{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T16:56:20Z","timestamp":1790268980876,"version":"4.1.0"},"reference-count":45,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2023,12,7]],"date-time":"2023-12-07T00:00:00Z","timestamp":1701907200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Fault detection using the domain adaptation technique is one of the more promising methods of solving the domain shift problem, and has therefore been intensively investigated in recent years. However, the domain adaptation method still has elements of impracticality: firstly, domain-specific decision boundaries are not taken into consideration, which often results in poor performance near the class boundary; and secondly, information on the source domain needs to be exploited with priority over information on the target domain, as the source domain can provide a rich dataset. Thus, the real-world implementations of this approach are still scarce. In order to address these issues, a novel fault detection approach based on one-sided domain adaptation for real-world railway door systems is proposed. An anomaly detector created using label-rich source domain data is used to generate distinctive source latent features, and the target domain features are then aligned toward the source latent features in a one-sided way. The performance and sensitivity analyses show that the proposed method is more accurate than alternative methods, with an F1 score of 97.9%, and is the most robust against variation in the input features. The proposed method also bridges the gap between theoretical domain adaptation research and tangible industrial applications. Furthermore, the proposed approach can be applied to conventional railway components and various electro-mechanical actuators. This is because the motor current signals used in this study are primarily obtained from the controller or motor drive, which eliminates the need for extra sensors.<\/jats:p>","DOI":"10.3390\/s23249688","type":"journal-article","created":{"date-parts":[[2023,12,8]],"date-time":"2023-12-08T03:03:33Z","timestamp":1702004613000},"page":"9688","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["A Fault Detection Approach Based on One-Sided Domain Adaptation and Generative Adversarial Networks for Railway Door Systems"],"prefix":"10.3390","volume":"23","author":[{"given":"Minoru","family":"Shimizu","sequence":"first","affiliation":[{"name":"Integrated Vehicle Health Management Centre, Cranfield University, Cranfield MK43 0AL, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2383-5724","authenticated-orcid":false,"given":"Yifan","family":"Zhao","sequence":"additional","affiliation":[{"name":"Centre for Life-Cycle Engineering and Management, Cranfield University, Cranfield MK43 0AL, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1314-0603","authenticated-orcid":false,"given":"Nicolas P.","family":"Avdelidis","sequence":"additional","affiliation":[{"name":"Integrated Vehicle Health Management Centre, Cranfield University, Cranfield MK43 0AL, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,12,7]]},"reference":[{"key":"ref_1","unstructured":"(2012). Condition Monitoring and Diagnostics of Machines\u2014Vocabulary. Standard No. BSI Standards BS ISO 13372:2012."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1007\/s00170-012-4303-0","article-title":"Robustness Study and Reliability Growth Based on Exploratory Design of Experiments and Statistical Analysis: A Case Study Using a Train Door Test Bench","volume":"66","author":"Cauffriez","year":"2012","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"71540","DOI":"10.1109\/ACCESS.2018.2880990","article-title":"Bearing Fault Automatic Classification Based on Deep Learning","volume":"6","author":"Yang","year":"2018","journal-title":"IEEE Access"},{"key":"ref_4","first-page":"390134","article-title":"Gearbox Fault Identification and Classification with Convolutional Neural Networks","volume":"2015","author":"Chen","year":"2015","journal-title":"Shock Vib."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1401","DOI":"10.1177\/1475921718805683","article-title":"Convolutional Neural Network for Gear Fault Diagnosis Based on Signal Segmentation Approach","volume":"18","author":"Kim","year":"2019","journal-title":"Struct. Health Monit."},{"key":"ref_6","first-page":"1539","article-title":"Machine Health Monitoring Using Local Feature-Based Gated Recurrent Unit Networks","volume":"65","author":"Wang","year":"2017","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Zhao, R., Yan, R., Wang, J., and Mao, K. (2017). Learning to Monitor Machine Health with Convolutional Bi-Directional LSTM Networks. Sensors, 17.","DOI":"10.3390\/s17020273"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"377","DOI":"10.1016\/j.sigpro.2016.07.028","article-title":"Fault Diagnosis of Rotary Machinery Components Using a Stacked Denoising Autoencoder-Based Health State Identification","volume":"130","author":"Lu","year":"2017","journal-title":"Signal Process."