{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T13:26:21Z","timestamp":1772717181343,"version":"3.50.1"},"reference-count":45,"publisher":"Oxford University Press (OUP)","issue":"7","license":[{"start":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T00:00:00Z","timestamp":1751328000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/100004358","name":"Samsung Electronics Co., Ltd.","doi-asserted-by":"publisher","award":["IO200507-07320-01"],"award-info":[{"award-number":["IO200507-07320-01"]}],"id":[{"id":"10.13039\/100004358","id-type":"DOI","asserted-by":"publisher"}]},{"name":"International Research & Development Program of the National Research Foundation of Korea"},{"DOI":"10.13039\/501100014188","name":"Ministry of Science and ICT","doi-asserted-by":"publisher","award":["2022K1A4A7A04096329"],"award-info":[{"award-number":["2022K1A4A7A04096329"]}],"id":[{"id":"10.13039\/501100014188","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,7,11]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Fault diagnosis of overhead hoist transports (OHTs) is crucial in semiconductor manufacturing, where OHT failures can halt wafer transfers between tightly synchronized processes, leading to significant downtime and potential wafer damage. However, developing a practically applicable fault diagnosis framework for a fleet of OHTs is challenging due to significant variability in torque signals across different units, the limited availability of labeled data, and the need for interpretability to support to support on-site decision-making. To address these issues, this article proposes a novel approach called the fleet-level semi-supervised domain adaptation network, which enables robust and interpretable OHT fault diagnosis. The proposed method employs a semi-supervised domain adaptation strategy to mitigate domain discrepancies between units and enhance diagnostic performance using both unlabeled and labeled data. Also, the method processes dual-motor torque signals from the front and rear motors to physically meaningful signals and extracts features using a multi-head convolutional neural network (CNN) structure. A feature-weighting module is incorporated to dynamically highlight informative features, which not only enhances diagnostic performance but also improves the interpretability of the diagnostic process. The validation of this method is performed using a dataset logged from OHT units that were in actual operation across multiple semiconductor manufacturing lines, demonstrating superior fault diagnosis performance and high practical applicability under limited labeling conditions. Moreover, the model provides interpretable diagnostic insights by analyzing multi-head weight contributions, enabling a more reliable assessment of its health conditions.<\/jats:p>","DOI":"10.1093\/jcde\/qwaf058","type":"journal-article","created":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T11:19:35Z","timestamp":1751368775000},"page":"49-60","source":"Crossref","is-referenced-by-count":1,"title":["FL-SSDAN: Fleet-level semi-supervised domain adaptation network for fault diagnosis of overhead hoist transports"],"prefix":"10.1093","volume":"12","author":[{"given":"Chaehyun","family":"Suh","sequence":"first","affiliation":[{"name":"Seoul National University Department of Mechanical Engineering, , Seoul 08826 ,","place":["Republic of Korea"]}]},{"given":"Hyeongmin","family":"Kim","sequence":"additional","affiliation":[{"name":"Seoul National University Department of Mechanical Engineering, , Seoul 08826 ,","place":["Republic of Korea"]},{"name":"Seoul National University Institute of Advanced Machines and Design, , Seoul 08826 ,","place":["Republic of Korea"]}]},{"given":"Chan Hee","family":"Park","sequence":"additional","affiliation":[{"name":"University of Seoul Department of Mechanical and Information Engineering, , Seoul 02504 ,","place":["Republic of Korea"]}]},{"given":"Minseok","family":"Chae","sequence":"additional","affiliation":[{"name":"Seoul National University Department of Mechanical Engineering, , Seoul 08826 ,","place":["Republic of Korea"]}]},{"given":"Joung Taek","family":"Yoon","sequence":"additional","affiliation":[{"name":"Samsung Electronics , Suwon 16677 ,","place":["Republic