{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T07:49:52Z","timestamp":1776152992801,"version":"3.50.1"},"reference-count":49,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["52475125"],"award-info":[{"award-number":["52475125"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Engineering Applications of Artificial Intelligence"],"published-print":{"date-parts":[[2026,6]]},"DOI":"10.1016\/j.engappai.2026.114548","type":"journal-article","created":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T10:50:56Z","timestamp":1773831056000},"page":"114548","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Dynamic vision-based machinery intelligent fault diagnosis with robustness on camera positions"],"prefix":"10.1016","volume":"174","author":[{"given":"Xiang","family":"Li","sequence":"first","affiliation":[]},{"given":"Peng","family":"Yu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3015-3580","authenticated-orcid":false,"given":"Bin","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Yaguo","family":"Lei","sequence":"additional","affiliation":[]},{"given":"Naipeng","family":"Li","sequence":"additional","affiliation":[]},{"given":"Ke","family":"Feng","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.engappai.2026.114548_b1","doi-asserted-by":"crossref","first-page":"2488","DOI":"10.1109\/TIP.2021.3052070","article-title":"SPADE-E2VID: Spatially-adaptive denormalization for event-based video reconstruction","volume":"30","author":"Cadena","year":"2021","journal-title":"IEEE Trans. Image Process."},{"issue":"4","key":"10.1016\/j.engappai.2026.114548_b2","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1109\/MSP.2020.2985815","article-title":"Event-based neuromorphic vision for autonomous driving: A paradigm shift for bio-inspired visual sensing and perception","volume":"37","author":"Chen","year":"2020","journal-title":"IEEE Signal Process. Mag."},{"issue":"9","key":"10.1016\/j.engappai.2026.114548_b3","doi-asserted-by":"crossref","first-page":"9251","DOI":"10.1109\/TCYB.2022.3164882","article-title":"Neuromorphic vision-based fall localization in event streams with temporal\u2013spatial attention weighted network","volume":"52","author":"Chen","year":"2022","journal-title":"IEEE Trans. Cybern."},{"key":"10.1016\/j.engappai.2026.114548_b4","doi-asserted-by":"crossref","DOI":"10.1016\/j.rcim.2025.102982","article-title":"Integrating large language model and digital twins in the context of industry 5.0: Framework, challenges and opportunities","volume":"94","author":"Chen","year":"2025","journal-title":"Robot. Comput.-Integr. Manuf."},{"key":"10.1016\/j.engappai.2026.114548_b5","doi-asserted-by":"crossref","DOI":"10.1016\/j.est.2026.120566","article-title":"Generalized foundation model for lithium-ion battery state-of-health prediction with distribution metric learning","volume":"150","author":"Gong","year":"2026","journal-title":"J. Energy Storage"},{"key":"10.1016\/j.engappai.2026.114548_b6","doi-asserted-by":"crossref","unstructured":"Guang, R., Li, X., Lei, Y., Li, N., Yang, B., 2023. Non-Contact Machine Vibration Sensing and Fault Diagnosis Method Based on Event Camera. In: Proceedings of CAA Symposium on Fault Detection, Supervision and Safety for Technical Processes. SAFEPROCESS, pp. 1\u20135.","DOI":"10.1109\/SAFEPROCESS58597.2023.10295762"},{"key":"10.1016\/j.engappai.2026.114548_b7","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2025.112445","article-title":"Dynamic vision-based machine vibration sensing and fault diagnosis with signal alignment and feature clustering","volume":"162","author":"Guang","year":"2025","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.engappai.2026.114548_b8","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2025.111562","article-title":"Hierarchical fault diagnosis of train communication networks based on cross-dimensional information fusion and mixture-of-head attention mechanism","volume":"265","author":"He","year":"2026","journal-title":"Reliab. Eng. Syst. Saf."},{"issue":"1","key":"10.1016\/j.engappai.2026.114548_b9","first-page":"9","article-title":"The effect of signal propagation delay on the measured vibration in planetary gearboxes","volume":"1","author":"Hilbert","year":"2022","journal-title":"J. Dyn. Monit. Diagn."