Development of Hybrid Models Based on AlexNet and Machine Learning Approaches for Strip Steel Surface Defect Classification

Boudiaf, Adel; Benlahmidi, Said; Dahane, Amine; Bouguettaya, Abdelmalek

doi:10.1007/s11668-024-01927-5

J Fail. Anal. and Preven.

2024

DOI: 10.1007/s11668-024-01927-5

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Development of Hybrid Models Based on AlexNet and Machine Learning Approaches for Strip Steel Surface Defect Classification

Adel Boudiaf,

Said Benlahmidi,

Amine Dahane

et al.

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An accurate localization method for underground pipeline leakage points in chemical parks based on ultrasonic creep wave flaw detection and data integration

Wang,

Li,

Sun

et al. 2024

Acta Acust.

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To ensure the reliable management of the safe operation of an underground pipeline in a chemical industrial park and accurately determine the location of the pipeline leakage point, a precise location method for the leakage point of an underground pipeline in a chemical industrial park based on ultrasonic creeping wave flaw detection and data integration is proposed. In this method, the ultrasonic creeping wave flaw detector is used to detect the underground pipelines in the chemical industry park and obtain the status signals of the underground pipelines in the chemical industry park. The improved K-means algorithm is used to fuse the signals collected by clustering to obtain abnormal signals in the signals; The wavelet transform is used to process the ultrasonic abnormal signals obtained by clustering, extract the local time energy density characteristics of the frequency band, and superimpose the image. By introducing image enhancement factors, the leakage area can be centrally located, the coordinates of the pipeline leakage points can be determined, and the leakage degree can be determined by calculating the sum of the damage probability of each leakage point. The test results show that the clustering effect of this method is good, and the separability measurement indexes are all above 0.925, which can complete the location of single point and multi-point leakage points, and the maximum location error is 11.55 mm. The reliability of the location of the leakage point is high.

show abstract

An accurate localization method for underground pipeline leakage points in chemical parks based on ultrasonic creep wave flaw detection and data integration

Wang,

Li,

Sun

et al. 2024

Acta Acust.

View full text Add to dashboard Cite

show abstract

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Development of Hybrid Models Based on AlexNet and Machine Learning Approaches for Strip Steel Surface Defect Classification

Cited by 1 publication

References 39 publications

An accurate localization method for underground pipeline leakage points in chemical parks based on ultrasonic creep wave flaw detection and data integration

An accurate localization method for underground pipeline leakage points in chemical parks based on ultrasonic creep wave flaw detection and data integration

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