{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T14:34:23Z","timestamp":1762353263923,"version":"build-2065373602"},"reference-count":47,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2022,10,26]],"date-time":"2022-10-26T00:00:00Z","timestamp":1666742400000},"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","doi-asserted-by":"publisher","award":["62074125"],"award-info":[{"award-number":["62074125"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Performing ultrasonic nondestructive testing experiments on insulators and then using machine learning algorithms to classify and identify the signals is an important way to achieve an intelligent diagnosis of insulators. However, in most cases, we can obtain only a limited number of data from the experiments, which is insufficient to meet the requirements for training an effective classification and recognition model. In this paper, we start with an existing data augmentation method called DBA (for dynamic time warping barycenter averaging) and propose a new data enhancement method called AWDBA (adaptive weighting DBA). We first validated the proposed method by synthesizing new data from insulator sample datasets. The results show that the AWDBA proposed in this study has significant advantages relative to DBA in terms of data enhancement. Then, we used AWDBA and two other data augmentation methods to synthetically generate new data on the original dataset of insulators. Moreover, we compared the performance of different machine learning algorithms for insulator health diagnosis on the dataset with and without data augmentation. In the SVM algorithm especially, we propose a new parameter optimization method based on GA (genetic algorithm). The final results show that the use of the data augmentation method can significantly improve the accuracy of insulator defect identification.<\/jats:p>","DOI":"10.3390\/s22218187","type":"journal-article","created":{"date-parts":[[2022,10,26]],"date-time":"2022-10-26T07:17:48Z","timestamp":1666768668000},"page":"8187","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["A Novel Data Augmentation Method for Improving the Accuracy of Insulator Health Diagnosis"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9178-9953","authenticated-orcid":false,"given":"Zhifeng","family":"Li","sequence":"first","affiliation":[{"name":"State Key Laboratory for Strength and Vibration of Mechanical Structures, School of Aerospace Engineering, Xi\u2019an Jiaotong University, Xi\u2019an 710049, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4729-9148","authenticated-orcid":false,"given":"Yaqin","family":"Song","sequence":"additional","affiliation":[{"name":"State Key Laboratory for Strength and Vibration of Mechanical Structures, School of Aerospace Engineering, Xi\u2019an Jiaotong University, Xi\u2019an 710049, China"}]},{"given":"Runchen","family":"Li","sequence":"additional","affiliation":[{"name":"State Key Laboratory for Strength and Vibration of Mechanical Structures, School of Aerospace Engineering, Xi\u2019an Jiaotong University, Xi\u2019an 710049, China"}]},{"given":"Sen","family":"Gu","sequence":"additional","affiliation":[{"name":"State Key Laboratory for Strength and Vibration of Mechanical Structures, School of Aerospace Engineering, Xi\u2019an Jiaotong University, Xi\u2019an 710049, China"}]},{"given":"Xuze","family":"Fan","sequence":"additional","affiliation":[{"name":"State Key Laboratory for Strength and Vibration of Mechanical Structures, School of Aerospace Engineering, Xi\u2019an Jiaotong University, Xi\u2019an 710049, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Choi, I.H., Koo, J.B., Woo, J.W., Son, J.A., Bae, D.Y., Yoon, Y.G., and Oh, T.K. (2019). Damage Evaluation of Porcelain Insulators with 154 KV Transmission Lines by Various Support Vector Machine (SVM) and Ensemble Methods Using Frequency Response Data. Appl. Sci., 10.","DOI":"10.3390\/app10010084"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1109","DOI":"10.1109\/TITS.2020.3020287","article-title":"Adversarial Reconstruction Based on Tighter Oriented Localization for Catenary Insulator Defect Detection in High-Speed Railways","volume":"23","author":"Zhong","year":"2022","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Suhaimi, S.M.I., Muhamad, N.A., Bashir, N., Mohd Jamil, M.K., and Abdul Rahman, M.N. (2022). Harmonic Components Analysis of Emitted Ultraviolet Signals of Aged Transmission Line Insulators under Different Surface Discharge Intensities. Sensors, 22.","DOI":"10.3390\/s22030722"},{"key":"ref_4","first-page":"3888","article-title":"Quality control for basin insulator used in gas insulated metal enclosed switchgear of Ultra high voltage","volume":"40","author":"Boyuan","year":"2022","journal-title":"High Volt. Eng."