{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,24]],"date-time":"2026-10-24T17:50:03Z","timestamp":1792864203430,"version":"4.5.0"},"reference-count":52,"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"}],"funder":[{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["2025SMECP012"],"award-info":[{"award-number":["2025SMECP012"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100013804","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100013804","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62203118"],"award-info":[{"award-number":["62203118"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["ISA Transactions"],"published-print":{"date-parts":[[2026,7]]},"DOI":"10.1016\/j.isatra.2025.12.006","type":"journal-article","created":{"date-parts":[[2025,12,5]],"date-time":"2025-12-05T08:02:05Z","timestamp":1764921725000},"page":"13-25","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":2,"special_numbering":"C","title":["A bimodal framework for nonstationary process monitoring via collaborative contrastive and adversarial unsupervised learning"],"prefix":"10.1016","volume":"174","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-4854-7614","authenticated-orcid":false,"given":"Jian","family":"Huang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hang","family":"Ruan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4535-7436","authenticated-orcid":false,"given":"Jianbo","family":"Yu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3402-9018","authenticated-orcid":false,"given":"Qingchao","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaofeng","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.isatra.2025.12.006_bib0005","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1016\/j.conengprac.2018.07.012","article-title":"Parallel PCA\u2013KPCA for nonlinear process monitoring","volume":"80","author":"Jiang","year":"2018","journal-title":"Control Eng Practice"},{"key":"10.1016\/j.isatra.2025.12.006_bib0010","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1016\/j.isatra.2024.04.006","article-title":"A multi-rate high-order dynamic twin-latent-variable probabilistic modeling and its process monitoring application","volume":"149","author":"Ying","year":"2024","journal-title":"ISA Trans"},{"issue":"4","key":"10.1016\/j.isatra.2025.12.006_bib0015","doi-asserted-by":"crossref","first-page":"1814","DOI":"10.1109\/TASE.2019.2897477","article-title":"Multimode process monitoring using variational Bayesian inference and canonical correlation analysis","volume":"16","author":"Jiang","year":"2019","journal-title":"IEEE Trans Autom Sci Eng"},{"issue":"2","key":"10.1016\/j.isatra.2025.12.006_bib0020","doi-asserted-by":"crossref","first-page":"11768","DOI":"10.1016\/j.ifacol.2023.10.565","article-title":"Advanced soft-sensor systems for process monitoring, control, optimisation, and fault diagnosis","volume":"56","author":"Shardt","year":"2023","journal-title":"IFAC-PapersOnLine"},{"key":"10.1016\/j.isatra.2025.12.006_bib0025","doi-asserted-by":"crossref","DOI":"10.1016\/j.chemolab.2024.105245","article-title":"Layer-wise-residual-driven approach for soft sensing in composite dynamic system based on slow and fast time-varying latent variables","volume":"254","author":"Zhang","year":"2024","journal-title":"Chemom Intell Lab Syst"},{"issue":"4","key":"10.1016\/j.isatra.2025.12.006_bib0030","doi-asserted-by":"crossref","first-page":"499","DOI":"10.1016\/j.isatra.2012.03.005","article-title":"Improved correlation analysis and visualization of industrial alarm data","volume":"51","author":"Yang","year":"2012","journal-title":"ISA Trans"},{"key":"10.1016\/j.isatra.2025.12.006_bib0035","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1016\/j.isatra.2018.10.011","article-title":"Active features extracted by deep belief network for process monitoring","volume":"84","author":"Yu","year":"2019","journal-title":"ISA Trans"},{"issue":"5","key":"10.1016\/j.isatra.2025.12.006_bib0040","doi-asserted-by":"crossref","first-page":"4404","DOI":"10.1109\/TIE.2020.2984443","article-title":"Deep