{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T22:22:59Z","timestamp":1761171779627,"version":"build-2065373602"},"reference-count":44,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2025,10,20]],"date-time":"2025-10-20T00:00:00Z","timestamp":1760918400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62071123","61601125"],"award-info":[{"award-number":["62071123","61601125"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100003392","name":"Natural Science Foundation of Fujian Province","doi-asserted-by":"crossref","award":["2024J01971","2020J01312"],"award-info":[{"award-number":["2024J01971","2020J01312"]}],"id":[{"id":"10.13039\/501100003392","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Accurate thickness recognition plays a vital role in safeguarding the structural reliability of critical assets. Pulse eddy current testing (PECT), as a non-destructive method that is both non-contact and insensitive to surface coatings, provides an efficient pathway for this purpose. Nevertheless, the complex, nonstationary, and nonlinear characteristics of PECT signals make it difficult for conventional models to jointly capture localized high-frequency patterns and long-range temporal dependencies, thereby constraining their prediction performance. To overcome these issues, we introduce a novel deep learning framework, multi-scale residual dilated convolution, and bidirectional long short-term memory with a squeeze-and-excitation mechanism (MRDC-BiLSE) for PECT time series analysis. The architecture integrates a multi-scale residual dilated convolution block. By combining dilated convolutions with residual connections at different scales, this block captures structural patterns across multiple temporal resolutions, leading to more comprehensive and discriminative feature extraction. Furthermore, to better exploit temporal dependencies, the BiLSTM-SE module combines bidirectional modeling with a squeeze-and-excitation mechanism, resulting in more discriminative feature representations. Experiments on experimental PECT datasets confirm that MRDC-BiLSE surpasses existing methods, showing applicability for real-world thickness recognition.<\/jats:p>","DOI":"10.3390\/info16100919","type":"journal-article","created":{"date-parts":[[2025,10,20]],"date-time":"2025-10-20T13:54:41Z","timestamp":1760968481000},"page":"919","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Towards Accurate Thickness Recognition from Pulse Eddy Current Data Using the MRDC-BiLSE Network"],"prefix":"10.3390","volume":"16","author":[{"given":"Wenhui","family":"Chen","sequence":"first","affiliation":[{"name":"Key Laboratory of Nondestructive Testing, Fujian Polytechnic Normal University, Fuqing 350300, China"}]},{"given":"Hong","family":"Zhang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Nondestructive Testing, Fujian Polytechnic Normal University, Fuqing 350300, China"}]},{"given":"Yiran","family":"Peng","sequence":"additional","affiliation":[{"name":"Faculty of Innovation Engineering, Macau University of Science and Technology, Avenida Wai Long, Macau 999078, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-7735-157X","authenticated-orcid":false,"given":"Benhuang","family":"Liu","sequence":"additional","affiliation":[{"name":"Faculty of Innovation Engineering, Macau University of Science and Technology, Avenida Wai Long, Macau 999078, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6161-2954","authenticated-orcid":false,"given":"Shunwu","family":"Xu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Nondestructive Testing, Fujian Polytechnic Normal University, Fuqing 350300, China"}]},{"given":"Hao","family":"Yan","sequence":"additional","affiliation":[{"name":"Key Laboratory of Nondestructive Testing, Fujian Polytechnic Normal University, Fuqing 350300, China"}]},{"given":"Jian","family":"Zhang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Nondestructive Testing, Fujian Polytechnic Normal University, Fuqing 350300, China"}]},{"given":"Zhaowen","family":"Chen","sequence":"additional","affiliation":[{"name":"Key Laboratory of Nondestructive Testing, Fujian Polytechnic Normal University, Fuqing 350300, China"},{"name":"Faculty of Innovation Engineering, Macau University of Science and Technology, Avenida Wai Long, Macau 999078, China"}]}],"member":"1968","published-online":{"date-parts":[[2025,10,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"112430","DOI":"10.1016\/j.ymssp.2025.112430","article-title":"Physics-based deep learning framework for Terahertz thickness measurement of thermal barrier coatings with variable refractive index","volume":"228","author":"Sun","year":"2025","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"102888","DOI":"10.1016\/j.ndteint.2023.102888","article-title":"Inductance-to-digital converters (LDC) based integrative multi-parameter eddy current testing sensors for NDT&E","volume":"138","author":"Tian","year":"2023","journal-title":"NDT E Int."},{"key":"ref_3","first-page":"2286","article-title":"Advances in applications of Non-Destructive Testing (NDT): A review","volume":"8","author":"Gupta","year":"2022","journal-title":"Adv. Mater. Process. Technol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"3517","DOI":"10.1080\/10589759.2024.2403701","article-title":"Near-and remote-field pulsed eddy current integrated testing for enhancement in imaging of buried flaws in layered conductive structures","volume":"40","author":"Su","year":"2025","journal-title":"Nondestruct. Test. Eval."