{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,23]],"date-time":"2026-07-23T20:16:06Z","timestamp":1784837766102,"version":"3.55.0"},"reference-count":50,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100009020","name":"University of South China","doi-asserted-by":"publisher","award":["200XOD055"],"award-info":[{"award-number":["200XOD055"]}],"id":[{"id":"10.13039\/501100009020","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100009020","name":"University of South China","doi-asserted-by":"publisher","award":["5525QD049"],"award-info":[{"award-number":["5525QD049"]}],"id":[{"id":"10.13039\/501100009020","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["22409083"],"award-info":[{"award-number":["22409083"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["52275104"],"award-info":[{"award-number":["52275104"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Journal of Industrial Information Integration"],"published-print":{"date-parts":[[2026,7]]},"DOI":"10.1016\/j.jii.2026.101143","type":"journal-article","created":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T07:34:08Z","timestamp":1780299248000},"page":"101143","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["A physics-constrained multimodal LLM for fault diagnosis of pressurized water reactor coolant systems with imbalanced and under-sampled data"],"prefix":"10.1016","volume":"52","author":[{"given":"Keshun","family":"You","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haidong","family":"Shao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhichao","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jie","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xian","family":"Du","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanghui","family":"Lin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yajun","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"8","key":"10.1016\/j.jii.2026.101143_bib0001","doi-asserted-by":"crossref","first-page":"7432","DOI":"10.1016\/j.eswa.2012.01.107","article-title":"Industrial implementation of intelligent system techniques for nuclear power plant condition monitoring","volume":"39","author":"West","year":"2012","journal-title":"Expert. Syst. Appl."},{"key":"10.1016\/j.jii.2026.101143_bib0002","doi-asserted-by":"crossref","DOI":"10.3389\/fenrg.2021.696785","article-title":"Prognostics and health management in nuclear power plants: an updated method-centric review with special focus on data-driven methods","volume":"9","author":"Zhao","year":"2021","journal-title":"Front. Energy Res."},{"issue":"5","key":"10.1016\/j.jii.2026.101143_bib0003","doi-asserted-by":"crossref","first-page":"3017","DOI":"10.1007\/s13369-024-09388-6","article-title":"Application of machine learning and deep learning techniques for corrosion and cracks detection in nuclear power plants: a review","volume":"50","author":"Allah","year":"2025","journal-title":"Arab. J. Sci. Eng."},{"key":"10.1016\/j.jii.2026.101143_bib0004","article-title":"Robust rotating machinery diagnosis from Single-Source heterogeneous signals under imbalanced and noisy conditions","volume":"79","author":"You","year":"2026","journal-title":"Engin. Sci. Tech. Int. J."},{"key":"10.1016\/j.jii.2026.101143_bib0005","doi-asserted-by":"crossref","DOI":"10.1016\/j.oceaneng.2022.111156","article-title":"Advances in nuclear power system design and fault-based condition monitoring towards safety of nuclear-powered ships","volume":"251","author":"Adumene","year":"2022","journal-title":"Ocean Eng."},{"key":"10.1016\/j.jii.2026.101143_bib0006","first-page":"3935","volume":"40","author":"Shao","year":"2025"},{"key":"10.1016\/j.jii.2026.101143_bib0007","doi-asserted-by":"crossref","DOI":"10.1016\/j.ymssp.2026.113923","article-title":"A liquid-impulse neural network model based on heterogeneous fusion of multimodal information for interpretable rotating machinery fault diagnosis","volume":"246","author":"You","year":"2026","journal-title":"Mech. Syst. Signal. Process."},{"issue":"22","key":"10.1016\/j.jii.2026.101143_bib42","doi-asserted-by":"crossref","first-page":"8906","DOI":"10.3390\/s22228906","article-title":"Rolling bearing fault diagnosis using hybrid neural network with principal component analysis","volume":"22","author":"You","year":"2022","journal-title":"Sensors"},{"issue":"9","key":"10.1016\/j.jii.2026.101143_bib43","doi-asserted-by":"crossref","DOI":"10.1088\/1361-6501\/acd5ef","article-title":"An efficient lightweight neural network using BiLSTM-SCN-CBAM with PCA-ICEEMDAN for diagnosing rolling bearing faults","volume":"34","author":"You","year":"2023","journal-title":"Measur. Sci. Tech."