Embedded decision support system for ultrasound nondestructive evaluation based on extreme learning machines

Silva, Lucas C.; Filho, Eduardo F. Simas; Albuquerque, Maria C.S.; Silva, Ivan C.; Farias, Cláudia Teresa Teles

doi:10.1016/j.compeleceng.2020.106891

Cited by 8 publications

(5 citation statements)

References 20 publications

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“…Machine learning can use all available information and produce a more accurate result, increasing automation, and reducing the possibility that the human operator will create errors. In recent years, several machine learning techniques have been employed in order to improve the reliability of ultrasound non-destructive testing [124,[221][222][223][224][225][226][227][228].…”

Section: In Us Non-destructive Evaluationmentioning

confidence: 99%

Recent Advances in Machine Learning Applied to Ultrasound Imaging

Micucci

Iula

2022

Electronics

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Machine learning (ML) methods are pervading an increasing number of fields of application because of their capacity to effectively solve a wide variety of challenging problems. The employment of ML techniques in ultrasound imaging applications started several years ago but the scientific interest in this issue has increased exponentially in the last few years. The present work reviews the most recent (2019 onwards) implementations of machine learning techniques for two of the most popular ultrasound imaging fields, medical diagnostics and non-destructive evaluation. The former, which covers the major part of the review, was analyzed by classifying studies according to the human organ investigated and the methodology (e.g., detection, segmentation, and/or classification) adopted, while for the latter, some solutions to the detection/classification of material defects or particular patterns are reported. Finally, the main merits of machine learning that emerged from the study analysis are summarized and discussed.

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Section: In Us Non-destructive Evaluationmentioning

confidence: 99%

Recent Advances in Machine Learning Applied to Ultrasound Imaging

Micucci

Iula

2022

Electronics

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“…ML has been adopted in NDT over the past decade, and progress has been achieved and demonstrated in particular studies [17], [18], [19] The applications target the holistic assessment of structures in terms of data fusion concepts [20] and the analysis of specific inspection techniques [21] With a wide range of studies conducted, there is still an urgent need for development regarding applying ML to NDT of concrete structures and, more specifically, NDT methods using elastic waves. With powerful algorithms available, ML models can be tailored to specific applications.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Applications in Nondestructive Testing of Concrete Structures

Algernon,

Münch,

Muller

et al. 2023

ce papers

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Machine Learning bears great potential for data‐driven solutions in the field of nondestructive testing (NDT) of concrete structures. The analysis of the data collected with NDT methods, such as ultrasonics, impact‐echo and ground penetrating radar, can be complex and requires experience. The expected benefit of Machine Learning applications in this context goes beyond the increase of efficiency obtained by automating the analysis process. While traditional analysis approaches are usually solely based on key features according to the basic principles, Machine Learning algorithms can consider the full data content and reveal hidden correlations. For an organized approach to Machine Learning on NDT data, an analysis tool has been developed. The tool provides a graphical user interface to manage and label training/test data and interactively define the Deep Neural Network architecture. In particular, Convolutional Neural Networks, as proven successful in various image recognition tasks, are implemented. The Machine Learning concepts are demonstrated in show cases, comprising ultrasonic as well as impact‐echo applications. In particular, the relevance of a targeted preprocessing is addressed, comparing the effectiveness of time‐, frequency‐ and joint‐time‐frequency‐representations.

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“…Regarding ANNs, the most popular approach is to implement already trained networks (on a capable platform, like PC) to the embedded device. As ANNs are algorithms of relatively low complexity, a whole variety of such applications can be found in the literature, regarding classification of signals for arrythmia detection [ 16 ], evaluation of fiber–metal laminates [ 17 ], Secchi depth calculation [ 18 ], power factor correction [ 19 ], or hybrid simulation [ 20 ]. Implementing SVM algorithms to real-time systems or embedded systems is also a popular approach.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Machine Learning Algorithms for Structure State Prediction in Operational Load Monitoring

Mucha

2020

Sensors

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The aim of operational load monitoring is to make predictions about the remaining usability time of structures, which is extremely useful in aerospace industry where in-service life of aircraft structural components can be maximized, taking into account safety. In order to make such predictions, strain sensors are mounted to the structure, from which data are acquired during operational time. This allows to determine how many load cycles has the structure withstood so far. Continuous monitoring of the strain distribution of the whole structure can be complicated due to vicissitude nature of the loads. Sensors should be mounted in places where stress and strain accumulations occur, and due to experiencing variable loads, the number of required sensors may be high. In this work, different machine learning and artificial intelligence algorithms are implemented to predict the current safety factor of the structure in its most stressed point, based on relatively low number of strain measurements. Adaptive neuro-fuzzy inference systems (ANFIS), support-vector machines (SVM) and Gaussian processes for machine learning (GPML) are trained with simulation data, and their effectiveness is measured using data obtained from experiments. The proposed methods are compared to the earlier work where artificial neural networks (ANN) were proven to be efficiently used for reduction of the number of sensors in operational load monitoring processes. A numerical comparison of accuracy and computational time (taking into account possible real-time applications) between all considered methods is provided.

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Embedded decision support system for ultrasound nondestructive evaluation based on extreme learning machines

Cited by 8 publications

References 20 publications

Recent Advances in Machine Learning Applied to Ultrasound Imaging

Recent Advances in Machine Learning Applied to Ultrasound Imaging

Machine Learning Applications in Nondestructive Testing of Concrete Structures

Comparison of Machine Learning Algorithms for Structure State Prediction in Operational Load Monitoring

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