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

Malikov, Azamatjon Kakhramon ugli; Cho, Younho; Kim, Young H.; Kim, Jeongnam; Kim, Hyung-Kyu

doi:10.1177/00368504221146081

Cited by 8 publications

(3 citation statements)

References 66 publications

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“…Although these methods are popular due to their statistical background, interpretability, and strong ability to handle process data collinearity, their inherent adaptability to linear relationships makes it difficult to handle highly nonlinear problems in industrial processes. In order to more effectively address the highly nonlinear characteristics of industrial process data, researchers are gradually turning to soft sensing models based on support vector machines (SVM) 11,12 and artificial neural networks [13][14][15] . Compared to traditional models, these models have strong nonlinear modeling capabilities and are better able to capture complex nonlinear relationships, making them suitable for processing high-dimensional data and strong nonlinear correlations.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Soft Sensor of Stacking Approximate Kernel- Based Broad Learning System for Batch Process

Zhao,

Yang,

et al. 2024

Preprint

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To deal with the highly nonlinear and time-varying characteristics of batch process, a model named Moving Window Stacking Approximate Kernel-Based Broad Learning System (MW-Stacking-AKBLS) is proposed in this paper. This model innovatively introduces the AKBLS algorithm and the MW-Stacking framework, giving it strong nonlinear fitting ability, excellent generalization ability, and adaptive ability. The Broad Learning System (BLS) is known for its shorter training time for effective nonlinear processing, but the uncertainty brought by its double random mapping results in poor resistance to noisy data and unpredictable impact on performance. To address this issue, this paper proposes an AKBLS algorithm that reduces uncertainty, eliminates redundant features, and improves prediction accuracy by projecting feature nodes into the kernel space. It also significantly reduces the computation time of the kernel matrix by searching for approximate kernels to enhance its ability in industrial online applications. Extensive comparative experiments on various public datasets of different sizes validate this. The MW-Stacking framework adopts the Stacking ensemble learning method, integrating multiple ABKLS models to enhance the model's generalization ability. Additionally, by adopting the moving window method, the model has been equipped with adaptive ability to better adapt to slow changes in industrial batch process. Finally, comparative experimental results on a substantial dataset of penicillin simulations indicate a significant improvement in predictive accuracy for the proposed MW-Stacking AKBLS model compared to other commonly used algorithms.

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Section: Introductionmentioning

confidence: 99%

Adaptive Soft Sensor of Stacking Approximate Kernel- Based Broad Learning System for Batch Process

Zhao,

Yang,

et al. 2024

Preprint

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“…A Lamb wave is a type of guided wave that can propagate for a long distance as well as its sensitivity to various types of defects. The Lamb wave can be used for the inspection of plate corrosion, flaws, debonding, and other kinds of defects (Zhang et al 2023 ; Malikov et al 2023 ). In addition, Lamb waves can propagate along curved structures as well, making them useful for monitoring the structural health of curved structures.…”

Section: Introductionmentioning

confidence: 99%

Ultrasonic tomography imaging enhancement approach based on deep convolutional neural networks

Malikov

Cuenca

Kim

et al. 2023

J Vis

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The containment liner plate (CLP) is a thin layer of carbon steel material applied as a base for concrete structures protecting nuclear material. The structural health monitoring of the CLP is critical to ensure the safety of nuclear power plants. Hidden defects in the CLP can be identified utilizing ultrasonic tomographic imaging techniques such as the reconstruction algorithm for the probabilistic inspection of damage (RAPID) methodology. However, Lamb waves have a multimodal dispersion feature, which makes the selection of a single mode more difficult. Thus, sensitivity analysis was utilized since it allows for the determination of each mode's level of sensitivity as a function of frequency; the S0 mode was chosen after examining the sensitivity. Even though proper Lamb wave mode was selected, the tomographic image had blurred zones. Blurring reduces the precision of an ultrasonic image and makes it more difficult to distinguish the dimensions of the flaw. To enhance the tomographic image of the CLP, deep learning architecture such as U-Net was utilized for the segmentation of the experimental ultrasonic tomographic image, which includes an encoder and decoder part for better visualization of the tomographic image. Nevertheless, collecting enough ultrasonic images to train the U-Net model was not economically feasible, and only a small number of the CLP specimens can be tested. Thus, it was necessary to utilize transfer learning and get the values of the parameters from a pre-trained model with a much larger dataset as a starting point for a new task, rather than training a new model from scratch. Through these deep learning approaches, we were able to eliminate the blurred section of the ultrasonic tomography, leading to images with clear edges of defects and no blurred zones. Graphical abstract

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“…To better handle non-linear problems, researchers have proposed soft sensing models based on support vector machines (SVM) [17,18] and artificial neural networks. [19][20][21][22][23] For instance, a methodology of artificial neural correlation analysis (ANCA), introduced by Chen et al, [24] offers an effective solution for addressing intricate non-linear scenarios within industrial processes. These models exhibit strong non-linear modelling capabilities, allowing them to capture non-linear relationships more effectively and handle high-dimensional data and non-linear correlations.…”

mentioning

confidence: 99%

Soft sensor based on multi‐phase stacking ensemble model with self‐selected primary learner for batch processes

Zhao,

Yang,

et al. 2023

Can J Chem Eng

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In batch processes, existing soft sensing methodologies encounter substantial challenges when confronted with nonlinearity and multi‐phase issues. In response to these challenges, an innovative soft sensing framework known as the multi‐phase stacking ensemble model with self‐selected primary learner is proposed. The main innovation of this framework lies in the solution to the primary learner selection issue within the stacking model. To commence, the batch process is divided into multiple phases employing a Gaussian mixture model, thereby establishing local ensemble models for each phase. Subsequently, the iterative self‐selection of primary learners strategy is proposed, which iteratively selects suitable primary learners for these models, optimizing their combination of primary learners for each local model. This primary learner selection strategy effectively enhances the accuracy of predictions in the stacking ensemble model. To further enhance the performance, Bayesian optimization is utilized to tune the hyperparameters of each local ensemble model. This step guarantees optimal performance of the model across diverse phases. Extensive simulation experiments are conducted on an industrial penicillin fermentation process to validate the effectiveness of the proposed framework. According to the findings, the model demonstrated superior performance compared to existing single‐learner soft sensing methods and commonly utilized ensemble‐based soft sensing methods in terms of both R2 score (0.97584) and RMSE (0.0513). Overall, this framework offers a novel approach for selecting primary learners in stacking ensemble models and enhancing the predictive performance in batch processes for soft sensing.

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

Cited by 8 publications

References 66 publications

Adaptive Soft Sensor of Stacking Approximate Kernel- Based Broad Learning System for Batch Process

Adaptive Soft Sensor of Stacking Approximate Kernel- Based Broad Learning System for Batch Process

Ultrasonic tomography imaging enhancement approach based on deep convolutional neural networks

Soft sensor based on multi‐phase stacking ensemble model with self‐selected primary learner for batch processes

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