{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T19:55:41Z","timestamp":1776110141599,"version":"3.50.1"},"reference-count":31,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2023,6,2]],"date-time":"2023-06-02T00:00:00Z","timestamp":1685664000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Research Foundation of Korea (NRF)","award":["2021M1A2A2043894"],"award-info":[{"award-number":["2021M1A2A2043894"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Cable is crucial to the control and instrumentation of machines and facilities. Therefore, early diagnosis of cable faults is the most effective approach to prevent system downtime and maximize productivity. We focused on a \u201csoft fault state\u201d, which is a transient state that eventually becomes a permanent fault \u2014open-circuit and short-circuit. However, the issue of soft fault diagnosis has not been considered enough in previous research, which could not provide crucial information, such as fault severity, to support maintenance. In this study, we focused on solving soft fault problem by estimating fault severity to diagnose early-stage faults. The proposed diagnosis method comprised a novelty detection and severity estimation network. The novelty detection part is specially designed to deal with varying operating conditions of industrial applications. First, an autoencoder calculates anomaly scores to detect faults using three-phase currents. If a fault is detected, a fault severity estimation network, wherein long short-term memory and attention mechanisms are integrated, estimates the fault severity based on the time-dependent information of the input. Accordingly, no additional equipment, such as voltage sensors and signal generators, is required. The conducted experiments demonstrated that the proposed method successfully distinguishes seven different soft fault degrees.<\/jats:p>","DOI":"10.3390\/s23115299","type":"journal-article","created":{"date-parts":[[2023,6,2]],"date-time":"2023-06-02T10:08:41Z","timestamp":1685700521000},"page":"5299","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Attention Recurrent Neural Network-Based Severity Estimation Method for Early-Stage Fault Diagnosis in Robot Harness Cable"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9230-4970","authenticated-orcid":false,"given":"Heonkook","family":"Kim","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering, Pohang University of Science and Technology, Pohang 37673, Republic of Korea"}]},{"given":"Hojin","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Pohang University of Science and Technology, Pohang 37673, Republic of Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2117-9674","authenticated-orcid":false,"given":"Seongyun","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Pohang University of Science and Technology, Pohang 37673, Republic of Korea"}]},{"given":"Sang Woo","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Pohang University of Science and Technology, Pohang 37673, Republic of Korea"}]}],"member":"1968","published-online":{"date-parts":[[2023,6,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2606","DOI":"10.1109\/TIM.2017.2700178","article-title":"Multiple chirp reflectometry for determination of fault direction and localization in live branched network cables","volume":"66","author":"Chang","year":"2017","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2804","DOI":"10.1016\/j.eswa.2011.08.140","article-title":"Partial discharge pattern recognition of power cable joints using extension method with fractal feature enhancement","volume":"39","author":"Gu","year":"2012","journal-title":"Expert Syst. Appl."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"5102","DOI":"10.1109\/JSEN.2020.3035754","article-title":"A Method of Fault Localization Within the Blind Spot Using the Hybridization Between TDR and Wavelet Transform","volume":"21","author":"Lee","year":"2021","journal-title":"IEEE Sens. J."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Jacob, R.A., Senemmar, S., and Zhang, J. (2021, January 22\u201325). Fault Diagnostics in Shipboard Power Systems using Graph Neural Networks. Proceedings of the 2021 IEEE 13th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED), Dallas, TX, USA.","DOI":"10.1109\/SDEMPED51010.2021.9605496"},{"key":"ref_5","unstructured":"Ip, K.H., and Tang, C.P. (2006). Electrical Contacts\u20142006, Proceedings of the 52nd IEEE Holm Conference on Electrical Contacts, Montreal, QC, Canada, 25\u201327 September 2006, IEEE."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"586","DOI":"10.1109\/TEMC.2018.2830404","article-title":"Never trust a cable bearing echoes: Understanding ambiguities in time-domain reflectometry applied to soft faults in cables","volume":"61","author":"Cozza","year":"2018","journal-title":"IEEE Trans. Electromagn. Compat."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"158","DOI":"10.3390\/app10010158","article-title":"Fault detection in multi-core C&I cable via machine learning based time-frequency domain reflectometry","volume":"10","author":"Lee","year":"2019","journal-title":"Appl. Sci."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"4163","DOI":"10.1109\/TIE.2019.2920606","article-title":"Classification of faults in multicore cable via time\u2013frequency domain reflectometry","volume":"67","author":"Bang","year":"2019","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Kim, H., Jeong, H., Lee, H., and Kim, S.W. (2021). Online and Offline Diagnosis of Motor Power Cables Based on 1D CNN and Periodic Burst Signal Injection. Sensors, 21.","DOI":"10.3390\/s21175936"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1725","DOI":"10.1109\/JSYST.2018.2822549","article-title":"Fast current-only based fault detection method in transmission line","volume":"13","author":"Jarrahi","year":"2018","journal-title":"IEEE Syst. J."