{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T05:06:38Z","timestamp":1789103198536,"version":"build-2803163510"},"reference-count":29,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T00:00:00Z","timestamp":1788912000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100007129","name":"Shandong Provincial Natural Science Foundation","doi-asserted-by":"publisher","award":["ZR2025QC2202Z"],"award-info":[{"award-number":["ZR2025QC2202Z"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Dezhou Intelligent Equipment Research and Development Center","award":["PT2025KJT004"],"award-info":[{"award-number":["PT2025KJT004"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Gas-insulated switchgear (GIS) is an important component of power transmission systems. Accurate partial discharge (PD) pattern recognition is a key requirement for identifying internal insulation defects within the equipment. However, ultra-high-frequency (UHF) PD pulse sequences produced by different insulation defects usually contain complex nonlinear temporal structures and multi-scale periodic variations. These coupled characteristics are difficult to describe adequately via a single feature-mapping strategy. Thus, this paper proposes a temporal-frequency dual-branch stochastic configuration network (TF-SCN), which consists of two heterogeneous hidden-layer branches, for GIS PD pattern recognition. Specifically, in the temporal branch, the model uses a non-periodic, nonlinear activation function similar to that used in a conventional SCN to capture the nonlinear temporal characteristics. The frequency-sensitive branch introduces paired sine\u2013cosine harmonic nodes with shared random projection parameters to capture frequency-sensitive features. The hidden outputs of the two branches are concatenated into a joint temporal-harmonic feature space, and the output weights are solved under the residual inequality constraints for GIS PD classification. To verify the superiority of the proposed model, comparative experiments are conducted on a dataset containing four PD patterns collected from the GIS PD experimental platform. Several baseline models, including 1DCNN, BPNN, SVM, KELM, RVFL, and SCN, are selected for performance comparison. The results show that, compared to 1DCNN, BPNN, SVM, KELM, RVFL, and SCN, TF-SCN effectively extracts distinguishable features in both the time and frequency domains, thereby achieving the best overall performance. Furthermore, its recognition performance remains consistently superior even on noisy data with signal-to-noise ratios ranging from 50 dB to 20 dB. By integrating highly sensitive UHF sensors with the proposed TF-SCN, this study presents a robust, AI-enhanced intelligent sensing and fault diagnosis system for continuous condition monitoring of power equipment.<\/jats:p>","DOI":"10.3390\/s26185739","type":"journal-article","created":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T16:43:16Z","timestamp":1788972196000},"page":"5739","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Intelligent UHF Sensor-Based Partial Discharge Fault Diagnosis in GIS Using a Temporal-Frequency Dual-Branch Stochastic Configuration Network"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1052-8952","authenticated-orcid":false,"given":"Mingyuan","family":"Hu","sequence":"first","affiliation":[{"name":"School of Electrical Engineering, Shandong Huayu University of Technology, Dezhou 253000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingwen","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering, Shandong Huayu University of Technology, Dezhou 253000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Baolong","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Mechanical and Electrical Engineering, Jiujiang Polytechnic University of Science and Technology, Jiujiang 332007, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3013-0290","authenticated-orcid":false,"given":"Ying-Ren","family":"Chien","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, National Taipei University of Technology, Taipei City 10608, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2192-0466","authenticated-orcid":false,"given":"Lei","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering, Beijing Jiaotong University, Beijing 100091, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Jing, Q.Z., Yan, J., Lu, L., Xu, Y.F., and Yang, F. (2022). A novel method for pattern recognition of GIS partial discharge via multi-information ensemble learning. Entropy, 24.","DOI":"10.3390\/e24070954"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2047","DOI":"10.1109\/TPWRD.2016.2635382","article-title":"A novel GIS partial discharge detection sensor with integrated optical and UHF methods","volume":"33","author":"Li","year":"2018","journal-title":"IEEE Trans. Power Deliv."