},{"issue":"1","key":"10.1016\/j.jii.2026.101143_bib45","doi-asserted-by":"crossref","DOI":"10.1088\/1361-6501\/acfbef","article-title":"Remaining useful life prediction of lithium-ion batteries using EM-PF-SSA-SVR with gamma stochastic process","volume":"35","author":"Keshun","year":"2024","journal-title":"Measur. Sci. Tech."},{"issue":"13","key":"10.1016\/j.jii.2026.101143_bib0008","doi-asserted-by":"crossref","first-page":"23002","DOI":"10.1109\/JIOT.2024.3377731","article-title":"Toward efficient and interpretative rolling bearing fault diagnosis via quadratic neural network with Bi-LSTM","volume":"11","author":"Keshun","year":"2024","journal-title":"IEEE Internet Things J."},{"key":"10.1016\/j.jii.2026.101143_bib46","article-title":"A sparse-to-dense guided fusion framework for three-dimensional object detection in railway environments","volume":"178","author":"Chen","year":"2026","journal-title":"Engin. App. Artific. Intell"},{"key":"10.1016\/j.jii.2026.101143_bib0009","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2024.110556","article-title":"A sound-vibration physical-information fusion constraint-guided deep learning method for rolling bearing fault diagnosis","volume":"253","author":"Keshun","year":"2025","journal-title":"Reliab. Eng. Syst. Saf."},{"issue":"23","key":"10.1016\/j.jii.2026.101143_bib44","doi-asserted-by":"crossref","first-page":"20903","DOI":"10.1007\/s11071-024-10157-1","article-title":"A performance-interpretable intelligent fusion of sound and vibration signals for bearing fault diagnosis via dynamic CAME","volume":"112","author":"Keshun","year":"2024","journal-title":"Nonlinear Dyn."},{"key":"10.1016\/j.jii.2026.101143_bib0010","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1080\/10589759.2025.2534429","article-title":"A novel physical constraint-guided quadratic neural networks for interpretable bearing fault diagnosis under zero-fault sample","author":"Keshun","year":"2025","journal-title":"Nondestruct. Test. Eval."},{"issue":"4","key":"10.1016\/j.jii.2026.101143_bib0011","doi-asserted-by":"crossref","first-page":"601","DOI":"10.1080\/00295639.2022.2123211","article-title":"Physics-informed neural network method and application to nuclear reactor calculations: a pilot study","volume":"197","author":"Elhareef","year":"2023","journal-title":"Nucl. Sci. Eng."},{"key":"10.1016\/j.jii.2026.101143_bib0012","doi-asserted-by":"crossref","DOI":"10.1016\/j.pnucene.2025.105745","article-title":"Physics-informed neural networks for the safety analysis of nuclear reactors","volume":"185","author":"Baraldi","year":"2025","journal-title":"Prog. Nucl. Energy"},{"key":"10.1016\/j.jii.2026.101143_bib0013","doi-asserted-by":"crossref","DOI":"10.1016\/j.aei.2025.103471","article-title":"Physics-informed CGAN and multi-scale attention CNN for pipeline leakage diagnosis under imbalanced data","volume":"66","author":"Zhu","year":"2025","journal-title":"Adv. Eng. Inform."},{"issue":"4","key":"10.1016\/j.jii.2026.101143_bib0014","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1080\/00295639.2019.1698237","article-title":"Integrated state awareness through secure embedded intelligence in nuclear systems: opportunities and implications","volume":"194","author":"Garcia","year":"2020","journal-title":"Nucl. Sci. Eng."},{"key":"10.1016\/j.jii.2026.101143_bib0015","doi-asserted-by":"crossref","DOI":"10.1016\/j.net.2025.103842","article-title":"Large language model agent for nuclear reactor operation assistance","volume":"57","author":"Lee","year":"2025","journal-title":"Nucl. Eng. Technol."},{"issue":"12","key":"10.1016\/j.jii.2026.101143_bib0016","doi-asserted-by":"crossref","DOI":"10.1093\/nsr\/nwae403","article-title":"A survey on multimodal large language models","volume":"11","author":"Yin","year":"2024","journal-title":"Natl. Sci. Rev."},{"key":"10.1016\/j.jii.2026.101143_bib0017","series-title":"European conference on computer vision","first-page":"549","article-title":"Transformer with implicit edges for particle-based physics simulation","author":"Shao","year":"2022"},{"key":"10.1016\/j.jii.2026.101143_bib0018","doi-asserted-by":"crossref","first-page":"19622","DOI":"10.52202\/075280-0861","article-title":"Large language models are zero-shot time series forecasters","volume":"36","author":"Gruver","year":"2023","journal-title":"Adv. Neural Inf. Process. Syst."