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Tian, F., Hao, Y., Zou, Z., Zheng, Y., He, W., Yang, L., and Li, L. (2019). An Ultrasonic Pulse-Echo Method to Detect Internal Defects in Epoxy Composite Insulation. Energies, 12.","DOI":"10.3390\/en12244804"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Li, W., Zhou, F., Zheng, Y., Chu, J., Gao, C., Liu, W., and Huang, R. (2020, January 20\u201323). An Ultrasonic Detection Method for Interface Defects of Three Post Insulators in Gas-Insulated Transmission Lines. Proceedings of the 2020 12th IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC), Nanjing, China.","DOI":"10.1109\/APPEEC48164.2020.9220740"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1109\/MIM.2021.9400959","article-title":"Application of Machine Learning in Outdoor Insulators Condition Monitoring and Diagnostics","volume":"24","year":"2021","journal-title":"IEEE Instrum. Meas. Mag."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"108148","DOI":"10.1016\/j.patcog.2021.108148","article-title":"Improving the Accuracy of Global Forecasting Models Using Time Series Data Augmentation","volume":"120","author":"Bandara","year":"2021","journal-title":"Pattern Recognit."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2831","DOI":"10.1016\/S0045-7825(02)00221-9","article-title":"Detection of Cracks Using Neural Networks and Computational Mechanics","volume":"191","author":"Liu","year":"2002","journal-title":"Comput. Methods Appl. Mech. Eng."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Um, T.T., Pfister, F.M.J., Pichler, D., Endo, S., Lang, M., Hirche, S., Fietzek, U., and Kuli\u0107, D. (2017, January 13\u201417). Data Augmentation of Wearable Sensor Data for Parkinson\u2019s Disease Monitoring Using Convolutional Neural Networks. Proceedings of the 19th ACM International Conference on Multimodal Interaction, Glasgow, UK.","DOI":"10.1145\/3136755.3136817"},{"key":"ref_11","unstructured":"Charalambous, C.C., and Bharath, A.A. (2016, January 19\u201322). A Data Augmentation Methodology for Training Machine\/Deep Learning Gait Recognition Algorithms. Proceedings of the British Machine Vision Conference, York, UK."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"107275","DOI":"10.1016\/j.patcog.2020.107275","article-title":"Global and Local Sensitivity Guided Key Salient Object Re-Augmentation for Video Saliency Detection","volume":"103","author":"Wang","year":"2020","journal-title":"Pattern Recognit."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"106800","DOI":"10.1016\/j.measurement.2019.07.028","article-title":"Measurement Investigations in Tubular Structures Health Monitoring via Ultrasonic Guided Waves: A Case of Study","volume":"147","author":"Yaacoubi","year":"2019","journal-title":"Measurement"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Iwana, B.K., and Uchida, S. (2021). An Empirical Survey of Data Augmentation for Time Series Classification with Neural Networks. PLoS ONE, 16.","DOI":"10.1371\/journal.pone.0254841"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"678","DOI":"10.1016\/j.patcog.2010.09.013","article-title":"A Global Averaging Method for Dynamic Time Warping, with Applications to Clustering","volume":"44","author":"Petitjean","year":"2011","journal-title":"Pattern Recognit."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Forestier, G., Petitjean, F., Dau, H.A., Webb, G.I., and Keogh, E. (2017, January 18\u201321). Generating Synthetic Time Series to Augment Sparse Datasets. Proceedings of the 2017 IEEE International Conference on Data Mining (ICDM), New Orleans, LA, USA.","DOI":"10.1109\/ICDM.2017.106"},{"key":"ref_17","unstructured":"Fawaz, H.I., Forestier, G., Weber, J., Idoumghar, L., and Muller, P.-A. (2018). Data Augmentation Using Synthetic Data for Time Series Classification with Deep Residual Networks. arXiv."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/j.patcog.2017.08.015","article-title":"Time-Series Averaging Using Constrained Dynamic Time Warping with Tolerance","volume":"74","author":"Morel","year":"2018","journal-title":"Pattern Recognit."