of Korea"]}]},{"given":"Ilkyu","family":"Lee","sequence":"additional","affiliation":[{"name":"Samsung Electronics , Suwon 16677 ,","place":["Republic of Korea"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8265-4638","authenticated-orcid":false,"given":"Heonjun","family":"Yoon","sequence":"additional","affiliation":[{"name":"Soongsil University School of Mechanical Engineering, , Seoul 06978 ,","place":["Republic of Korea"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0135-3660","authenticated-orcid":false,"given":"Byeng D","family":"Youn","sequence":"additional","affiliation":[{"name":"Seoul National University Department of Mechanical Engineering, , Seoul 08826 ,","place":["Republic of Korea"]},{"name":"Seoul National University Institute of Advanced Machines and Design, , Seoul 08826 ,","place":["Republic of Korea"]},{"name":"OnePredict Inc. , Seoul 06160 ,","place":["Republic of Korea"]}]}],"member":"286","published-online":{"date-parts":[[2025,7,1]]},"reference":[{"key":"2025071111263519900_bib1","doi-asserted-by":"publisher","first-page":"118802","DOI":"10.1016\/j.eswa.2022.118802","article-title":"Domain adaptation network base on contrastive learning for bearings fault diagnosis under variable working conditions","volume":"212","author":"An","year":"2023","journal-title":"Expert Systems with Applications"},{"key":"2025071111263519900_bib2","doi-asserted-by":"publisher","first-page":"337","DOI":"10.1093\/jcde\/qwae052","article-title":"PCDC: Prototype-assisted dual-contrastive learning with depthwise separable convolutional neural network for few-shot fault diagnosis of permanent magnet synchronous motors under new operating conditions","volume":"11","author":"Chae","year":"2024","journal-title":"Journal of Computational Design and Engineering"},{"key":"2025071111263519900_bib3","doi-asserted-by":"publisher","first-page":"1000","DOI":"10.3390\/en14041000","article-title":"Design and position control of overhang-type rail mover using dual BLAC motor","volume":"14","author":"Cho","year":"2021","journal-title":"Energies"},{"key":"2025071111263519900_bib4","doi-asserted-by":"publisher","first-page":"123969","DOI":"10.1016\/j.eswa.2024.123969","article-title":"Compound fault diagnosis of diesel engines by combining generative adversarial networks and transfer learning","volume":"251","author":"Cui","year":"2024","journal-title":"Expert Systems with Applications"},{"key":"2025071111263519900_bib5","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1093\/jcde\/qwaf021","article-title":"An improved RSMamba network based on multi-domain image fusion for wheelset bearing fault diagnosis under composite conditions","volume":"12","author":"Deng","year":"2025","journal-title":"Journal of Computational Design and Engineering"},{"key":"2025071111263519900_bib6","doi-asserted-by":"publisher","first-page":"327","DOI":"10.1016\/j.isatra.2021.03.042","article-title":"A new dynamic model and transfer learning based intelligent fault diagnosis framework for rolling element bearings race faults: Solving the small sample problem","volume":"121","author":"Dong","year":"2022","journal-title":"ISA Transactions"},{"key":"2025071111263519900_bib7","doi-asserted-by":"publisher","first-page":"108149","DOI":"10.1016\/j.knosys.2022.108149","article-title":"Weighted quantile discrepancy-based deep domain adaptation network for intelligent fault diagnosis","volume":"240","author":"Fan","year":"2022","journal-title":"Knowledge-Based Systems"},{"key":"2025071111263519900_bib8","doi-asserted-by":"publisher","first-page":"107528","DOI":"10.1016\/j.triboint.2022.107528","article-title":"A novel cyclic-correntropy based indicator for gear wear monitoring","volume":"171","author":"Feng","year":"2022","journal-title":"Tribology International"},{"key":"2025071111263519900_bib9","doi-asserted-by":"publisher","first-page":"205806","DOI":"10.1016\/j.wear.2025.205806","article-title":"A digital twin methodology for vibration-based monitoring and prediction of gear wear","volume":"571","author":"Feng","year":"2025","journal-title":"Wear"},{"key":"2025071111263519900_bib10","doi-asserted-by":"publisher","first-page":"381","DOI":"10.1016\/j.isatra.2024.03.033","article-title":"TRA-ACGAN: A motor bearing