},{"key":"ref_9","unstructured":"Yan, W., and Yu, L. (2019, January 21\u201326). On Accurate and Reliable Anomaly Detection for Gas Turbine Combustors: A Deep Learning Approach. Proceedings of the Annual Conference of the Prognostics and Health Management Society, PHM, Scottsdale, AZ, USA."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1693","DOI":"10.1109\/TIM.2017.2669947","article-title":"Multisensor Feature Fusion for Bearing Fault Diagnosis Using Sparse Autoencoder and Deep Belief Network","volume":"66","author":"Chen","year":"2017","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_11","unstructured":"Yoon, A.S., Lee, T., Lim, Y., Jung, D., Kang, P., Kim, D., Park, K., and Choi, Y. (2017). Semi-Supervised Learning with Deep Generative Models for Asset Failure Prediction. arXiv."},{"key":"ref_12","first-page":"9306205","article-title":"Bearing Fault Diagnosis Based on Deep Belief Network and Multisensor Information Fusion","volume":"2016","author":"Tao","year":"2016","journal-title":"Shock Vib."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1007\/s10994-009-5152-4","article-title":"A Theory of Learning from Different Domains","volume":"79","author":"Blitzer","year":"2010","journal-title":"Mach. Learn"},{"key":"ref_14","first-page":"1","article-title":"Applications of Unsupervised Deep Transfer Learning to Intelligent Fault Diagnosis: A Survey and Comparative Study","volume":"70","author":"Zhao","year":"2021","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"14347","DOI":"10.1109\/ACCESS.2017.2720965","article-title":"Transfer Learning With Neural Networks for Bearing Fault Diagnosis in Changing Working Conditions","volume":"5","author":"Zhang","year":"2017","journal-title":"IEEE Access"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Zhang, C., Xu, L., Li, X., and Wang, H. (2018, January 26\u201328). A Method of Fault Diagnosis for Rotary Equipment Based on Deep Learning. Proceedings of the 2018 Prognostics and System Health Management Conference, PHM-Chongqing 2018, Chongqing, China.","DOI":"10.1109\/PHM-Chongqing.2018.00171"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"439","DOI":"10.1016\/j.ymssp.2017.06.022","article-title":"A Deep Convolutional Neural Network with New Training Methods for Bearing Fault Diagnosis under Noisy Environment and Different Working Load","volume":"100","author":"Zhang","year":"2018","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_18","unstructured":"Li, Y., Wang, N., Shi, J., Liu, J., and Hou, X. (2017, January 24\u201326). Revisiting Batch Normalization For Practical Domain Adaptation. Proceedings of the 5th International Conference on Learning Representations, ICLR 2017\u2014Workshop Track Proceedings, Toulon, France."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Zhang, W., Peng, G., Li, C., Chen, Y., and Zhang, Z. (2017). A New Deep Learning Model for Fault Diagnosis with Good Anti-Noise and Domain Adaptation Ability on Raw Vibration Signals. Sensors, 17.","DOI":"10.20944\/preprints201701.0132.v1"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"9404","DOI":"10.1109\/TII.2022.3228395","article-title":"A Novel Unsupervised Clustering and Domain Adaptation Framework for Rotating Machinery Fault Diagnosis","volume":"19","author":"Kim","year":"2023","journal-title":"IEEE Trans. Ind. Inf."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"443","DOI":"10.1007\/978-3-319-49409-8_35","article-title":"Deep CORAL: Correlation Alignment for Deep Domain Adaptation","volume":"Volume 9915","author":"Sun","year":"2016","journal-title":"Proceedings of the Computer Vision\u2013ECCV 2016 Workshops"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"e49","DOI":"10.1093\/bioinformatics\/btl242","article-title":"Integrating Structured Biological Data by Kernel Maximum Mean Discrepancy","volume":"22","author":"Borgwardt","year":"2006","journal-title":"Bioinformatics"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"7316","DOI":"10.1109\/TIE.2018.2877090","article-title":"Deep Convolutional Transfer Learning Network: A New Method for Intelligent Fault Diagnosis of Machines with Unlabeled Data","volume":"66","author":"Guo","year":"2019","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_24","first-page":"97","article-title":"Learning Transferable Features with Deep Adaptation Networks","volume":"37","author":"Long","year":"2015","journal-title":"Proc. Mach. Learn. Res."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1016\/j.sigpro.2018.12.005","article-title":"Multi-Layer Domain Adaptation Method for Rolling Bearing Fault Diagnosis","volume":"157","author":"Li","year":"2019","journal-title":"Signal Process."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Long, M., Wang, J., Ding, G., Sun, J., and Yu, P.S. (2013, January 1\u20138). Transfer Feature Learning with Joint Distribution Adaptation. Proceedings of the 2013 IEEE International Conference on Computer Vision, Sydney, NSW, Australia.","DOI":"10.1109\/ICCV.2013.274"},{"key":"ref_27","first-page":"2672","article-title":"Generative Adversarial Nets","volume":"3","author":"Goodfellow","year":"2014","journal-title":"Adv. Neural Inf. Process Syst."