is Radiography testing. However, the detection and classification of various defects from the generated radiography imagesremains a costly, lengthy and subjective process as it is purely conducted manually by trained experts. It has been shown that most rail breaks occur due to a crack that initiated from the weld joint defect that was not detected. To meet the requirements of the modern technologies, the development of an automated detection and classification model is significantly demanded by the railway industry. This work presents a method based on image processing and machine learning techniques to automatically detect and classify welding defects. Radiography images are first enhanced using the Contrast Limited Adaptive Histogram Equalisation method; thereafter, the Chan-Vese Active Contour Model is applied to the enhanced images to segment and extract the weld joint as the Region of Interest from the image background. A comparative investigation between the Local Binary Patterns descriptor and the Bag of Visual Words approach with Speeded Up Robust Features descriptor was carried out for extracting features in the weld joint images. The effectiveness of the aforementioned feature extractors was evaluated using the Support Vector Machines, K-Nearest Neighbours and Naive Bayes classifiers. This study’s experimental results showed that the Bag of Visual Words approach when used with the Support Vector Machines classifier, achieves the best overall classification accuracy of 94.66%. The proposed method can be expanded in other industries where Radiography testing is used as the inspection tool