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"107699","DOI":"10.1016\/j.patcog.2020.107699","article-title":"Time-Series Averaging and Local Stability-Weighted Dynamic Time Warping for Online Signature Verification","volume":"112","author":"Okawa","year":"2021","journal-title":"Pattern Recognit."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Kamycki, K., Kapuscinski, T., and Oszust, M. (2019). Data Augmentation with Suboptimal Warping for Time-Series Classification. Sensors, 20.","DOI":"10.3390\/s20010098"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Rashid, K.M., and Louis, J. (2019, January 21\u201324). Time-warping: A time series data augmentation of IMU data for construction equipment activity identification. Proceedings of the 36th International Symposium on Automation and Robotics in Construction (ISARC), Banff, AB, Canda.","DOI":"10.22260\/ISARC2019\/0087"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Iwana, B.K., and Uchida, S. (2021, January 10\u201315). Time Series Data Augmentation for Neural Networks by Time Warping with a Discriminative Teacher. Proceedings of the 2020 25th International Conference on Pattern Recognition (ICPR), Milan, Italy.","DOI":"10.1109\/ICPR48806.2021.9412812"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"100944","DOI":"10.1016\/j.aei.2019.100944","article-title":"Times-Series Data Augmentation and Deep Learning for Construction Equipment Activity Recognition","volume":"42","author":"Rashid","year":"2019","journal-title":"Adv. Eng. Inform."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1145\/3422622","article-title":"Generative adversarial networks","volume":"63","author":"Goodfellow","year":"2020","journal-title":"Commun. ACM"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Lou, H., Qi, Z., and Li, J. (2018, January 9\u201311). One-Dimensional Data Augmentation Using a Wasserstein Generative Adversarial Network with Supervised Signal. Proceedings of the 2018 Chinese Control and Decision Conference (CCDC), Shenyang, China.","DOI":"10.1109\/CCDC.2018.8407436"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Haradal, S., Hayashi, H., and Uchida, S. (2018, January 18\u201321). Biosignal Data Augmentation Based on Generative Adversarial Networks. Proceedings of the 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Honolulu, HI, USA.","DOI":"10.1109\/EMBC.2018.8512396"},{"key":"ref_27","unstructured":"Esteban, C., Hyland, S.L., and R\u00e4tsch, G. (2017). Real-Valued (Medical) Time Series Generation with Recurrent Conditional GANs. arXiv."},{"key":"ref_28","unstructured":"Ramponi, G., Protopapas, P., Brambilla, M., and Janssen, R. (2019). T-CGAN: Conditional Generative Adversarial Network for Data Augmentation in Noisy Time Series with Irregular Sampling. arXiv."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Che, Z., Cheng, Y., Zhai, S., Sun, Z., and Liu, Y. (2017, January 18\u201321). Boosting Deep Learning Risk Prediction with Generative Adversarial Networks for Electronic Health Records. Proceedings of the 2017 IEEE International Conference on Data Mining (ICDM), New Orleans, LA, USA.","DOI":"10.1109\/ICDM.2017.93"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Chen, G., Zhu, Y., Hong, Z., and Yang, Z. (2019, January 12\u201313). EmotionalGAN: Generating ECG to Enhance Emotion State Classification. Proceedings of the 2019 International Conference on Artificial Intelligence and Computer Science, Wuhan, China.","DOI":"10.1145\/3349341.3349422"},{"key":"ref_31","unstructured":"Sakoe, H., and Chiba, S. (1971, January 18\u201326). A Dynamic Programming Approach to Continuous Speech Recognition. Proceedings of the Seventh International Congress on Acoustics, Budapest, Hungary."