Recognition Method of Pipeline Weld Defects Based on Auxiliary Classifier Generative Adversarial Networks

Lang, Xianming

doi:10.1109/mim.2022.9756386

Cited by 5 publications

(3 citation statements)

References 21 publications

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“…(5) End when it is lower than the set minimum RMSE or reaches the maximum number of iterations; otherwise, return to step (2).…”

Section: Pso Optimization Algorithmmentioning

confidence: 99%

“…However, due to the high risk coefficient of material transportation, man-made destruction, natural environment and other factors [1], there are still many safety problems. Once leaked, the surrounding environment will be polluted, and personal safety may be affected [2]. Therefore, it is very important to monitor the pipeline in real time and detect leakage in time.…”

Section: Introductionmentioning

confidence: 99%

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Abnormal diagnosis of acoustic emission pipeline working conditions based on PSO-LSTM-DAE

Sui

Lang

2023

Eng. Res. Express

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To solve the problem of difficult identification of pipeline working conditions, acoustic emission was used to extract abnormal pipeline data, and a PSO-Lstm-DAE model was proposed to classify and identify abnormal working conditions of acoustic emission pipelines.The algorithm took advantage of the timing characteristics of LSTM and the noise reduction ability of DAE, and set the optimal superparameters through PSO. In this paper, four commonly used abnormal condition detection data sets were collected, and algorithm tests were carried out on the data sets and compared with other anomaly detection algorithms. The classification accuracy of the proposed PSO-LSTM-DAE model was 95.68%. The results of multiple indexes show that the PSO-LSTM-DAE model proposed in this paper has significant advantages in the diagnosis of abnormal pipeline conditions.

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“…(5) End when it is lower than the set minimum RMSE or reaches the maximum number of iterations; otherwise, return to step (2).…”

Section: Pso Optimization Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Abnormal diagnosis of acoustic emission pipeline working conditions based on PSO-LSTM-DAE

Sui

Lang

2023

Eng. Res. Express

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“…Dai et al 7 used GAN-based data augmentation to improve the performance of spot welding defects classification. Lang 8 proposed a data augmentation method, which was combined the generative adversarial network with the time-frequency graph of magnetic flux leakage (MFL) signals.…”

Section: Introductionmentioning

confidence: 99%

A study on spot welding quality judgment of stainless steel plate based on semi-supervised conditional generation adversarial network

Wang

2023

Advances in Mechanical Engineering

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During RSW, the number of qualified samples is much more than the unqualified ones, forming an unbalanced set, thus affecting the training effect of the model, meanwhile, most samples are unlabeled, and if all the joints are marked, it is more expensive. Based on this, a method of spot welding quality judgment of stainless steel plate based on semi-supervised conditional generation adversarial network is proposed. Firstly, labels are added to the noise to generate labeled data and unlabeled data, which are mixed in a certain proportion to ensure the diversity of generated data. Then, the real data is divided into two parts, in which the unlabeled part plays a game with the generated data to generate samples as close to the real as possible, meanwhile, the data with true discriminant results and labeled data are input into the autoencoder to obtain the feature vectors set of different states. Finally, the training parameters and test samples are input into the classifier to obtain the judgment results. The proposed method was applied to application case, and the results showed that it not only had a good fitting effect, but also had a high classification accuracy. Consequently, the method proposed was effective.

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Recent progress of sensing and machine learning technologies for process monitoring and defects detection in wire arc additive manufacturing

Guo,

Zhang,

Pan

et al. 2024

Journal of Manufacturing Processes

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Recognition Method of Pipeline Weld Defects Based on Auxiliary Classifier Generative Adversarial Networks

Cited by 5 publications

References 21 publications

Abnormal diagnosis of acoustic emission pipeline working conditions based on PSO-LSTM-DAE

Abnormal diagnosis of acoustic emission pipeline working conditions based on PSO-LSTM-DAE

A study on spot welding quality judgment of stainless steel plate based on semi-supervised conditional generation adversarial network

Recent progress of sensing and machine learning technologies for process monitoring and defects detection in wire arc additive manufacturing

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