Multi-Harmonic Nonlinear Ultrasonic Fusion with Deep Learning for Subtle Parameter Identification of Micro-Crack Groups
Fatigue crack defects in metallic materials significantly reduce the remaining useful life (RUL) of parts. However, much of the existing research has focused on identifying single-millimeter-scale cracks using individual nonlinear ultrasonic responses. The identification of subtle parameters from co...
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MDPI AG
2025-02-01
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| author | Qi Lin Xiaoyang Bi Xiangyan Ding Bo Yang Bingxi Liu Xiao Yang Jie Xue Mingxi Deng Ning Hu |
| author_facet | Qi Lin Xiaoyang Bi Xiangyan Ding Bo Yang Bingxi Liu Xiao Yang Jie Xue Mingxi Deng Ning Hu |
| author_sort | Qi Lin |
| collection | DOAJ |
| description | Fatigue crack defects in metallic materials significantly reduce the remaining useful life (RUL) of parts. However, much of the existing research has focused on identifying single-millimeter-scale cracks using individual nonlinear ultrasonic responses. The identification of subtle parameters from complex ultrasonic responses of micro-crack groups remains a significant challenge in the field of nondestructive testing. We propose a novel multi-harmonic nonlinear response fusion identification method integrated with a deep learning (DL) model to identify the subtle parameters of micro-crack groups. First, we trained a one-dimensional convolutional neural network (1D CNN) with various time-domain signals obtained from finite element method (FEM) models and analyzed the sensitivity of different harmonic nonlinear responses to various subtle parameters of micro-crack groups. Then, high harmonics were fused to perform a decoupled identification of multiple subtle parameters. We enhanced the Dempster–Shafer (DS) evidence theory used in decision fusion by accounting for different sensitivities, achieving an identification accuracy of 93.73%. Building on this, we assigned sensor weights based on our proposed new conflict measurement method and further conducted decision fusion on the decision results from multiple ultrasonic sensors. Our proposed method achieves an identification accuracy of 95.68%. |
| format | Article |
| id | doaj-art-5721ef5a1bbe47fe9323bdef85fd8ef9 |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
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| series | Sensors |
| spelling | doaj-art-5721ef5a1bbe47fe9323bdef85fd8ef92025-08-20T03:12:15ZengMDPI AGSensors1424-82202025-02-01254115210.3390/s25041152Multi-Harmonic Nonlinear Ultrasonic Fusion with Deep Learning for Subtle Parameter Identification of Micro-Crack GroupsQi Lin0Xiaoyang Bi1Xiangyan Ding2Bo Yang3Bingxi Liu4Xiao Yang5Jie Xue6Mingxi Deng7Ning Hu8School of Mechanical Engineering, Hebei University of Technology, Tianjin 300401, ChinaSchool of Mechanical Engineering, Hebei University of Technology, Tianjin 300401, ChinaSchool of Mechanical Engineering, Hebei University of Technology, Tianjin 300401, ChinaSchool of Mechanical Engineering, Hebei University of Technology, Tianjin 300401, ChinaTianjin Fire Science and Technology Research Institute of Ministry of Emergency Management, Tianjin 300381, ChinaSchool of Mechanical Engineering, Hebei University of Technology, Tianjin 300401, ChinaSchool of Mechanical Engineering, Hebei University of Technology, Tianjin 300401, ChinaCollege of Aerospace Engineering, Chongqing University, Chongqing 400044, ChinaSchool of Mechanical Engineering, Hebei University of Technology, Tianjin 300401, ChinaFatigue crack defects in metallic materials significantly reduce the remaining useful life (RUL) of parts. However, much of the existing research has focused on identifying single-millimeter-scale cracks using individual nonlinear ultrasonic responses. The identification of subtle parameters from complex ultrasonic responses of micro-crack groups remains a significant challenge in the field of nondestructive testing. We propose a novel multi-harmonic nonlinear response fusion identification method integrated with a deep learning (DL) model to identify the subtle parameters of micro-crack groups. First, we trained a one-dimensional convolutional neural network (1D CNN) with various time-domain signals obtained from finite element method (FEM) models and analyzed the sensitivity of different harmonic nonlinear responses to various subtle parameters of micro-crack groups. Then, high harmonics were fused to perform a decoupled identification of multiple subtle parameters. We enhanced the Dempster–Shafer (DS) evidence theory used in decision fusion by accounting for different sensitivities, achieving an identification accuracy of 93.73%. Building on this, we assigned sensor weights based on our proposed new conflict measurement method and further conducted decision fusion on the decision results from multiple ultrasonic sensors. Our proposed method achieves an identification accuracy of 95.68%.https://www.mdpi.com/1424-8220/25/4/1152convolutional neural networkinformation fusionmicro-crack groups identificationnondestructive testingultrasonic nonlinearity |
| spellingShingle | Qi Lin Xiaoyang Bi Xiangyan Ding Bo Yang Bingxi Liu Xiao Yang Jie Xue Mingxi Deng Ning Hu Multi-Harmonic Nonlinear Ultrasonic Fusion with Deep Learning for Subtle Parameter Identification of Micro-Crack Groups Sensors convolutional neural network information fusion micro-crack groups identification nondestructive testing ultrasonic nonlinearity |
| title | Multi-Harmonic Nonlinear Ultrasonic Fusion with Deep Learning for Subtle Parameter Identification of Micro-Crack Groups |
| title_full | Multi-Harmonic Nonlinear Ultrasonic Fusion with Deep Learning for Subtle Parameter Identification of Micro-Crack Groups |
| title_fullStr | Multi-Harmonic Nonlinear Ultrasonic Fusion with Deep Learning for Subtle Parameter Identification of Micro-Crack Groups |
| title_full_unstemmed | Multi-Harmonic Nonlinear Ultrasonic Fusion with Deep Learning for Subtle Parameter Identification of Micro-Crack Groups |
| title_short | Multi-Harmonic Nonlinear Ultrasonic Fusion with Deep Learning for Subtle Parameter Identification of Micro-Crack Groups |
| title_sort | multi harmonic nonlinear ultrasonic fusion with deep learning for subtle parameter identification of micro crack groups |
| topic | convolutional neural network information fusion micro-crack groups identification nondestructive testing ultrasonic nonlinearity |
| url | https://www.mdpi.com/1424-8220/25/4/1152 |
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