fault diagnosis model based on an auxiliary classifier generative adversarial network and transformer network","volume":"149","author":"Fu","year":"2024","journal-title":"ISA Transactions"},{"key":"2025071111263519900_bib11","first-page":"1180","article-title":"Unsupervised domain adaptation by backpropagation","author":"Ganin","year":"2015","journal-title":"32nd International Conference on Machine Learning, ICML 2015"},{"key":"2025071111263519900_bib12","first-page":"723","article-title":"A kernel two-sample test","volume":"13","author":"Gretton","year":"2012","journal-title":"Journal of Machine Learning Research"},{"key":"2025071111263519900_bib13","doi-asserted-by":"publisher","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 Transactions on Industrial Electronics"},{"key":"2025071111263519900_bib14","doi-asserted-by":"publisher","first-page":"107150","DOI":"10.1016\/j.asoc.2021.107150","article-title":"Deep transfer learning with limited data for machinery fault diagnosis","volume":"103","author":"Han","year":"2021","journal-title":"Applied Soft Computing"},{"key":"2025071111263519900_bib15","doi-asserted-by":"publisher","first-page":"306","DOI":"10.1093\/jcde\/qwae072","article-title":"Adaptive-conditional loss and correction module enhanced informer network for long-tailed fault diagnosis of motor","volume":"11","author":"Huang","year":"2024","journal-title":"Journal of Computational Design and Engineering"},{"key":"2025071111263519900_bib16","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1093\/jcde\/qwaf040","article-title":"Frequency-enhanced neural networks with a hybrid spall-size estimator for bearing fault diagnosis","volume":"12","author":"Hwang","year":"2025","journal-title":"Journal of Computational Design and Engineering"},{"key":"2025071111263519900_bib17","doi-asserted-by":"publisher","first-page":"1975","DOI":"10.1109\/TII.2023.3280566","article-title":"Uncertainty-aware ensemble combination method for quality monitoring fault diagnosis in safety-related products","volume":"20","author":"Kafunah","year":"2023","journal-title":"IEEE Transactions on Industrial Informatics"},{"key":"2025071111263519900_bib18","doi-asserted-by":"publisher","first-page":"789","DOI":"10.1080\/00207540600675819","article-title":"Effectiveness of vehicle reassignment in a large-scale overhead hoist transport system","volume":"45","author":"Kim","year":"2007","journal-title":"International Journal of Production Research"},{"key":"2025071111263519900_bib19","doi-asserted-by":"publisher","first-page":"860","DOI":"10.1093\/jcde\/qwad031","article-title":"MPARN: Multi-scale path attention residual network for fault diagnosis of rotating machines","volume":"10","author":"Kim","year":"2023","journal-title":"Journal of Computational Design and Engineering"},{"key":"2025071111263519900_bib20","doi-asserted-by":"publisher","first-page":"110293","DOI":"10.1016\/j.ress.2024.110293","article-title":"Gradient alignment based partial domain adaptation (GAPDA) using a domain knowledge filter for fault diagnosis of bearing","volume":"250","author":"Kim","year":"2024","journal-title":"Reliability Engineering and System Safety"},{"key":"2025071111263519900_bib21","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1007\/s001700200137","article-title":"Modelling and performance evaluation of an overhead hoist transport system in a 300 mm fabrication plant","volume":"20","author":"Kuo","year":"2002","journal-title":"International Journal of Advanced Manufacturing Technology"},{"key":"2025071111263519900_bib22","doi-asserted-by":"publisher","first-page":"119463","DOI":"10.1016\/j.eswa.2022.119463","article-title":"Detection and analysis of shaft misalignment in application of production and logistics systems using motor current signature analysis","volume":"217","author":"Lee","year":"2023","journal-title":"Expert Systems with Applications"},{"key":"2025071111263519900_bib23","doi-asserted-by":"publisher","first-page":"3517410","DOI":"10.1109\/TIM.2021.3080402","article-title":"Convolutional neural network-based bayesian gaussian mixture for intelligent fault diagnosis of rotating machinery","volume":"70","author":"Li","year":"2021","journal-title":"IEEE Transactions on Instrumentation and