},{"key":"10.1016\/j.engappai.2026.114548_b10","doi-asserted-by":"crossref","DOI":"10.1016\/j.ymssp.2022.108947","article-title":"High-speed train wheel set bearing fault diagnosis and prognostics: Fingerprint feature recognition method based on acoustic emission","volume":"171","author":"Hou","year":"2022","journal-title":"Mech. Syst. Signal Process."},{"issue":"1","key":"10.1016\/j.engappai.2026.114548_b11","first-page":"13","article-title":"Compound fault diagnosis for rotating machinery: State-of-the-Art, challenges, and opportunities","volume":"2","author":"Huang","year":"2023","journal-title":"J. Dyn. Monit. Diagn."},{"key":"10.1016\/j.engappai.2026.114548_b12","doi-asserted-by":"crossref","first-page":"349","DOI":"10.1016\/j.ymssp.2018.03.025","article-title":"Deep normalized convolutional neural network for imbalanced fault classification of machinery and its understanding via visualization","volume":"110","author":"Jia","year":"2018","journal-title":"Mech. Syst. Signal Process."},{"issue":"8","key":"10.1016\/j.engappai.2026.114548_b13","doi-asserted-by":"crossref","first-page":"1710","DOI":"10.1109\/TNNLS.2014.2352401","article-title":"Asynchronous event-based multikernel algorithm for high-speed visual features tracking","volume":"26","author":"Lagorce","year":"2015","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"10.1016\/j.engappai.2026.114548_b14","doi-asserted-by":"crossref","DOI":"10.1016\/j.ymssp.2021.108487","article-title":"A perspective survey on deep transfer learning for fault diagnosis in industrial scenarios: Theories, applications and challenges","volume":"167","author":"Li","year":"2022","journal-title":"Mech. Syst. Signal Process."},{"key":"10.1016\/j.engappai.2026.114548_b15","doi-asserted-by":"crossref","DOI":"10.1016\/j.est.2025.118989","article-title":"Intelligent domain-generalized second-life EV battery state-of-health estimation","volume":"140","author":"Li","year":"2025","journal-title":"J. Energy Storage"},{"key":"10.1016\/j.engappai.2026.114548_b16","first-page":"1","article-title":"Enhancing 3-D LiDAR point clouds with event-based camera","volume":"70","author":"Li","year":"2021","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"10.1016\/j.engappai.2026.114548_b17","doi-asserted-by":"crossref","DOI":"10.1016\/j.cjme.2025.100163","article-title":"XJTU-DV: Open-source dynamic vision dataset for non-contact vibration measurement and fault diagnosis of mechanical systems","volume":"39","author":"Li","year":"2026","journal-title":"Chin. J. Mech. Eng."},{"key":"10.1016\/j.engappai.2026.114548_b18","doi-asserted-by":"crossref","first-page":"523","DOI":"10.1016\/j.jmsy.2023.05.006","article-title":"Federated transfer learning in fault diagnosis under data privacy with target self-adaptation","volume":"68","author":"Li","year":"2023","journal-title":"J. Manuf. Syst."},{"key":"10.1016\/j.engappai.2026.114548_b19","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2025.111601","article-title":"Interpretable attention-based prototype network for UAV fault diagnosis under small sample conditions","volume":"265","author":"Liang","year":"2026","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"10.1016\/j.engappai.2026.114548_b20","doi-asserted-by":"crossref","DOI":"10.1016\/j.aei.2025.103300","article-title":"Neuromorphic computing-enabled generalized machine fault diagnosis with dynamic vision","volume":"65","author":"Liu","year":"2025","journal-title":"Adv. Eng. Inform."},{"key":"10.1016\/j.engappai.2026.114548_b21","doi-asserted-by":"crossref","DOI":"10.1016\/j.ymssp.2021.108664","article-title":"Imbalanced fault diagnosis of rolling bearing using improved MsR-GAN and feature enhancement-driven CapsNet","volume":"168","author":"Liu","year":"2022","journal-title":"Mech. Syst. Signal Process."},{"issue":"3","key":"10.1016\/j.engappai.2026.114548_b22","doi-asserted-by":"crossref","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 Trans. Ind. Electron."},{"key":"10.1016\/j.engappai.2026.114548_b23","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1016\/j.neunet.2021.03.019","article-title":"Real-time face & eye tracking and blink detection using event cameras","volume":"141","author":"Ryan","year":"2021","journal-title":"Neural Netw."