{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T05:19:44Z","timestamp":1782969584072,"version":"3.54.5"},"reference-count":45,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2024,7,26]],"date-time":"2024-07-26T00:00:00Z","timestamp":1721952000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"SungKyunKwan University","award":["BK21 FOUR"],"award-info":[{"award-number":["BK21 FOUR"]}]},{"name":"Ministry of Education (MOE, Korea)","award":["BK21 FOUR"],"award-info":[{"award-number":["BK21 FOUR"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The manufacturing industry has been operating within a constantly evolving technological environment, underscoring the importance of maintaining the efficiency and reliability of manufacturing processes. Motor-related failures, especially bearing defects, are common and serious issues in manufacturing processes. Bearings provide accurate and smooth movements and play essential roles in mechanical equipment with shafts. Given their importance, bearing failure diagnosis has been extensively studied. However, the imbalance in failure data and the complexity of time series data make diagnosis challenging. Conventional AI models (convolutional neural networks (CNNs), long short-term memory (LSTM), support vector machine (SVM), and extreme gradient boosting (XGBoost)) face limitations in diagnosing such failures. To address this problem, this paper proposes a bearing failure diagnosis model using a graph convolution network (GCN)-based LSTM autoencoder with self-attention. The model was trained on data extracted from the Case Western Reserve University (CWRU) dataset and a fault simulator testbed. The proposed model achieved 97.3% accuracy on the CWRU dataset and 99.9% accuracy on the fault simulator dataset.<\/jats:p>","DOI":"10.3390\/s24154855","type":"journal-article","created":{"date-parts":[[2024,7,26]],"date-time":"2024-07-26T09:22:42Z","timestamp":1721985762000},"page":"4855","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["GCN-Based LSTM Autoencoder with Self-Attention for Bearing Fault Diagnosis"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-2280-972X","authenticated-orcid":false,"given":"Daehee","family":"Lee","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering, Sungkyunkwan University, 2066 Seobu-ro, Jangan-gu, Suwon-si 16419, Gyeonggi-do, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6485-3155","authenticated-orcid":false,"given":"Hyunseung","family":"Choo","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Sungkyunkwan University, 2066 Seobu-ro, Jangan-gu, Suwon-si 16419, Gyeonggi-do, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4061-9532","authenticated-orcid":false,"given":"Jongpil","family":"Jeong","sequence":"additional","affiliation":[{"name":"Department of Smart Factory Convergence, Sungkyunkwan University, 2066 Seobu-ro, Jangan-gu, Suwon-si 16419, Gyeonggi-do, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,7,26]]},"reference":[{"key":"ref_1","unstructured":"Lee, D., Lee, J., Park, J., Choi, J., and Choe, T. (2021, January 25\u201327). Anomaly Detection in Rotating Motor using Two-level LSTM. Proceedings of the KIIT Conference, Jeju Island, Republic of Korea."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2046","DOI":"10.1109\/TIM.2016.2570398","article-title":"Anomaly detection and fault prognosis for bearings","volume":"65","author":"Jin","year":"2016","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"108760","DOI":"10.1016\/j.compeleceng.2023.108760","article-title":"Artificial intelligence of things based approach for anomaly detection in rotating machines","volume":"109","author":"Mian","year":"2023","journal-title":"Comput. Electr. Eng."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Panza, M.A., Pota, M., and Esposito, M. (2023). Anomaly Detection Methods for Industrial Applications: A Comparative Study. Electronics, 12.","DOI":"10.3390\/electronics12183971"},{"key":"ref_5","unstructured":"O\u2019shea, K., and Nash, R. (2015). An introduction to convolutional neural networks. arXiv."},{"key":"ref_6","unstructured":"Staudemeyer, R.C., and Morris, E.R. (2019). Understanding LSTM\u2013A tutorial into long short-term memory recurrent neural networks. arXiv."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1109\/5254.708428","article-title":"Support vector machines","volume":"13","author":"Hearst","year":"1998","journal-title":"IEEE Intell. Syst. Their Appl."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Chen, T., and Guestrin, C. (2016, January 13\u201317). Xgboost: A scalable tree boosting system. Proceedings of the 22nd ACM Sigkdd International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"214","DOI":"10.1016\/j.sbspro.2015.10.090","article-title":"Maintenance management and lean manufacturing practices in a firm which produces dairy products","volume":"207","author":"Arslankaya","year":"2015","journal-title":"Procedia-Soc. Behav. Sci."