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1109\/TASSP.1978.1163055","article-title":"Dynamic Programming Algorithm Optimization for Spoken Word Recognition","volume":"26","author":"Sakoe","year":"1978","journal-title":"IEEE Trans. Acoust. Speech Signal Process"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"495","DOI":"10.1093\/bioinformatics\/17.6.495","article-title":"Aligning Gene Expression Time Series with Time Warping Algorithms","volume":"17","author":"Aach","year":"2001","journal-title":"Bioinformatics"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"366","DOI":"10.1016\/j.neucom.2009.08.005","article-title":"A Multidimensional Dynamic Time Warping Algorithm for Efficient Multimodal Fusion of Asynchronous Data Streams","volume":"73","author":"Eyben","year":"2009","journal-title":"Neurocomputing"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Jiang, Y., Qi, Y., Wang, W.K., Bent, B., Avram, R., Olgin, J., and Dunn, J. (2020). EventDTW: An Improved Dynamic Time Warping Algorithm for Aligning Biomedical Signals of Nonuniform Sampling Frequencies. Sensors, 20.","DOI":"10.3390\/s20092700"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Li, S., Zuo, X., Li, Z., Wang, H., and Sun, L. (2020). Combining SDAE Network with Improved DTW Algorithm for Similarity Measure of Ultra-Weak FBG Vibration Responses in Underground Structures. Sensors, 20.","DOI":"10.3390\/s20082179"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"2231","DOI":"10.1016\/j.patcog.2010.09.022","article-title":"Weighted Dynamic Time Warping for Time Series Classification","volume":"44","author":"Jeong","year":"2011","journal-title":"Pattern Recognit."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10115-015-0878-8","article-title":"Faster and More Accurate Classification of Time Series by Exploiting a Novel Dynamic Time Warping Averaging Algorithm","volume":"47","author":"Petitjean","year":"2016","journal-title":"Knowl. Inf. Syst."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"81010","DOI":"10.1109\/ACCESS.2019.2923093","article-title":"Template Matching Using Time-Series Averaging and DTW With Dependent Warping for Online Signature Verification","volume":"7","author":"Okawa","year":"2019","journal-title":"IEEE Access"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/1541880.1541882","article-title":"Anomaly detection: A survey","volume":"41","author":"Chandola","year":"2009","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1998","DOI":"10.1016\/j.net.2020.02.001","article-title":"Fault State Detection and Remaining Useful Life Prediction in AC Powered Solenoid Operated Valves Based on Traditional Machine Learning and Deep Neural Networks","volume":"52","author":"Utah","year":"2020","journal-title":"Nucl. Eng. Technol."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1502","DOI":"10.12928\/telkomnika.v14i4.3956","article-title":"SVM Parameter Optimization Using Grid Search and Genetic Algorithm to Improve Classification Performance","volume":"14","author":"Syarif","year":"2016","journal-title":"TELKOMNIKA (Telecommun. Comput. Electron. Control)"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Machova, K., Mach, M., and Vasilko, M. (2021). Comparison of Machine Learning and Sentiment Analysis in Detection of Suspicious Online Reviewers on Different Type of Data. Sensors, 22.","DOI":"10.3390\/s22010155"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1016\/j.patcog.2008.06.025","article-title":"PCA and SVD with Nonnegative Loadings","volume":"42","author":"Lipovetsky","year":"2009","journal-title":"Pattern Recognit."},{"key":"ref_45","unstructured":"El Mountassir, M., Yaacoubi, S., Ragot, J., Mourot, G., and Maquin, G. (2016, January 5\u20138). Feature selection techniques for identifying the most relevant damage indices in SHM using Guided Waves. Proceedings of the 8th European Workshop On Structural Health Monitoring, EWSHM 2016, Bilbao, Spain."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"S\u00e1nchez-Maro\u00f1o, N., Alonso-Betanzos, A., and Tombilla-Snarom\u00e1n, M. (2007). Filter methods for feature selection\u2013a comparative study. Intelligent Data Engineering and Automated Learning-IDEAL, Springer.","DOI":"10.1007\/978-3-540-77226-2_19"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1016\/S0004-3702(97)00043-X","article-title":"Wrappers for Feature Subset Selection","volume":"97","author":"Kohavi","year":"1997","journal-title":"Artif. Intell."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/21\/8187\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:02:54Z","timestamp":1760144574000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/21\/8187"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,26]]},"references-count":47,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2022,11]]}},"alternative-id":["s22218187"],"URL":"https:\/\/doi.org\/10.3390\/s22218187","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2022,10,26]]}}}