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Kim, H., Lee, H., and Kim, S.W. (2022). Current Only-Based Fault Diagnosis Method for Industrial Robot Control Cables. Sensors, 22.","DOI":"10.3390\/s22051917"},{"key":"ref_12","unstructured":"Chalapathy, R., Menon, A.K., and Chawla, S. (2019). Anomaly detection using one-class neural networks. arXiv."},{"key":"ref_13","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_14","doi-asserted-by":"crossref","first-page":"132330","DOI":"10.1109\/ACCESS.2020.3010274","article-title":"Anomalous Example Detection in Deep Learning: A Survey","volume":"8","author":"Bulusu","year":"2020","journal-title":"IEEE Access"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Chen, J., Sathe, S., Aggarwal, C., and Turaga, D. (2017, January 27\u201329). Outlier detection with autoencoder ensembles. Proceedings of the 2017 SIAM International Conference on Data Mining, Houston, TX, USA.","DOI":"10.1137\/1.9781611974973.11"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Liao, W., Guo, Y., Chen, X., and Li, P. (2018, January 10\u201313). A unified unsupervised gaussian mixture variational autoencoder for high dimensional outlier detection. Proceedings of the 2018 IEEE International Conference on Big Data (Big Data), Seattle, WA, USA.","DOI":"10.1109\/BigData.2018.8622120"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Zhou, C., and Paffenroth, R.C. (2017, January 13\u201317). Anomaly detection with robust deep autoencoders. Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Halifax, NS, Canada.","DOI":"10.1145\/3097983.3098052"},{"key":"ref_18","unstructured":"Zong, B., Song, Q., Min, M.R., Cheng, W., Lumezanu, C., Cho, D., and Chen, H. (May, January 30). Deep autoencoding gaussian mixture model for unsupervised anomaly detection. Proceedings of the International Conference on Learning Representations, Vancouver, BC, Canada."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Cho, K., Van Merri\u00ebnboer, B., Bahdanau, D., and Bengio, Y. (2014). On the properties of neural machine translation: Encoder-decoder approaches. arXiv.","DOI":"10.3115\/v1\/W14-4012"},{"key":"ref_20","unstructured":"Bahdanau, D., Cho, K., and Bengio, Y. (2014). Neural machine translation by jointly learning to align and translate. arXiv."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1466","DOI":"10.1049\/iet-gtd.2017.0670","article-title":"Equivalent circuit and calculation of unbalanced power in three-wire three-phase linear networks","volume":"12","author":"Diez","year":"2018","journal-title":"IET Gener. Transm. Distrib."},{"key":"ref_22","unstructured":"Rahman, M.A.A., and Ghosh, P.S. (2008, January 21\u201324). Diagnosis on MV XLPE power cable by using frequency variance leakage current analysis. Proceedings of the 2008 International Conference on Condition Monitoring and Diagnosis, Beijing, China."},{"key":"ref_23","unstructured":"Kim, K.H., Shim, S., Lim, Y., Jeon, J., Choi, J., Kim, B., and Yoon, A.S. (2019, January 6\u20139). Rapp: Novelty detection with reconstruction along projection pathway. Proceedings of the International Conference on Learning Representations, New Orleans, LA, USA."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Shin, S.Y., and Kim, H.-j. (2020). Extended Autoencoder for Novelty Detection with Reconstruction along Projection Pathway. Appl. Sci., 10.","DOI":"10.3390\/app10134497"},{"key":"ref_25","first-page":"602","article-title":"Framewise phoneme classification with bidirectional LSTM and other neural network architectures","volume":"18","author":"Graves","year":"2005","journal-title":"SAE Int. J. Passeng. Cars-Electron. Electr. Syst."},{"key":"ref_26","unstructured":"Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., and Isard, M. (2016, January 2\u20134). Tensorflow: A system for large-scale machine learning. Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 16), Savannah, GA, USA."},{"key":"ref_27","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2467","DOI":"10.1109\/TDEI.2018.007344","article-title":"Detection of abnormality occurring over the whole cable length by frequency domain reflectometry","volume":"25","author":"Ohki","year":"2018","journal-title":"IEEE Trans. Dielectr. Electr. Insul."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1171","DOI":"10.1109\/JSEN.2013.2294193","article-title":"A new algorithm for wire fault location using time-domain reflectometry","volume":"14","author":"Shi","year":"2013","journal-title":"IEEE Sens. J."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"2482","DOI":"10.1109\/JSYST.2020.3010334","article-title":"Online Detection of Aircraft ARINC Bus Cable Fault Based on SSTDR","volume":"15","author":"Shi","year":"2021","journal-title":"IEEE Syst. J."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1082","DOI":"10.1109\/JSEN.2020.2997696","article-title":"Industrial Applications of Cable Diagnostics and Monitoring Cables via Time\u2013Frequency Domain Reflectometry","volume":"21","author":"Lee","year":"2021","journal-title":"IEEE Sens. J."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/11\/5299\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:48:08Z","timestamp":1760125688000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/11\/5299"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,2]]},"references-count":31,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2023,6]]}},"alternative-id":["s23115299"],"URL":"https:\/\/doi.org\/10.3390\/s23115299","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,6,2]]}}}