},{"key":"ref_10","first-page":"2118","article-title":"Unsupervised deep anomaly detection for multi-sensor time-series signals","volume":"35","author":"Zhang","year":"2021","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"93155","DOI":"10.1109\/ACCESS.2020.2990528","article-title":"Bearing fault detection and diagnosis using case western reserve university dataset with deep learning approaches: A review","volume":"8","author":"Neupane","year":"2020","journal-title":"IEEE Access"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"104522","DOI":"10.1016\/j.conengprac.2020.104522","article-title":"Anomaly detection in the fan system of a thermal power plant monitored by continuous and two-valued variables","volume":"102","author":"Wang","year":"2020","journal-title":"Control Eng. Pract."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"2315","DOI":"10.1109\/JIOT.2017.2737479","article-title":"Motor anomaly detection for unmanned aerial vehicles using reinforcement learning","volume":"5","author":"Lu","year":"2017","journal-title":"IEEE Internet Things J."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"e17584","DOI":"10.1016\/j.heliyon.2023.e17584","article-title":"Machine learning for fault analysis in rotating machinery: A comprehensive review","volume":"9","author":"Das","year":"2023","journal-title":"Heliyon"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Chalapathy, R., and Chawla, S. (2019). Deep learning for anomaly detection: A survey. arXiv.","DOI":"10.1145\/3394486.3406704"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Ahmad, S., Styp-Rekowski, K., Nedelkoski, S., and Kao, O. (2020, January 10\u201313). Autoencoder-based condition monitoring and anomaly detection method for rotating machines. Proceedings of the 2020 IEEE International Conference on Big Data (Big Data), Atlanta, GA, USA.","DOI":"10.1109\/BigData50022.2020.9378015"},{"key":"ref_17","first-page":"3","article-title":"Gear diagnostics based on LSTM anomaly detection","volume":"24","author":"Wang","year":"2021","journal-title":"Int. J. Comadem"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2473","DOI":"10.1007\/s12206-018-0504-2","article-title":"Anomaly detection of tripod shafts using modified Mahalanobis distance","volume":"32","author":"Lee","year":"2018","journal-title":"J. Mech. Sci. Technol."},{"key":"ref_19","first-page":"1","article-title":"Anomaly Detection based on 1D-CNN-LSTM Auto-Encoder for Bearing Data","volume":"20","author":"Lee","year":"2023","journal-title":"WSEAS Trans. Inf. Sci. Appl."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1016\/j.epsr.2008.06.004","article-title":"Early detection of stator voltage imbalance in three-phase induction motors","volume":"79","author":"Samsi","year":"2009","journal-title":"Electr. Power Syst. Res."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Pollak, A., Temich, S., Ptasi\u0144ski, W., Kucharczyk, J., and G\u0105siorek, D. (2021). Prediction of belt drive faults in case of predictive maintenance in industry 4.0 platform. Appl. Sci., 11.","DOI":"10.3390\/app112110307"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Bao, J., Adcock, J., Li, S., and Jiang, Y. (2023). Enhancing Quality Control of Chip Seal Construction through Machine Learning-Based Analysis of Surface Macrotexture Metrics. Lubricants, 11.","DOI":"10.20944\/preprints202308.2013.v1"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"115641","DOI":"10.1016\/j.jsv.2020.115641","article-title":"Mechanism and method for the full-scale quantitative diagnosis of ball bearings with an inner race fault","volume":"488","author":"Zhang","year":"2020","journal-title":"J. Sound Vib."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"J\u00edrov\u00e1, R., Pe\u0161\u00edk, L., \u017dul\u2019ov\u00e1, L., and Grega, R. (2023). Method of failure diagnostics to linear rolling guides in handling machines. Sensors, 23.","DOI":"10.3390\/s23073770"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"107536","DOI":"10.1016\/j.ijmecsci.2022.107536","article-title":"Vibration characteristics of bearing-rotor systems with inner ring dynamic misalignment","volume":"230","author":"Xu","year":"2022","journal-title":"Int. J. Mech. Sci."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"107050","DOI":"10.1016\/j.ymssp.2020.107050","article-title":"High-speed train wheel set bearing fault diagnosis and prognostics: A new prognostic model based on extendable useful life","volume":"146","author":"Xu","year":"2021","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"107518","DOI":"10.1016\/j.engfailanal.2023.107518","article-title":"A review of bearing failure Modes, mechanisms and causes","volume":"152","author":"Xu","year":"2023","journal-title":"Eng. Fail. Anal."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Zhang, Y., and Lei, Y. (2021). Data anomaly detection of bridge structures using convolutional neural network based on structural vibration signals. Symmetry, 13.","DOI":"10.3390\/sym13071186"},{"key":"ref_29","unstructured":"Nunes, E.C. (2021). Anomalous sound detection with machine learning: A systematic review. arXiv."