},{"issue":"11","key":"10.1016\/j.jii.2026.101143_bib0019","doi-asserted-by":"crossref","DOI":"10.1103\/PhysRevFluids.5.110520","article-title":"Multiphase flows: rich physics, challenging theory, and big simulations","volume":"5","author":"Subramaniam","year":"2020","journal-title":"Phys. Rev. Fluids"},{"issue":"5","key":"10.1016\/j.jii.2026.101143_bib0020","doi-asserted-by":"crossref","first-page":"3286","DOI":"10.1109\/TITS.2023.3321309","article-title":"Toward ensuring safety for autonomous driving perception: standardization progress, research advances, and perspectives","volume":"25","author":"Sun","year":"2023","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"10.1016\/j.jii.2026.101143_bib0021","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2025.128592","article-title":"DTMPI-DIVR: a digital twins for multi-margin physical information via dynamic interaction of virtual and real sound-vibration signals for bearing fault diagnosis without real fault samples","volume":"292","author":"Keshun","year":"2025","journal-title":"Expert. Syst. Appl."},{"key":"10.1016\/j.jii.2026.101143_bib47","doi-asserted-by":"crossref","DOI":"10.1109\/TEMC.2026.3674675","article-title":"Joint tailoring of electromagnetic radiated emission and susceptibility limits for coexistence of multiple radio-frequency devices,","author":"Xiao","year":"2026","journal-title":"IEEE Trans. Electromag. Compatib."},{"key":"10.1016\/j.jii.2026.101143_bib48","article-title":"Bio-inspired magnetically triggered snap-through mechanism for enhanced mechanical-to-electrical energy conversion","volume":"176507","author":"Cong","year":"2026","journal-title":"Chem. Engin. J."},{"key":"10.1016\/j.jii.2026.101143_bib49","doi-asserted-by":"crossref","DOI":"10.1016\/j.jechem.2026.03.051","article-title":"Antimony-based architectures for aqueous batteries: Recent advances, challenges, and future directions","author":"Lin","year":"2026","journal-title":"J. Energy Chem."},{"key":"10.1016\/j.jii.2026.101143_bib50","doi-asserted-by":"crossref","DOI":"10.1016\/j.jcat.2026.116940","article-title":"Orbital-tailored MnO2-CNT hybrids for interfacial electron transfer-dominated periodate activation toward selective IO3\u2022 generation in complex water remediation","author":"Lin","year":"2026","journal-title":"J. Catalysis"},{"issue":"8","key":"10.1016\/j.jii.2026.101143_bib0022","doi-asserted-by":"crossref","first-page":"7432","DOI":"10.1016\/j.eswa.2012.01.107","article-title":"Industrial implementation of intelligent system techniques for nuclear power plant condition monitoring","volume":"39","author":"West","year":"2012","journal-title":"Expert. Syst. Appl."},{"key":"10.1016\/j.jii.2026.101143_bib0023","series-title":"2021 International Conference on Electrical, Computer and Energy Technologies (ICECET)","first-page":"1","article-title":"An enhanced fault diagnosis in nuclear power plants for a digital twin framework","author":"Ayo-Imoru","year":"2021"},{"issue":"14","key":"10.1016\/j.jii.2026.101143_bib0024","doi-asserted-by":"crossref","first-page":"21893","DOI":"10.1109\/JSEN.2023.3296670","article-title":"A 3-D attention-enhanced hybrid neural network for turbofan engine remaining life prediction using CNN and BiLSTM models","volume":"24","author":"Keshun","year":"2023","journal-title":"IEEE Sens. J."},{"key":"10.1016\/j.jii.2026.101143_bib0025","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2023.109793","article-title":"Optimizing prior distribution parameters for probabilistic prediction of remaining useful life using deep learning","volume":"242","author":"Keshun","year":"2024","journal-title":"Reliab. Engin. Syst. Safety"},{"key":"10.1016\/j.jii.2026.101143_bib0026","first-page":"1","article-title":"Explainable AI models for enhancing operator reliability during reactor design-based accidents using radionuclide data","author":"Najar","year":"2025","journal-title":"Nucl. Technol."},{"key":"10.1016\/j.jii.2026.101143_bib0027","doi-asserted-by":"crossref","DOI":"10.1016\/j.anucene.2022.109201","article-title":"Pre-trained network-based transfer learning: a small-sample machine learning approach to nuclear power plant classification problem","volume":"175","author":"Zhong","year":"2022","journal-title":"Ann. Nucl. Energy"},{"issue":"5","key":"10.1016\/j.jii.2026.101143_bib0028","doi-asserted-by":"crossref","first-page":"649","DOI":"10.1016\/j.ijhcs.2004.06.001","article-title":"The quality of human-automation cooperation in human-system interface for nuclear power plants","volume":"61","author":"Skjerve","year":"2004","journal-title":"Int. J. Hum. Comput. Stud."},{"issue":"4","key":"10.1016\/j.jii.2026.101143_bib0029","doi-asserted-by":"crossref","first-page":"79","DOI":"10.3390\/jcp5040079","article-title":"A systematic literature review on AI-based cybersecurity in nuclear power plants","volume":"5","author":"Lezzi","year":"2025","journal-title":"J. Cybersecur. Priv."