learning with spatiotemporal attention-based LSTM for industrial soft sensor model development","volume":"68","author":"Yuan","year":"2020","journal-title":"IEEE Trans Ind Electron"},{"issue":"3","key":"10.1016\/j.isatra.2025.12.006_bib0045","doi-asserted-by":"crossref","first-page":"1521","DOI":"10.1109\/TII.2021.3086798","article-title":"Novel transformer based on gated convolutional neural network for dynamic soft sensor modeling of industrial processes","volume":"18","author":"Geng","year":"2021","journal-title":"IEEE Trans Ind Inform"},{"key":"10.1016\/j.isatra.2025.12.006_bib0050","article-title":"Industrial data imputation based on multiscale spatiotemporal information embedding with asymmetrical transformer","author":"Li","year":"2025","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"10.1016\/j.isatra.2025.12.006_bib0055","article-title":"Frequency adaptive normalization for non-stationary time series forecasting","author":"Ye","year":"2024","journal-title":"Proc 38th Conf Neural Inf Process Syst NeurIPS 2024"},{"issue":"10","key":"10.1016\/j.isatra.2025.12.006_bib0060","doi-asserted-by":"crossref","first-page":"1053","DOI":"10.1080\/0740817X.2014.999180","article-title":"Time series forecasting for nonlinear and non-stationary processes: a review and comparative study","volume":"47","author":"Cheng","year":"2015","journal-title":"IIE Trans"},{"key":"10.1016\/j.isatra.2025.12.006_bib0065","doi-asserted-by":"crossref","DOI":"10.1016\/j.ymssp.2024.112266","article-title":"Physics-driven cross domain digital twin framework for bearing fault diagnosis in non-stationary conditions","volume":"228","author":"Peng","year":"2025","journal-title":"Mech Syst Signal Process"},{"key":"10.1016\/j.isatra.2025.12.006_bib0070","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1016\/j.jprocont.2022.06.011","article-title":"Perspectives on nonstationary process monitoring in the era of industrial artificial intelligence","volume":"116","author":"Zhao","year":"2022","journal-title":"J Process Control"},{"issue":"10","key":"10.1016\/j.isatra.2025.12.006_bib0075","doi-asserted-by":"crossref","first-page":"7296","DOI":"10.1109\/TII.2022.3151072","article-title":"Recursive hybrid variable monitoring for fault detection in nonstationary industrial processes","volume":"18","author":"Wang","year":"2022","journal-title":"IEEE Trans Ind Inform"},{"key":"10.1016\/j.isatra.2025.12.006_bib0080","first-page":"1","article-title":"Adaptive recursive variational mode decomposition for multiple engine faults detection","volume":"71","author":"Tang","year":"2022","journal-title":"IEEE Trans Instrum Meas"},{"issue":"6","key":"10.1016\/j.isatra.2025.12.006_bib0085","doi-asserted-by":"crossref","first-page":"5259","DOI":"10.1109\/TIE.2020.2989708","article-title":"A just-in-time-learning-aided canonical correlation analysis method for multimode process monitoring and fault detection","volume":"68","author":"Chen","year":"2020","journal-title":"IEEE Trans Ind Electron"},{"issue":"7","key":"10.1016\/j.isatra.2025.12.006_bib0090","doi-asserted-by":"crossref","first-page":"3533","DOI":"10.1021\/ie801611s","article-title":"Cointegration testing method for monitoring nonstationary processes","volume":"48","author":"Chen","year":"2009","journal-title":"Ind Eng Chem Res"},{"key":"10.1016\/j.isatra.2025.12.006_bib0095","article-title":"A novel dynamic monitoring method based on cointegration analysis for nonstationary processes","author":"Chen","year":"2025","journal-title":"IEEE Trans Instrum Meas"},{"issue":"21","key":"10.1016\/j.isatra.2025.12.006_bib0100","doi-asserted-by":"crossref","DOI":"10.1103\/PhysRevLett.103.214101","article-title":"Finding stationary subspaces in multivariate time series","volume":"103","author":"Von B\u00fcnau","year":"2009","journal-title":"Phys Rev Lett"},{"key":"10.1016\/j.isatra.2025.12.006_bib0105","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1016\/j.neunet.2012.04.001","article-title":"Separation of stationary and non-stationary