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Liu, W., Jiang, H., Che, D., Chen, L., and Jiang, Q. (2020, January 7\u20139). A Real-time Temperature Anomaly Detection Method for IoT Data. Proceedings of the IoTBDS, Prague, Czech Republic.","DOI":"10.5220\/0009410001120118"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Kao, J.B., and Jiang, J.R. (2019, January 3\u20136). Anomaly detection for univariate time series with statistics and deep learning. Proceedings of the 2019 IEEE Eurasia Conference on IOT, Communication and Engineering (ECICE), Yunlin, Taiwan.","DOI":"10.1109\/ECICE47484.2019.8942727"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"106037","DOI":"10.1016\/j.engappai.2023.106037","article-title":"Anomaly detection of train wheels utilizing short-time Fourier transform and unsupervised learning algorithms","volume":"122","author":"Wan","year":"2023","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_33","first-page":"1","article-title":"Multiscale wavelet graph autoencoder for multivariate time-series anomaly detection","volume":"72","author":"Wang","year":"2022","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_34","first-page":"5","article-title":"Lightweight LAE for Anomaly Detection with Sound based Architecture in Smart Poultry Farm","volume":"11","author":"Goyal","year":"2023","journal-title":"IEEE Internet Things J."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"478","DOI":"10.1016\/j.neunet.2023.11.047","article-title":"A filter-augmented auto-encoder with learnable normalization for robust multivariate time series anomaly detection","volume":"170","author":"Yu","year":"2024","journal-title":"Neural Netw."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"6439","DOI":"10.1007\/s11071-024-09389-y","article-title":"An intelligent bearing fault diagnosis framework: One-dimensional improved self-attention-enhanced CNN and empirical wavelet transform","volume":"112","author":"Dong","year":"2024","journal-title":"Nonlinear Dyn."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1007\/s40436-023-00464-y","article-title":"A data-driven approach to RUL prediction of tools","volume":"12","author":"Li","year":"2024","journal-title":"Adv. Manuf."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Lee, D., Choo, H., and Jeong, J. (2023, January 14\u201316). Leak Detection and Classification of Water Pipeline Data Using LSTM Auto-Encoder with Xavier Initialization. Proceedings of the 2023 IEEE\/ACIS 8th International Conference on Big Data, Cloud Computing, and Data Science (BCD), Hochimin City, Vietnam.","DOI":"10.1109\/BCD57833.2023.10466341"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"112161","DOI":"10.1016\/j.nucengdes.2023.112161","article-title":"Attention-based time series analysis for data-driven anomaly detection in nuclear power plants","volume":"404","author":"Dong","year":"2023","journal-title":"Nucl. Eng. Des."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"10509","DOI":"10.1007\/s00500-023-08467-4","article-title":"ALAE: Self-attention reconstruction network for multivariate time series anomaly identification","volume":"27","author":"Jiang","year":"2023","journal-title":"Soft Comput."},{"key":"ref_41","unstructured":"Xu, J., Wu, H., Wang, J., and Long, M. (2021). Anomaly transformer: Time series anomaly detection with association discrepancy. arXiv."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Wu, G., Zhao, Z., Fu, G., Wang, H., Wang, Y., Wang, Z., Hou, J., and Huang, L. (2019, January 12\u201314). A Fast k NN-Based Approach for Time Sensitive Anomaly Detection over Data Streams. Proceedings of the International Conference on Computational Science, Computational Science in the Interconnected World, Faro, Portugal.","DOI":"10.1007\/978-3-030-22741-8_5"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Hu, Z., Wu, T., Zhang, Y., Li, J., and Jiang, L. (2020, January 16\u201318). Time series anomaly detection based on graph convolutional networks. Proceedings of the 2020 2nd International Conference on Applied Machine Learning (ICAML), Changsha, China.","DOI":"10.1109\/ICAML51583.2020.00036"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Oluwasanmi, A., Aftab, M.U., Baagyere, E., Qin, Z., Ahmad, M., and Mazzara, M. (2021). Attention autoencoder for generative latent representational learning in anomaly detection. Sensors, 22.","DOI":"10.3390\/s22010123"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Luong, M.T., Pham, H., and Manning, C.D. (2015). Effective approaches to attention-based neural machine translation. arXiv.","DOI":"10.18653\/v1\/D15-1166"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/15\/4855\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:24:07Z","timestamp":1760109847000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/15\/4855"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7,26]]},"references-count":45,"journal-issue":{"issue":"15","published-online":{"date-parts":[[2024,8]]}},"alternative-id":["s24154855"],"URL":"https:\/\/doi.org\/10.3390\/s24154855","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,7,26]]}}}