},{"key":"10.1016\/j.engappai.2026.114548_b24","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2025.113017","article-title":"Physics-informed auto-encoder based on digital twin for rolling bearing fault diagnosis under imbalanced sample conditions","volume":"163","author":"Shang","year":"2026","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.engappai.2026.114548_b25","doi-asserted-by":"crossref","DOI":"10.1109\/TVT.2022.3158436","article-title":"Contactless fault diagnosis for railway point machines based on multi-scale fractional wavelet packet energy entropy and synchronous optimization strategy","author":"Sun","year":"2022","journal-title":"IEEE Trans. Veh. Technol."},{"issue":"3","key":"10.1016\/j.engappai.2026.114548_b26","doi-asserted-by":"crossref","first-page":"1522","DOI":"10.1109\/TMECH.2021.3084956","article-title":"A robust deep learning network for low-speed machinery fault diagnosis based on multi-kernel and RPCA","volume":"27","author":"Tang","year":"2022","journal-title":"IEEE\/ASME Trans. Mechatronics"},{"key":"10.1016\/j.engappai.2026.114548_b27","doi-asserted-by":"crossref","unstructured":"Vitale, A., Renner, A., Nauer, C., Scaramuzza, D., Sandamirskaya, Y., 2021. Event-driven Vision and Control for UAVs on a Neuromorphic Chip. In: Proceedings of 2021 IEEE International Conference on Robotics and Automation. ICRA, pp. 103\u2013109.","DOI":"10.1109\/ICRA48506.2021.9560881"},{"key":"10.1016\/j.engappai.2026.114548_b28","first-page":"1","article-title":"Multisource domain feature adaptation network for bearing fault diagnosis under time-varying working conditions","volume":"71","author":"Wang","year":"2022","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"10.1016\/j.engappai.2026.114548_b29","first-page":"1","article-title":"VisEvent: Reliable object tracking via collaboration of frame and event flows","author":"Wang","year":"2023","journal-title":"IEEE Trans. Cybern."},{"key":"10.1016\/j.engappai.2026.114548_b30","doi-asserted-by":"crossref","DOI":"10.1016\/j.compind.2025.104386","article-title":"Deep learning-powered heating, ventilation, and air conditioning compressor fault diagnosis facing unseen domains and class imbalances","volume":"173","author":"Wang","year":"2025","journal-title":"Comput. Ind."},{"key":"10.1016\/j.engappai.2026.114548_b31","article-title":"A simulation-to-reality transfer learning method based on Kolmogorov-Arnold network enhanced model for bearing fault diagnosis","volume":"69","author":"Wang","year":"2026","journal-title":"Adv. Eng. Inform."},{"issue":"4","key":"10.1016\/j.engappai.2026.114548_b32","first-page":"246","article-title":"Industrial battery state-of-health estimation with incomplete limited data toward second-life applications","volume":"3","author":"Yang","year":"2024","journal-title":"J. Dyn. Monit. Diagn."},{"key":"10.1016\/j.engappai.2026.114548_b33","doi-asserted-by":"crossref","DOI":"10.1016\/j.ymssp.2021.108095","article-title":"Multi-source transfer learning network to complement knowledge for intelligent diagnosis of machines with unseen faults","volume":"162","author":"Yang","year":"2022","journal-title":"Mech. Syst. Signal Process."},{"issue":"11","key":"10.1016\/j.engappai.2026.114548_b34","doi-asserted-by":"crossref","first-page":"2359","DOI":"10.1109\/JAS.2024.125007","article-title":"Dynamic vision-enabled intelligent micro-vibration estimation method with spatiotemporal pattern consistency","volume":"12","author":"Yu","year":"2025","journal-title":"IEEE\/CAA J. Autom. Sin."},{"key":"10.1016\/j.engappai.2026.114548_b35","article-title":"Multimodal data-enabled large model for machine fault diagnosis towards intelligent operation and maintenance","volume":"50","author":"Yu","year":"2026","journal-title":"J. Ind. Inf. Integr."},{"key":"10.1016\/j.engappai.2026.114548_b36","article-title":"Deep learning-based open set fault diagnosis by extreme value theory","author":"Yu","year":"2021","journal-title":"IEEE Trans. Ind. Inform."},{"key":"10.1016\/j.engappai.2026.114548_b37","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2025.114283","article-title":"State of charge prediction for lithium-ion batteries in electric aircraft based on self-supervised informer","volume":"186","author":"Zhang","year":"2026","journal-title":"Appl. Soft Comput."},{"key":"10.1016\/j.engappai.2026.114548_b38","doi-asserted-by":"crossref","DOI":"10.1016\/j.ymssp.2021.108576","article-title":"Bearing fault diagnosis via generalized logarithm sparse regularization","volume":"167","author":"Zhang","year":"2022","journal-title":"Mech. Syst. Signal Process."