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"4024","DOI":"10.1080\/10589759.2024.2413693","article-title":"Modelling and experimental study on pipeline defect characterisations using a pulsed eddy current measurement","volume":"40","author":"Fang","year":"2024","journal-title":"Nondestruct. Test. Eval."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"111910","DOI":"10.1016\/j.measurement.2022.111910","article-title":"A sensitivity enhancement PEC method for bottom flaws and corrosions detection","volume":"202","author":"Huang","year":"2022","journal-title":"Measurement"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"5012","DOI":"10.1109\/TIA.2024.3362918","article-title":"The Development and Performance Testing of a V\/f Control for Induction Motors Fed by Wavelet Modulated Power Electronic Converters","volume":"60","author":"Saleh","year":"2024","journal-title":"IEEE Trans. Ind. Appl."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Romero-Arismendi, N.O., Olivares-Galvan, J.C., Hernandez-Avila, J.L., Escarela-Perez, R., Jimenez-Mondragon, V.M., and Gonzalez-Monta\u00f1ez, F. (2024). Past, present, and future of new applications in utilization of eddy currents. Technologies, 12.","DOI":"10.3390\/technologies12040050"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Syasko, M., Solomenchuk, P., Soloviev, I., and Ampilova, N. (2023). A technique for multi-parameter signal processing of an eddy-current probe for measuring the thickness of non-conductive coatings on non-magnetic electrically conductive base metals. Appl. Sci., 13.","DOI":"10.3390\/app13085144"},{"key":"ref_10","first-page":"1","article-title":"Experimental investigation of low-frequency and pulsed eddy current testing in thickness measurement","volume":"39","author":"Ge","year":"2024","journal-title":"Nondestruct. Test. Eval."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"102333","DOI":"10.1016\/j.ndteint.2020.102333","article-title":"Measurement of coating thickness using lift-off point of intersection features from pulsed eddy current signals","volume":"116","author":"Wang","year":"2020","journal-title":"NDT E Int."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Citrin, D.S. (2021). Effects of Timing Noise on Square-Wave Optoelectronic Oscillators. Appl. Sci., 11.","DOI":"10.3390\/app112412038"},{"key":"ref_13","first-page":"1","article-title":"A novel data-driven auto compensation algorithm for pulsed eddy current inspection of high voltage feeder cable pipe","volume":"40","author":"Huang","year":"2025","journal-title":"Nondestruct. Test. Eval."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"6009304","DOI":"10.1109\/LSENS.2024.3424910","article-title":"Evaluation of pulse eddy current for autonomous airborne inspections","volume":"8","author":"Zhao","year":"2024","journal-title":"IEEE Sens. Lett."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"528","DOI":"10.1109\/JAS.2025.125117","article-title":"Multi-Scale Time Series Segmentation Network Based on Eddy Current Testing for Detecting Surface Metal Defects","volume":"12","author":"Li","year":"2025","journal-title":"IEEE\/CAA J. Autom. Sin."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"103247","DOI":"10.1016\/j.ndteint.2024.103247","article-title":"Analyzing the permeability distribution of multilayered specimens using pulsed eddy-current testing with multi-scale 1D-ResNet","volume":"149","author":"Zheng","year":"2025","journal-title":"NDT E Int."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Meng, B., Zhuang, Z., Ma, J., and Zhao, S. (2024). Research on the detection method for feeding metallic foreign objects in coal mine crushers based on reflective pulsed eddy current testing. Appl. Sci., 14.","DOI":"10.3390\/app142411704"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"116410","DOI":"10.1016\/j.measurement.2024.116410","article-title":"Advances in high-precision displacement and thickness measurement based on eddy current sensors: A review","volume":"243","author":"Zhao","year":"2025","journal-title":"Measurement"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1080\/10589759.2013.823608","article-title":"An improved PSO-SVM model for online recognition defects in eddy current testing","volume":"28","author":"Liu","year":"2013","journal-title":"Nondestruct. Test. Eval."},{"key":"ref_20","first-page":"1","article-title":"Eddy current conductive coating layer assessment on conductive substrate: A machine learning approach","volume":"39","author":"Aldbaisi","year":"2024","journal-title":"Nondestruct. Test. Eval."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/S0963-8695(02)00069-5","article-title":"A feature extraction technique based on principal component analysis for pulsed Eddy current NDT","volume":"36","author":"Sophian","year":"2003","journal-title":"NDT E Int."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"2525","DOI":"10.3390\/s110302525","article-title":"Non-destructive techniques based on eddy current testing","volume":"11","year":"2011","journal-title":"Sensors"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Rifai, D., Abdalla, A.N., Ali, K., and Razali, R. (2016). Giant magnetoresistance sensors: A review on structures and non-destructive eddy current testing applications. Sensors, 16.","DOI":"10.3390\/s16030298"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"7000404","DOI":"10.1109\/LSENS.2018.2822296","article-title":"Lift-off point of intersection in spectral pulsed eddy current signals for thickness measurement","volume":"2","author":"Wen","year":"2018","journal-title":"IEEE Sens. Lett."