},{"issue":"6","key":"10.1016\/j.jii.2026.101143_bib0030","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0304652","article-title":"An integrated method for the leakage fault mode diagnosis and life prediction of the reactor coolant pump","volume":"19","author":"Shao","year":"2024","journal-title":"PLoS. One"},{"issue":"1","key":"10.1016\/j.jii.2026.101143_bib0031","first-page":"1","article-title":"Towards an international regulatory framework for AI safety: lessons from the IAEA\u2019s nuclear safety regulations","volume":"11","author":"Cha","year":"2024","journal-title":"Humanit. Soc. Sci. Commun."},{"key":"10.1016\/j.jii.2026.101143_bib0041","series-title":"Proceedings of the Human Factors and Ergonomics Society Annual Meeting","first-page":"1686","article-title":"A comparison of displays and associated workload on nuclear power plants tasks","volume":"60","author":"Reinerman-Jones","year":"2016"},{"issue":"5","key":"10.1016\/j.jii.2026.101143_bib0032","doi-asserted-by":"crossref","first-page":"1454","DOI":"10.1007\/s10618-020-00701-z","article-title":"ROCKET: exceptionally fast and accurate time series classification using random convolutional kernels","volume":"34","author":"Dempster","year":"2020","journal-title":"Data Min. Knowl. Discov."},{"issue":"6","key":"10.1016\/j.jii.2026.101143_bib0033","doi-asserted-by":"crossref","first-page":"1936","DOI":"10.1007\/s10618-020-00710-y","article-title":"Inceptiontime: finding alexnet for time series classification","volume":"34","author":"Ismail Fawaz","year":"2020","journal-title":"Data Min. Knowl. Discov."},{"key":"10.1016\/j.jii.2026.101143_bib0034","first-page":"1","volume":"12","author":"Van Den Oord","year":"2016","journal-title":"Wavenet: Gener. Model Raw Audio"},{"issue":"3","key":"10.1016\/j.jii.2026.101143_bib0035","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/j.cosrev.2009.03.005","article-title":"Reservoir computing approaches to recurrent neural network training","volume":"3","author":"Luko\u0161evi\u010dius","year":"2009","journal-title":"Comput. Sci. Rev."},{"key":"10.1016\/j.jii.2026.101143_bib0036","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1016\/j.ins.2013.02.030","article-title":"A time series forest for classification and feature extraction","volume":"239","author":"Deng","year":"2013","journal-title":"Inf. Sci. (Ny)"},{"issue":"2","key":"10.1016\/j.jii.2026.101143_bib0037","doi-asserted-by":"crossref","first-page":"1987","DOI":"10.1109\/TII.2023.3282979","article-title":"Causal-trivial attention graph neural network for fault diagnosis of complex industrial processes","volume":"20","author":"Wang","year":"2023","journal-title":"IEEE Trans. Ind. Inform."},{"key":"10.1016\/j.jii.2026.101143_bib0038","series-title":"Proceedings of the 27th ACM SIGKDD conference on knowledge discovery & data mining","first-page":"2114","article-title":"A transformer-based framework for multivariate time series representation learning","author":"Zerveas","year":"2021"},{"key":"10.1016\/j.jii.2026.101143_bib0039","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1016\/j.neunet.2021.01.001","article-title":"Multi-scale attention convolutional neural network for time series classification","volume":"136","author":"Chen","year":"2021","journal-title":"Neural Netw."},{"key":"10.1016\/j.jii.2026.101143_bib0040","doi-asserted-by":"crossref","first-page":"686","DOI":"10.1016\/j.jcp.2018.10.045","article-title":"Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations","volume":"378","author":"Raissi","year":"2019","journal-title":"J. Comput. Phys."}],"container-title":["Journal of Industrial Information Integration"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2452414X26000853?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2452414X26000853?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,23]],"date-time":"2026-07-23T19:54:30Z","timestamp":1784836470000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S2452414X26000853"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7]]},"references-count":50,"alternative-id":["S2452414X26000853"],"URL":"https:\/\/doi.org\/10.1016\/j.jii.2026.101143","relation":{},"ISSN":["2452-414X"],"issn-type":[{"value":"2452-414X","type":"print"}],"subject":[],"published":{"date-parts":[[2026,7]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"A physics-constrained multimodal LLM for fault diagnosis of pressurized water reactor coolant systems with imbalanced and under-sampled data","name":"articletitle","label":"Article Title"},{"value":"Journal of Industrial Information Integration","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.jii.2026.101143","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"101143"}}