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"9235","DOI":"10.1109\/JSEN.2019.2925848","article-title":"A high sensitivity optical fiber sensor for GIS partial discharge detection","volume":"19","author":"Ma","year":"2019","journal-title":"IEEE Sens. J."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"864","DOI":"10.1049\/iet-smt.2019.0542","article-title":"GIS partial discharge pattern recognition via lightweight convolutional neural network in the ubiquitous power internet of things context","volume":"14","author":"Wang","year":"2020","journal-title":"IET Sci. Meas. Technol."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"\u00c1lvarez G\u00f3mez, F., Albarrac\u00edn-S\u00e1nchez, R., Garnacho Vecino, F., and Granizo Arrab\u00e9, R. (2018). Diagnosis of insulation condition of MV switchgears by application of different partial discharge measuring methods and sensors. Sensors, 18.","DOI":"10.3390\/s18030720"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"891","DOI":"10.1109\/TDEI.2005.1522184","article-title":"Partial discharge diagnostics and electrical equipment insulation condition assessment","volume":"12","author":"Stone","year":"2005","journal-title":"IEEE Trans. Dielectr. Electr. Insul."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Cao, T.N., Cui, Y.F., Tan, H.T., Lu, W., Zhang, F.Z., Liu, K., Chen, X.G., and Wang, L.J. (2025). Research on a degradation identification method for GIS UHF partial discharge sensors based on S-parameters. Sensors, 25.","DOI":"10.3390\/s25226860"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1049\/iet-smt.2016.0419","article-title":"Partial discharge detection by extracting UHF signal from inner grading electrode of insulating spacer in GIS","volume":"12","author":"Shao","year":"2018","journal-title":"IET Sci. Meas. Technol."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Yin, K.Y., Wang, Y.H., Liu, S.H., Li, P.F., Xue, Y.X., Li, B.Z., and Dai, K.J. (2022). GIS partial discharge pattern recognition based on multi-feature information fusion of PRPD image. Symmetry, 14.","DOI":"10.3390\/sym14112464"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Fang, W.X., Chen, G.J., Li, W.X., Xu, M., Xie, W., Chen, C., Wang, W.Q., and Zhu, Y.C. (2023). A PRPD-based UHF filtering and noise reduction algorithm for GIS partial discharge. Sensors, 23.","DOI":"10.3390\/s23156763"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"118054","DOI":"10.1016\/j.measurement.2025.118054","article-title":"A novel small-sample diagnosis method for GIS partial discharge by transfer robust support matrix machine","volume":"255","author":"Jing","year":"2025","journal-title":"Measurement"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"3315","DOI":"10.1109\/ACCESS.2024.3349410","article-title":"A novel fault diagnosis of GIS partial discharge based on improved whale optimization algorithm","volume":"12","author":"Sun","year":"2024","journal-title":"IEEE Access"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1016\/j.measurement.2015.02.032","article-title":"Partial discharge classifications: Review of recent progress","volume":"68","author":"Raymond","year":"2015","journal-title":"Measurement"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Li, S.Y., Wang, X.P., Han, Y., Feng, J.J., Yin, Z., Yang, J.W., Luo, W.P., and Xie, J. (2025). Partial discharge data enhancement and pattern recognition method based on a CAE-ACGAN and ResNet. Symmetry, 17.","DOI":"10.3390\/sym17010055"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"111154","DOI":"10.1016\/j.measurement.2022.111154","article-title":"A novel differentiable neural network architecture automatic search method for GIS partial discharge pattern recognition","volume":"195","author":"Jing","year":"2022","journal-title":"Measurement"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"742","DOI":"10.1109\/TDEI.2025.3579444","article-title":"Enhancement of GIS partial discharge pattern recognition accuracy based on domain adversarial ConvNeXt","volume":"33","author":"Liu","year":"2026","journal-title":"IEEE Trans. Dielectr. Electr. Insul."