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"16814","DOI":"10.1109\/TIE.2024.3395750","article-title":"Thickness measurement of titanium-alloy sheets based on the resistance-frequency eddy current method","volume":"71","author":"Chen","year":"2024","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_26","first-page":"1","article-title":"The roles of conductivity in eddy current distributions in nondestructive testing of isotropic and anisotropic materials","volume":"40","author":"Wang","year":"2025","journal-title":"Nondestruct. Test. Eval."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"9518945","DOI":"10.1155\/2020\/9518945","article-title":"Intelligent Defect Identification Based on PECT Signals and an Optimized Two-Dimensional Deep Convolutional Network","volume":"2020","author":"Liu","year":"2020","journal-title":"Complexity"},{"key":"ref_28","first-page":"1","article-title":"Multi-frequency eddy current non-destructive evaluation of battery tab welding quality","volume":"40","author":"Chen","year":"2025","journal-title":"Nondestruct. Test. Eval."},{"key":"ref_29","first-page":"1","article-title":"Evaluating the thickness of thin-walled structures from ultrasound signals based on CNN-BILSTM model","volume":"40","author":"Yang","year":"2025","journal-title":"Nondestruct. Test. Eval."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Na, Y., He, Y., Deng, B., Lu, X., Wang, H., Wang, L., and Cao, Y. (2025). Advances of Machine Learning in Phased Array Ultrasonic Non-Destructive Testing: A Review. AI, 6.","DOI":"10.3390\/ai6060124"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"49","DOI":"10.2528\/PIERM24072601","article-title":"An Intelligent Algorithm Based on the Improved CNN-LSTM for the Detection of Concrete Reinforcement Information","volume":"130","author":"Bai","year":"2024","journal-title":"Prog. Electromagn. Res. M"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"15540","DOI":"10.1109\/JSEN.2024.3373756","article-title":"Real-time tunnel-magnetoresistive-based pulsed eddy current testing with deep learning","volume":"24","author":"Meng","year":"2024","journal-title":"IEEE Sens. J."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"2530313","DOI":"10.1109\/TIM.2024.3418101","article-title":"Thickness measurement and surface-defect detection for metal plate using pulsed eddy current testing and optimized Res2Net network","volume":"73","author":"She","year":"2024","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Chauhan, R., Ghanshala, K.K., and Joshi, R.C. (2018, January 15\u201317). Convolutional neural network (CNN) for image detection and recognition. Proceedings of the 2018 First International Conference on Secure Cyber Computing and Communication (ICSCCC), Jalandhar, India.","DOI":"10.1109\/ICSCCC.2018.8703316"},{"key":"ref_35","unstructured":"Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., and Adam, H. (2017). Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., and Darrell, T. (2015, January 7\u201312). Fully convolutional networks for semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Cho, K., Van Merri\u00ebnboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., and Bengio, Y. (2014). Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv.","DOI":"10.3115\/v1\/D14-1179"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TMECH.2013.2273435","article-title":"Regression methods for virtual metrology of layer thickness in chemical vapor deposition","volume":"19","author":"Purwins","year":"2013","journal-title":"IEEE\/ASME Trans. Mechatron."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Zhang, X., Zhou, X., Lin, M., and Sun, J. (2018, January 18\u201322). Shufflenet: An extremely efficient convolutional neural network for mobile devices. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00716"},{"key":"ref_41","unstructured":"Bai, S., Kolter, J.Z., and Koltun, V. (2018). An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv."},{"key":"ref_42","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, \u0141., and Polosukhin, I. (2017). Attention is all you need. Adv. Neural Inf. Process. Syst., 30, Available online: https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2017\/file\/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Abdollahi-Mamoudan, F., Ibarra-Castanedo, C., and Maldague, X.P. (2025). Non-Destructive Testing and Evaluation of Hybrid and Advanced Structures: A Comprehensive Review of Methods, Applications, and Emerging Trends. Sensors, 25.","DOI":"10.20944\/preprints202505.0910.v1"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"042011","DOI":"10.1088\/2516-1083\/ac8ccb","article-title":"Non-destructive examination (NDE) methods for dynamic subsea cables for offshore renewable energy","volume":"4","author":"Thies","year":"2022","journal-title":"Prog. Energy"}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/16\/10\/919\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T04:11:22Z","timestamp":1761106282000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/16\/10\/919"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,20]]},"references-count":44,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2025,10]]}},"alternative-id":["info16100919"],"URL":"https:\/\/doi.org\/10.3390\/info16100919","relation":{},"ISSN":["2078-2489"],"issn-type":[{"type":"electronic","value":"2078-2489"}],"subject":[],"published":{"date-parts":[[2025,10,20]]}}}