A review of ultrasonic sensing and machine learning methods to monitor industrial processes

Bowler, Alexander; Pound, Michael P.; Watson, Nick J.

doi:10.1016/j.ultras.2022.106776

Cited by 38 publications

(9 citation statements)

References 241 publications

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“…By comparing different machine-learning models (support vector regression (SVR), partial least-square regression (PLSR), neural network etc.) and by optimizing them [44], the approach shown in this study could be improved further. Another research direction would be to use the methodology developed in this study to measure further structural parameters (e.g., void fraction) of cereal-based foams [43] or to extend the method for further dough matrices.…”

Section: Discussionmentioning

confidence: 99%

Ultrasonic Wave Mode-Based Application for Contactless Density Measurement of Highly Aerated Batters

Metzenmacher

Geier

Becker

2023

Foods

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An ultrasonic wave mode-based method for density measurement in highly foamed batters was developed. Therefore, a non-contact ultrasonic sensor system was designed to generate signals for batch-wise processes. An ultrasonic sensor, containing a piezoelectric ceramic at the fundamental longitudinal frequency of 2 MHz, was used to take impedance measurements in pulse-echo mode. The ultrasonic signals were processed and analysed wave-mode wise, using a feature-driven approach. The measurements were carried out for different mixing times within a container, with the attached ultrasonic sensor. Within the biscuit batter, the change to the ultrasonic signals caused by density changes during the batter-mixing process was monitored (R2 = 0.96). The density range detected by the sensor ranges between 500 g/L and 1000 g/L. The ultrasonic sensor system developed also shows a reasonable level of accuracy for the measurements of biscuit batter variations (R2 > 0.94). The main benefit of this novel technique, which comprises multiple wave modes for signal features and combines these features with the relevant process parameters, leads to a more robust system as regards to multiple interference factors.

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Section: Discussionmentioning

confidence: 99%

Ultrasonic Wave Mode-Based Application for Contactless Density Measurement of Highly Aerated Batters

Metzenmacher

Geier

Becker

2023

Foods

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“…Many breakthroughs have been accomplished with deep learning in fields such as image, audio and video processing. [3] In non-destructive testing (NDT) artificial neural networks have also been applied successfully in many cases [4]. Typically, NDT measurement data is classified manually after some feature extraction.…”

Section: Related Workmentioning

confidence: 99%

Spatial and Temporal Deep Learning in Air-coupled Ultrasonic Testing for Enabling NDE 4.0

Schmid

Dürrmeier

Große

2022

Preprint

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Air-coupled ultrasonic (ACU) testing has been used for several years to detect defects in plate-like structures. Especially, for automated testing procedures, ACU testing is advantageous in comparison to conventional testing. However, the evaluation of the measurement data is usually done in a manual manner, which is an obstruction to the application of ACU testing. The goal of this study is to automate and improve defect characterization and NDE 4.0 accordingly with deep learning. In conventional ACU testing the measurement data contains temporal (A-scans) and spatial (C-scans) information. Both data types are investigated in this study. For the A-scans, which represent time series data, neural networkarchitectures tailored to such data types are applied. In addition, it is evaluated if further adaptions of the training procedure increase the performance. The C-scans are segmented by applying differentU-net similar architectures and training strategies. In order to use spatial and temporal information, a further approach is taken. The prediction of the time series models is segmented with imagemodels. To the performance of all trained models and training strategies is compared with the F1-score. As specimens, artificial defects in acrylic and carbon fiber-reinforced polymers plates are investigated.

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“…Also, recent reviews comparing different AI algorithms indicated that DNN has the distinguishable property of automatically extracting features from input signals. 40,41 The aim of this work is to propose thin patch-type piezoelectric sensors for use in the water immersion test for the inspection of closely arranged HEs. Water within HE is the main indicator of a defective HE, regardless of the type of defect.…”

Section: Introductionmentioning

confidence: 99%

“…Also, recent reviews comparing different AI algorithms indicated that DNN has the distinguishable property of automatically extracting features from input signals. 40 , 41 …”

Section: Introductionmentioning

confidence: 99%

A novel ultrasonic inspection method of the heat exchangers based on circumferential waves and deep neural networks

et al. 2023

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The heat exchanger (HE) is an important component of almost every energy generation system. Periodic inspection of the HEs is particularly important to keep high efficiency of the entire system. In this paper, a novel ultrasonic water immersion inspection method is presented based on circumferential wave (CW) propagation to detect defective HE. Thin patch-type piezoelectric elements with multiple resonance frequencies were adopted for the ultrasonic inspection of narrow-spaced HE in an immersion test. Water-filled HE was used to simulate defective HE because water is the most reliable indicator of the defect. The HE will leak water no matter what the defect pattern is. Furthermore, continuous wavelet transform (CWT) was used to investigate the received CW, and inverse CWT was applied to separate frequency bands corresponding to the thickness and lateral resonance modes of the piezoelectric element. Different arrangements of intact and leaky HE were tested with several pairs of thin piezoelectric patch probes in various instrumental setups. Also, direct waveforms in the water without HE were used as reference signals, to indicate instrumental gain and probe sensitivity. Moreover, all filtered CW corresponding to resonance modes together with the direct waveforms in the water were used to train the deep neural networks (DNNs). As a result, an automatic HE state classification method was obtained, and the accuracy of the applied DNN was estimated as 99.99%.

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A review of ultrasonic sensing and machine learning methods to monitor industrial processes

Cited by 38 publications

References 241 publications

Ultrasonic Wave Mode-Based Application for Contactless Density Measurement of Highly Aerated Batters

Ultrasonic Wave Mode-Based Application for Contactless Density Measurement of Highly Aerated Batters

Spatial and Temporal Deep Learning in Air-coupled Ultrasonic Testing for Enabling NDE 4.0

A novel ultrasonic inspection method of the heat exchangers based on circumferential waves and deep neural networks

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