sources with a generalized eigenvalue problem","volume":"33","author":"Hara","year":"2012","journal-title":"Neural Netw"},{"issue":"4","key":"10.1016\/j.isatra.2025.12.006_bib0110","doi-asserted-by":"crossref","first-page":"631","DOI":"10.1109\/TNNLS.2012.2185811","article-title":"Feature extraction for change-point detection using stationary subspace analysis","volume":"23","author":"Blythe","year":"2012","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"47","key":"10.1016\/j.isatra.2025.12.006_bib0115","doi-asserted-by":"crossref","first-page":"20787","DOI":"10.1021\/acs.iecr.0c04059","article-title":"Dynamic stationary subspace analysis for monitoring nonstationary dynamic processes","volume":"59","author":"Wu","year":"2020","journal-title":"Ind Eng Chem Res"},{"issue":"5","key":"10.1016\/j.isatra.2025.12.006_bib0120","doi-asserted-by":"crossref","first-page":"3114","DOI":"10.1109\/TII.2021.3103005","article-title":"Probabilistic stationary subspace analysis for monitoring nonstationary industrial processes with uncertainty","volume":"18","author":"Wu","year":"2021","journal-title":"IEEE Trans Ind Inform"},{"issue":"12","key":"10.1016\/j.isatra.2025.12.006_bib0125","doi-asserted-by":"crossref","first-page":"8345","DOI":"10.1109\/TII.2021.3053308","article-title":"Exponential stationary subspace analysis for stationary feature analytics and adaptive nonstationary process monitoring","volume":"17","author":"Chen","year":"2021","journal-title":"IEEE Trans Ind Inform"},{"key":"10.1016\/j.isatra.2025.12.006_bib0130","article-title":"Disentangled feature representation based on multiscale content learning in industrial heterogeneous nonstationary fault detection","author":"Yu","year":"2025","journal-title":"IEEE Trans Instrum Meas"},{"key":"10.1016\/j.isatra.2025.12.006_bib0135","article-title":"Multipeeling of homogeneous stationarity and heterogeneous nonstationarity with differentiated learning for process monitoring","author":"Yu","year":"2025","journal-title":"IEEE Trans Cybern"},{"issue":"5","key":"10.1016\/j.isatra.2025.12.006_bib0140","doi-asserted-by":"crossref","first-page":"4325","DOI":"10.1109\/TIE.2017.2677351","article-title":"Process monitoring for multimodal processes with mode-reachability constraints","volume":"64","author":"Afzal","year":"2017","journal-title":"IEEE Trans Ind Electron"},{"key":"10.1016\/j.isatra.2025.12.006_bib0145","series-title":"Proc. IEEE\/CVF conf. Comput. Vis. Pattern recogn","first-page":"6631","article-title":"Decoupled multimodal distilling for emotion recognition","author":"Li","year":"2023"},{"key":"10.1016\/j.isatra.2025.12.006_bib0150","series-title":"Proc. IEEE\/CVF INT. Conf. Comput. Vis","first-page":"2473","article-title":"Multimodal contrastive learning and tabular attention for automated alzheimer\u2019s disease prediction","author":"Huang","year":"2023"},{"key":"10.1016\/j.isatra.2025.12.006_bib0155","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2024.111846","article-title":"A novel self-supervised representation learning framework based on time-frequency alignment and interaction for mechanical fault diagnosis","volume":"295","author":"Fu","year":"2024","journal-title":"Knowl-Based Syst"},{"key":"10.1016\/j.isatra.2025.12.006_bib0160","article-title":"Operating condition recognition of industrial flotation processes using visual and acoustic bimodal autoencoder with manifold learning","author":"Liu","year":"2024","journal-title":"IEEE Trans Ind Inform"},{"key":"10.1016\/j.isatra.2025.12.006_bib0165","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2024.123801","article-title":"A synchronization-induced cross-modal contrastive learning strategy for fault diagnosis of electromechanical systems under semi-supervised learning with current signal","volume":"249","author":"Luo","year":"2024","journal-title":"Expert Syst Appl"},{"issue":"1","key":"10.1016\/j.isatra.2025.12.006_bib0170","doi-asserted-by":"crossref","first-page":"2","DOI":"10.3390\/technologies9010002","article-title":"A survey on contrastive self-supervised