},{"issue":"7","key":"10.1016\/j.engappai.2026.114548_b39","doi-asserted-by":"crossref","DOI":"10.1088\/1361-6501\/ade552","article-title":"Federated transfer learning for remaining useful life prediction in prognostics with data privacy","volume":"36","author":"Zhang","year":"2025","journal-title":"Meas. Sci. Technol."},{"issue":"4","key":"10.1016\/j.engappai.2026.114548_b40","doi-asserted-by":"crossref","first-page":"1329","DOI":"10.1177\/14759217211029201","article-title":"Data privacy preserving federated transfer learning in machinery fault diagnostics using prior distributions","volume":"21","author":"Zhang","year":"2021","journal-title":"Struct. Health Monit."},{"issue":"1","key":"10.1016\/j.engappai.2026.114548_b41","doi-asserted-by":"crossref","first-page":"430","DOI":"10.1109\/TMECH.2021.3065522","article-title":"Federated transfer learning for intelligent fault diagnostics using deep adversarial networks with data privacy","volume":"27","author":"Zhang","year":"2022","journal-title":"IEEE\/ASME Trans. Mechatronics"},{"key":"10.1016\/j.engappai.2026.114548_b42","doi-asserted-by":"crossref","DOI":"10.1016\/j.measurement.2020.108052","article-title":"Deep learning-based prognostic approach for lithium-ion batteries with adaptive time-series prediction and on-line validation","volume":"164","author":"Zhang","year":"2020","journal-title":"Measurement"},{"key":"10.1016\/j.engappai.2026.114548_b43","article-title":"Open set domain adaptation in machinery fault diagnostics using instance-level weighted adversarial learning","author":"Zhang","year":"2021","journal-title":"IEEE Trans. Ind. Inform."},{"key":"10.1016\/j.engappai.2026.114548_b44","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2021.107556","article-title":"Transfer learning using deep representation regularization in remaining useful life prediction across operating conditions","volume":"211","author":"Zhang","year":"2021","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"10.1016\/j.engappai.2026.114548_b45","article-title":"Blockchain-based decentralized federated transfer learning methodology for collaborative machinery fault diagnosis","author":"Zhang","year":"2022","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"10.1016\/j.engappai.2026.114548_b46","doi-asserted-by":"crossref","DOI":"10.1016\/j.measurement.2023.114051","article-title":"Data-driven deep learning approach for thrust prediction of solid rocket motors","volume":"225","author":"Zhang","year":"2024","journal-title":"Measurement"},{"key":"10.1016\/j.engappai.2026.114548_b47","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2021.106974","article-title":"Joint distribution adaptation network with adversarial learning for rolling bearing fault diagnosis","volume":"222","author":"Zhao","year":"2021","journal-title":"Knowl.-Based Syst."},{"key":"10.1016\/j.engappai.2026.114548_b48","first-page":"1","article-title":"Deep domain generalization combining a priori diagnosis knowledge toward cross-domain fault diagnosis of rolling bearing","volume":"70","author":"Zheng","year":"2021","journal-title":"IEEE Trans. Instrum. Meas."},{"issue":"5","key":"10.1016\/j.engappai.2026.114548_b49","doi-asserted-by":"crossref","first-page":"1433","DOI":"10.1109\/TRO.2021.3062252","article-title":"Event-based stereo visual odometry","volume":"37","author":"Zhou","year":"2021","journal-title":"IEEE Trans. Robot."}],"container-title":["Engineering Applications of Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0952197626008298?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0952197626008298?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T06:55:56Z","timestamp":1776149756000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0952197626008298"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6]]},"references-count":49,"alternative-id":["S0952197626008298"],"URL":"https:\/\/doi.org\/10.1016\/j.engappai.2026.114548","relation":{},"ISSN":["0952-1976"],"issn-type":[{"value":"0952-1976","type":"print"}],"subject":[],"published":{"date-parts":[[2026,6]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Dynamic vision-based machinery intelligent fault diagnosis with robustness on camera positions","name":"articletitle","label":"Article Title"},{"value":"Engineering Applications of Artificial Intelligence","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.engappai.2026.114548","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"114548"}}