},{"key":"ref_28","first-page":"1","article-title":"Domain-Adversarial Training of Neural Networks","volume":"17","author":"Ganin","year":"2016","journal-title":"J. Mach. Learn. Res."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Wang, Q., Michau, G., and Fink, O. (2019, January 2\u20135). Domain Adaptive Transfer Learning for Fault Diagnosis. Proceedings of the 2019 Prognostics and System Health Management Conference (PHM-Paris), Paris, France.","DOI":"10.1109\/PHM-Paris.2019.00054"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"8702","DOI":"10.1109\/TIM.2020.2995441","article-title":"Domain Adversarial Transfer Network for Cross-Domain Fault Diagnosis of Rotary Machinery","volume":"69","author":"Chen","year":"2020","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/j.neucom.2020.05.040","article-title":"Wasserstein Distance Based Deep Adversarial Transfer Learning for Intelligent Fault Diagnosis with Unlabeled or Insufficient Labeled Data","volume":"409","author":"Cheng","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"663","DOI":"10.1109\/TIE.2019.2962438","article-title":"Missing-Class-Robust Domain Adaptation by Unilateral Alignment","volume":"68","author":"Wang","year":"2021","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Saito, K., Watanabe, K., Ushiku, Y., and Harada, T. (2017, January 21\u201326). Maximum Classifier Discrepancy for Unsupervised Domain Adaptation. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2018.00392"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"2035","DOI":"10.1109\/TIM.2018.2814067","article-title":"Data-Based Detection and Diagnosis of Faults in Linear Actuators","volume":"67","author":"Starr","year":"2018","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"103339","DOI":"10.1016\/j.compind.2020.103339","article-title":"Hybrid Modelling for Linear Actuator Diagnosis in Absence of Faulty Data Records","volume":"123","author":"Starr","year":"2020","journal-title":"Comput Ind"},{"key":"ref_36","unstructured":"Ruiz-Carcel, C., and Starr, A. (2022, July 22). Data Set for \u201cData-Based Detection and Diagnosis of Faults in Linear Actuators\u201d. Available online: https:\/\/figshare.com\/s\/dac98f9c1bc46b7a2800."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"96442","DOI":"10.1109\/ACCESS.2022.3205352","article-title":"A Real-Time Fault Detection Framework Based on Unsupervised Deep Learning for Prognostics and Health Management of Railway Assets","volume":"10","author":"Shimizu","year":"2022","journal-title":"IEEE Access"},{"key":"ref_38","unstructured":"Namoano, B. (2020). Fault Diagnosis in Time Series Data with Application to Railway Assets. [Ph.D. Thesis, School of Aerospace, Transport and Manufacturing, Engineering And Management of Manufacturing Systems, Cranfield University]."},{"key":"ref_39","first-page":"723","article-title":"A Kernel Two-Sample Test","volume":"13","author":"Gretton","year":"2012","journal-title":"J. Mach. Learn. Res."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Jennions Ian, K. (2013). Integrated Vehicle Health Management: The Technology, IEEE.","DOI":"10.4271\/R-429"},{"key":"ref_41","unstructured":"Andreas, C., and M\u00fcller, S.G. (2016). Introduction to Machine Learning with Python, O\u2019Reilly Media, Inc."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1109\/TNN.2010.2091281","article-title":"Domain Adaptation via Transfer Component Analysis","volume":"22","author":"Pan","year":"2011","journal-title":"IEEE Trans. Neural. Netw."},{"key":"ref_43","unstructured":"De Mathelin, A., Atiq, M., Richard, G., De La Concha, A., Yachouti, M., Deheeger, F., Mougeot, M., and Vayatis, N. (2021). ADAPT: Awesome Domain Adaptation Python Toolbox. arXiv."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Shimizu, M., Perinpanayagam, S., and Namoano, B. (2022, January 6\u20138). A Fault Detection Technique Based on Deep Transfer Learning from Experimental Linear Actuator to Real-World Railway Door Systems. Proceedings of the Annual Conference of the Prognostics and Health Management Society (Accepted), Turin, Italy.","DOI":"10.36001\/phmconf.2022.v14i1.3378"},{"key":"ref_45","unstructured":"Russell, S., and Norvig, P. (2021). Artificial Intelligence: A Modern Approach, Pearson Education, Limited."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/24\/9688\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:35:01Z","timestamp":1760132101000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/24\/9688"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,7]]},"references-count":45,"journal-issue":{"issue":"24","published-online":{"date-parts":[[2023,12]]}},"alternative-id":["s23249688"],"URL":"https:\/\/doi.org\/10.3390\/s23249688","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,12,7]]}}}