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Ran, Y.B. (2024, January 9\u201311). State recognition of GIS partial discharge based on PRPD and CNNs. Proceedings of the 2024 IEEE 2nd International Conference on Power Science and Technology (ICPST), Dali, China.","DOI":"10.1109\/ICPST61417.2024.10602113"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Xu, X.Y., Wu, T.Y., Wang, S.J., Xu, P., Li, Z.X., and Zang, Y.M. (2025, January 15\u201319). Pattern recognition of GIS partial discharge optical signal based on GWO-CNN algorithm. Proceedings of the 2025 10th Asia Conference on Power and Electrical Engineering (ACPEE), Beijing, China.","DOI":"10.1109\/ACPEE64358.2025.11041503"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"113081","DOI":"10.1016\/j.knosys.2025.113081","article-title":"CCMnet: A cluster-level contrastive cross-domain framework for GIS insulation defects diagnosis","volume":"311","author":"Yu","year":"2025","journal-title":"Knowl. Based Syst."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"3537811","DOI":"10.1109\/TIM.2024.3470010","article-title":"A novel bilateral branching network with cost-sensitive Res2Net for diagnosis GIS insulation defects on imbalanced data","volume":"73","author":"Yu","year":"2024","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"113923","DOI":"10.1016\/j.ymssp.2026.113923","article-title":"A liquid-impulse neural network model based on heterogeneous fusion of multimodal information for interpretable rotating machinery fault diagnosis","volume":"246","author":"You","year":"2026","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Spirto, M., Malfi, P., Nicolella, A., Melluso, F., Cosenza, C., Savino, S., and Niola, V. (2026). Fault detection of electric motors via symmetrized dot pattern-based features. Int. J. Mech. Syst. Dyn., 1\u201315.","DOI":"10.1002\/msd2.70075"},{"key":"ref_23","first-page":"183","article-title":"An interpretable wavelet Kolmogorov\u2013Arnold convolutional LSTM for spatial-temporal feature extraction and intelligent fault diagnosis","volume":"4","author":"Chen","year":"2025","journal-title":"J. Dyn. Monit. Diagn."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"110556","DOI":"10.1016\/j.ress.2024.110556","article-title":"A sound-vibration physical-information fusion constraint-guided deep learning method for rolling bearing fault diagnosis","volume":"253","author":"You","year":"2025","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"133978","DOI":"10.1016\/j.eswa.2026.133978","article-title":"A physical soft-constraint interpretability-based multimodal deep learning model for small-sample fault diagnosis of pressurized water reactor coolant systems in nuclear power plants","volume":"333","author":"You","year":"2027","journal-title":"Expert Syst. Appl."},{"key":"ref_26","first-page":"101143","article-title":"A physics-constrained multimodal LLM for fault diagnosis of pressurized water reactor coolant systems with imbalanced and under-sampled data","volume":"52","author":"You","year":"2026","journal-title":"J. Ind. Inf. Integr."},{"key":"ref_27","first-page":"102394","article-title":"Robust rotating machinery diagnosis from single-source heterogeneous signals under imbalanced and noisy conditions","volume":"79","author":"You","year":"2026","journal-title":"Eng. Sci. Technol. Int. J."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"3466","DOI":"10.1109\/TCYB.2017.2734043","article-title":"Stochastic configuration networks: Fundamentals and algorithms","volume":"47","author":"Wang","year":"2017","journal-title":"IEEE Trans. Cybern."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"086137","DOI":"10.1088\/1361-6501\/adf245","article-title":"GCS-EN-SCNs-wd: A generalized class-specific cost-sensitive mechanism to regulate stochastic configuration networks with direct link for transformer fault diagnosis","volume":"36","author":"Song","year":"2025","journal-title":"Meas. Sci. Technol."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/18\/5739\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T04:23:33Z","timestamp":1789100613000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/18\/5739"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,9]]},"references-count":29,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2026,9]]}},"alternative-id":["s26185739"],"URL":"https:\/\/doi.org\/10.3390\/s26185739","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,9]]}}}