{"title":"Non-destructive Testing of Steel Wire Ropes Incorporating Magnetic Memory Information","authors":"Juwei Zhang, Zengguang Zhang, Bo Liu","doi":"10.1784/insi.2023.65.2.87","DOIUrl":null,"url":null,"abstract":"In order to avoid the influence of the interfering magnetic field, a wire rope magnetic memory detection platform under the excitation of a weak magnetic field is built and then the enhanced magnetic memory signal, infrared signal and visible light signal are fused to increase the recognition\n rate and reduce the identification error of the quantitative identification of broken wires, realising more effective defect identification and life assessment of wire ropes. The magnetic memory signal is denoised by applying intrinsic time-scale decomposition (ITD) and a wavelet algorithm\n to effectively remove noise such as the signal baseline and strand waves. The image fusion method based on curvelet transform is applied to realise pixel-level fusion of the defect images. The extracted fused image features are used as the input to the support vector machine optimised by the\n grey wolf optimiser (GWO-SVM) neural network to quantitatively identify wire rope defects. The results show that the image fusion method is better than the single detection method for broken wire identification.","PeriodicalId":344397,"journal":{"name":"Insight - Non-Destructive Testing and Condition Monitoring","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Insight - Non-Destructive Testing and Condition Monitoring","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1784/insi.2023.65.2.87","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
In order to avoid the influence of the interfering magnetic field, a wire rope magnetic memory detection platform under the excitation of a weak magnetic field is built and then the enhanced magnetic memory signal, infrared signal and visible light signal are fused to increase the recognition
rate and reduce the identification error of the quantitative identification of broken wires, realising more effective defect identification and life assessment of wire ropes. The magnetic memory signal is denoised by applying intrinsic time-scale decomposition (ITD) and a wavelet algorithm
to effectively remove noise such as the signal baseline and strand waves. The image fusion method based on curvelet transform is applied to realise pixel-level fusion of the defect images. The extracted fused image features are used as the input to the support vector machine optimised by the
grey wolf optimiser (GWO-SVM) neural network to quantitatively identify wire rope defects. The results show that the image fusion method is better than the single detection method for broken wire identification.