Measurement"},{"key":"2025071111263519900_bib24","doi-asserted-by":"publisher","first-page":"302","DOI":"10.1109\/TFUZZ.2024.3470960","article-title":"Composite Neuro-fuzzy system-guided cross-modal zero-sample diagnostic framework using multi-source heterogeneous non-contact sensing data","volume":"33","author":"Li","year":"2024","journal-title":"IEEE Transactions on Fuzzy Systems"},{"key":"2025071111263519900_bib25","doi-asserted-by":"publisher","first-page":"2833","DOI":"10.1109\/TII.2020.3008010","article-title":"Intelligent fault diagnosis by fusing domain adversarial training and maximum mean discrepancy via ensemble learning","volume":"17","author":"Li","year":"2021","journal-title":"IEEE Transactions on Industrial Informatics"},{"key":"2025071111263519900_bib26","doi-asserted-by":"publisher","first-page":"103003","DOI":"10.1016\/j.aei.2024.103003","article-title":"Residual attention guided vision transformer with acoustic-vibration signal feature fusion for cross-domain fault diagnosis","volume":"64","author":"Lian","year":"2025","journal-title":"Advanced Engineering Informatics"},{"key":"2025071111263519900_bib27","doi-asserted-by":"publisher","first-page":"2296","DOI":"10.1109\/TIE.2016.2627020","article-title":"Deep model based domain adaptation for fault diagnosis","volume":"64","author":"Lu","year":"2017","journal-title":"IEEE Transactions on Industrial Electronics"},{"key":"2025071111263519900_bib28","doi-asserted-by":"publisher","first-page":"111594","DOI":"10.1016\/j.measurement.2022.111594","article-title":"Attention mechanism in intelligent fault diagnosis of machinery: A review of technique and application","volume":"199","author":"Lv","year":"2022","journal-title":"Measurement: Journal of the International Measurement Confederation"},{"key":"2025071111263519900_bib29","doi-asserted-by":"publisher","first-page":"521","DOI":"10.1016\/j.isatra.2021.11.019","article-title":"A deep transferable motion-adaptive fault detection method for industrial robots using a residual\u2013convolutional neural network","volume":"128","author":"Oh","year":"2022","journal-title":"ISA Transactions"},{"key":"2025071111263519900_bib30","doi-asserted-by":"publisher","first-page":"1804","DOI":"10.1093\/jcde\/qwad076","article-title":"Multi-head de-noising autoencoder-based multi-task model for fault diagnosis of rolling element bearings under various speed conditions","volume":"10","author":"Park","year":"2023","journal-title":"Journal of Computational Design and Engineering"},{"key":"2025071111263519900_bib31","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1093\/jcde\/qwae105","article-title":"Fault frequency band segmentation and domain adaptation with fault simulated signal for fault diagnosis of rolling element bearings","volume":"12","author":"Park","year":"2025","journal-title":"Journal of Computational Design and Engineering"},{"key":"2025071111263519900_bib32","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2024.116437","article-title":"A partial domain adaptation broad learning system for machinery fault diagnosis","volume":"243","author":"Qin","year":"2025","journal-title":"Measurement: Journal of the International Measurement Confederation"},{"key":"2025071111263519900_bib33","doi-asserted-by":"publisher","first-page":"461","DOI":"10.1016\/j.isatra.2024.03.032","article-title":"A gradient aligned domain adversarial network for unsupervised intelligent fault diagnosis of gearboxes","volume":"148","author":"Ran","year":"2024","journal-title":"ISA Transactions"},{"key":"2025071111263519900_bib34","doi-asserted-by":"publisher","first-page":"1423","DOI":"10.1093\/jcde\/qwad067","article-title":"Detecting balling defects using multisource transfer learning in wire arc additive manufacturing","volume":"10","author":"Shin","year":"2023","journal-title":"Journal of Computational Design and Engineering"},{"key":"2025071111263519900_bib35","doi-asserted-by":"publisher","first-page":"2579","DOI":"10.1007\/s10479-011-0841-3","article-title":"Visualizing data using t-SNE","volume":"9","author":"van\u00a0der\u00a0Maaten","year":"2008","journal-title":"Journal of Machine Learning Research"},{"key":"2025071111263519900_bib36","doi-asserted-by":"publisher","first-page":"570","DOI":"10.1016\/j.ymssp.2016.04.035","article-title":"Misalignment