Generative Adversarial Networks for Data Generation in Structural Health Monitoring

Luleci, Furkan; Çatbaş, F. Necati; Avcı, Onur

doi:10.3389/fbuil.2022.816644

Cited by 10 publications

(5 citation statements)

References 46 publications

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“…In this study, the groove image generated by WGAN-GP is inserted into the disease-free road image to generate new training data. Firstly, the Poisson algorithm is used to output the fused image [ 44 ], and then the synthesized image is output by the color migration algorithm [ 45 ]. When integrating the image, the image should be integrated as real as possible.…”

Section: Methodsmentioning

confidence: 99%

PDNet: Improved YOLOv5 Nondeformable Disease Detection Network for Asphalt Pavement

Yang

Luo

2022

Computational Intelligence and Neuroscience

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In the daily inspection task of the expressway, accuracy and speed are the two most important indexes to reflect the detection efficiency of nondeformation diseases of asphalt pavement. To achieve model compression, accelerated detection, and accurate identification under multiscale conditions, a lightweight algorithm (PDNet) based on improved YOLOv5 is proposed. The algorithm is improved based on the network structure of YOLOv5, and the improved network structure is called YOLO-W. Firstly, a novel cross-layer weighted cascade aggregation network (W-PAN) is proposed to replace the original YOLOv5 network. Secondly, more economical GhostC3 and ShuffleConv modules are designed to replace C3 and Conv modules in the original network model. In terms of parameter setting, CIoU is selected as the loss function of the model, and the K-Means ++ algorithm is used for anchor box clustering. Before the model training, the confrontation generation network (GAN) and Poisson migration fusion algorithm (Poisson) are used for data enhancement and the negative sample training (NST) method is used to improve the robustness of the model. Finally, Softer-NMS is used to remove the prediction box in the prediction stage. Seven common asphalt pavement disease data sets (FAFU-PD) are constructed at the same time. Compared with the original YOLOv5 algorithm, PDNet improves the scores of FAFU-PD data sets on F1-score by 10 percentage points and FPS by 77.5%.

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Section: Methodsmentioning

confidence: 99%

PDNet: Improved YOLOv5 Nondeformable Disease Detection Network for Asphalt Pavement

Yang

Luo

2022

Computational Intelligence and Neuroscience

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“…To identify and clarify the position of GANs in the civil SHM field, in terms of the type of GAN applications studied by the researchers, an illustrative figure is made (Figure 9) which shows the classification of the applications of GANs in civil SHM and the corresponding studies with the GAN models used in each study. As the main concept of GAN is to learn data domain and data generation, some studies solely studied data generation [ (Kanghyeok and do Hyoung, 2019;Xiong and Chen, 2019;Zhang and Wang, 2019;Tsialiamanis et al, 2020;Xu et al, 2021;Yu et al, 2021;Tsialiamanis et al, 2022a;Heesch et al, 2021;Colombera et al, 2021;Luleci et al, 2022b;Luleci et al, 2023)] (a total of 11 studies) by using original GAN or other GAN variants. Thus, the data generation category is separated from other categories.…”

Section: Generative Adversarial Network In Civil Structural Health Mo...mentioning

confidence: 99%

“…The authors concluded that the reconstructed data is in good agreement with the actual dataset. Luleci et al (2022b) pointed out the problem of data scarcity in SHM and employed the WDCGAN-GP (or Deep Convolutional WGAN-GP) model to tackle the challenge. In that study, they first trained the model with acceleration data whose domain is damaged (bolt-loosening).…”

Section: Studies Published In 2022 (8 Papers)mentioning

confidence: 99%

A literature review: Generative adversarial networks for civil structural health monitoring

Luleci

Çatbaş

Avcı

2022

Front. Built Environ.

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Structural Health Monitoring (SHM) of civil structures has been constantly evolving with novel methods, advancements in data science, and more accessible technology to address issues related to structural safety, operations, and resiliency. Research and development in the civil SHM field during the last few decades have been progressive due to the increasing use of Artificial Intelligence (AI) methods such as Machine Learning (ML) and Deep Learning (DL). Particularly, Generative Adversarial Networks (GAN), which is a subfamily of Deep Learning has been highly favored in the SHM community within the last couple of years. After its release in 2014, GANs (original GAN and other GAN variants) have been in use for a wide variety of applications in various disciplines, and it has been one of the most popular research topics in the AI-ML domain. While there has not been a review study on the applications of GAN in the civil SHM field, this paper aims to fill this gap by presenting a literature review of the studies that employed GAN specifically in civil SHM applications from 2014 to date, in a condensed format. This study intends to inform SHM practitioners and researchers about GANs and present the highlights of the published work on GANs in the civil SHM field.

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“…Wang et al monitored the operating status of wind turbines using a least squares GAN [27]. Luleci et al generated vibration datasets related to real damages in civil structures using a one-dimensional GAN with gradient penalty, and they applied this approach to vibration-based damage diagnosis [28]. These applications demonstrate the strength of GAN models.…”

Section: Introductionmentioning

confidence: 99%

Similarity indicator and CG-CGAN prediction model for remaining useful life of rolling bearings

Yang,

Binbin,

Cancan

et al. 2024

Meas. Sci. Technol.

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To tackle the challenges of performing early fault warning and improving the prediction accuracy for the remaining useful life (RUL) of rolling bearings, this paper proposes a similarity health indicator and a predictive model of CG-CGAN, which relies on a Conditional Generative Adversarial Network (CGAN) that combines one-dimensional Convolutional Neural Network (CNN) with a Bidirectional Gate Recurrent Unit (Bi-GRU). This framework provides a comprehensive theoretical foundation for RUL prediction of rolling bearings. The similarity health indicator allows for early fault warning of rolling bearings without expert knowledge. Within the CGAN framework, the inclusion of constraints guides the generation of samples in a more targeted manner. Additionally, the proposed CG-CGAN model incorporates Bi-GRU to consider both forward and backward information, thus improving the precision of RUL forecasting. Firstly, the similarity indicator between the vibration signals of the rolling bearing over its full life span and the standard vibration signals (healthy status) is calculated. This indicator helps to determine the early deterioration points of the rolling bearings. Secondly, the feature matrix composed of traditional health indicators and similarity health indicator, is utilized to train and test the proposed CG-CGAN model for RUL prediction. Finally, to corroborate the efficacy of the proposed method, two sets of real experiment data of rolling bearing accelerated life from the Intelligent Maintenance Systems (IMS) are utilized. Experimental findings substantiate that the proposed similarity health indicator offers precocious fault alerts and precisely delineates the performance diminution of the rolling bearing. Furthermore, the put-forward CG-CGAN model achieves high-precision RUL prediction of rolling bearing.

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Generative Adversarial Networks for Data Generation in Structural Health Monitoring

Cited by 10 publications

References 46 publications

PDNet: Improved YOLOv5 Nondeformable Disease Detection Network for Asphalt Pavement

PDNet: Improved YOLOv5 Nondeformable Disease Detection Network for Asphalt Pavement

A literature review: Generative adversarial networks for civil structural health monitoring

Similarity indicator and CG-CGAN prediction model for remaining useful life of rolling bearings

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