detection in induction motors with flexible coupling by means of estimated torque analysis and MCSA","volume":"80","author":"Verucchi","year":"2016","journal-title":"Mechanical Systems and Signal Processing"},{"key":"2025071111263519900_bib37","doi-asserted-by":"publisher","first-page":"3063","DOI":"10.1080\/00207543.2021.1910870","article-title":"Predictive vehicle dispatching method for overhead hoist transport systems in semiconductor fabs","volume":"60","author":"Wan","year":"2022","journal-title":"International Journal of Production Research"},{"key":"2025071111263519900_bib38","doi-asserted-by":"publisher","first-page":"2511","DOI":"10.1109\/TII.2020.3003353","article-title":"Cascade convolutional neural network with progressive optimization for motor fault diagnosis under nonstationary conditions","volume":"17","author":"Wang","year":"2021","journal-title":"IEEE Transactions on Industrial Informatics"},{"key":"2025071111263519900_bib39","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TIM.2020.3035385","article-title":"Intelligent fault diagnosis with deep adversarial domain adaptation","volume":"70","author":"Wang","year":"2021","journal-title":"IEEE Transactions on Instrumentation and Measurement"},{"key":"2025071111263519900_bib40","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-030-01234-2_1","article-title":"CBAM: Convolutional block attention module","volume-title":"Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","author":"Woo","year":"2018"},{"key":"2025071111263519900_bib41","doi-asserted-by":"publisher","first-page":"217","DOI":"10.1016\/j.ymssp.2018.06.030","article-title":"A novel method based on self-sensing motor drive system for misalignment detection","volume":"116","author":"Yao","year":"2019","journal-title":"Mechanical Systems and Signal Processing"},{"key":"2025071111263519900_bib42","doi-asserted-by":"publisher","first-page":"337","DOI":"10.1109\/TSM.2020.2984326","article-title":"Application of ANN for fault detection in overhead transport systems for semiconductor fab","volume":"33","author":"Zhakov","year":"2020","journal-title":"IEEE Transactions on Semiconductor Manufacturing"},{"key":"2025071111263519900_bib43","doi-asserted-by":"publisher","first-page":"108071","DOI":"10.1016\/j.measurement.2020.108071","article-title":"Unsupervised domain adaptation via enhanced transfer joint matching for bearing fault diagnosis","volume":"165","author":"Zhang","year":"2020","journal-title":"Measurement: Journal of the International Measurement Confederation"},{"key":"2025071111263519900_bib44","doi-asserted-by":"publisher","first-page":"108865","DOI":"10.1016\/j.ress.2022.108865","article-title":"An uncertainty-informed framework for trustworthy fault diagnosis in safety-critical applications","volume":"229","author":"Zhou","year":"2023","journal-title":"Reliability Engineering and System Safety"},{"key":"2025071111263519900_bib45","doi-asserted-by":"publisher","first-page":"103055","DOI":"10.1016\/j.aei.2024.103055","article-title":"Digital twin-enabled entropy regularized wavelet attention domain adaptation network for gearboxes fault diagnosis without fault data","volume":"64","author":"Zhu","year":"2025","journal-title":"Advanced Engineering Informatics"}],"container-title":["Journal of Computational Design and Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/jcde\/advance-article-pdf\/doi\/10.1093\/jcde\/qwaf058\/63637021\/qwaf058.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/jcde\/article-pdf\/12\/7\/49\/63637021\/qwaf058.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/jcde\/article-pdf\/12\/7\/49\/63637021\/qwaf058.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,11]],"date-time":"2025-07-11T15:26:45Z","timestamp":1752247605000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/jcde\/article\/12\/7\/49\/8180389"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,1]]},"references-count":45,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2025,7,11]]}},"URL":"https:\/\/doi.org\/10.1093\/jcde\/qwaf058","relation":{},"ISSN":["2288-5048"],"issn-type":[{"value":"2288-5048","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2025,7]]},"published":{"date-parts":[[2025,7,1]]}}}