learning","volume":"9","author":"Jaiswal","year":"2020","journal-title":"Technologies"},{"issue":"1","key":"10.1016\/j.isatra.2025.12.006_bib0175","first-page":"857","article-title":"Self-supervised learning: generative or contrastive","volume":"35","author":"Liu","year":"2021","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"10.1016\/j.isatra.2025.12.006_bib0180","author":"Oord"},{"key":"10.1016\/j.isatra.2025.12.006_bib0185","series-title":"ICML","first-page":"1597","article-title":"A simple framework for contrastive learning of visual representations","author":"Chen","year":"2020"},{"key":"10.1016\/j.isatra.2025.12.006_bib0190","series-title":"International Conference on machine Learning","first-page":"8748","article-title":"Learning transferable visual models from natural language supervision","author":"Radford","year":"2021"},{"key":"10.1016\/j.isatra.2025.12.006_bib0195","author":"Hinton"},{"key":"10.1016\/j.isatra.2025.12.006_bib0200","series-title":"ECCV","first-page":"166","article-title":"Graph distillation for action detection with privileged modalities","author":"Luo","year":"2018"},{"key":"10.1016\/j.isatra.2025.12.006_bib0205","author":"Zhang"},{"key":"10.1016\/j.isatra.2025.12.006_bib0210","doi-asserted-by":"crossref","DOI":"10.1109\/TNNLS.2024.3389714","article-title":"Reconstructed graph neural network with knowledge distillation for lightweight anomaly detection","author":"Zhou","year":"2024","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"10.1016\/j.isatra.2025.12.006_bib0215","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2024.125469","article-title":"A multi-scale graph pyramid attention network with knowledge distillation towards edge computing robotic fault diagnosis","volume":"260","author":"Chen","year":"2025","journal-title":"Expert Syst Appl"},{"issue":"5","key":"10.1016\/j.isatra.2025.12.006_bib0220","doi-asserted-by":"crossref","first-page":"471","DOI":"10.1016\/S0959-1524(00)00022-6","article-title":"Recursive PCA for adaptive process monitoring","volume":"10","author":"Li","year":"2000","journal-title":"J Process Control"},{"issue":"11","key":"10.1016\/j.isatra.2025.12.006_bib0225","doi-asserted-by":"crossref","first-page":"8895","DOI":"10.1109\/TIE.2018.2811358","article-title":"Recursive slow feature analysis for adaptive monitoring of industrial processes","volume":"65","author":"Shang","year":"2018","journal-title":"IEEE Trans Ind Electron"},{"issue":"9","key":"10.1016\/j.isatra.2025.12.006_bib0230","doi-asserted-by":"crossref","first-page":"6015","DOI":"10.1109\/TNNLS.2021.3132376","article-title":"Interaction-aware graph neural networks for fault diagnosis of complex industrial processes","volume":"34","author":"Chen","year":"2021","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"10.1016\/j.isatra.2025.12.006_bib0235","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.jprocont.2022.12.007","article-title":"A local\u2013global transformer for distributed monitoring of multi-unit nonlinear processes","volume":"122","author":"Yi","year":"2023","journal-title":"J Process Control"},{"issue":"3\u20134","key":"10.1016\/j.isatra.2025.12.006_bib0240","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1002\/(SICI)1099-128X(199905\/08)13:3\/4<379::AID-CEM556>3.0.CO;2-N","article-title":"A comparison of principal component analysis, multiway principal component analysis, trilinear decomposition and parallel factor analysis for fault detection in a semiconductor etch process","volume":"13","author":"Wise","year":"1999","journal-title":"J Chemometrics"},{"key":"10.1016\/j.isatra.2025.12.006_bib0245","article-title":"Isolating sources of disentanglement in variational autoencoders","volume":"31","author":"Chen","year":"2018","journal-title":"Adv Neural Inf Process Syst"},{"issue":"3","key":"10.1016\/j.isatra.2025.12.006_bib0250","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1016\/0098-1354(93)80018-I","article-title":"A plant-wide industrial process control problem","volume":"17","author":"Downs","year":"1993","journal-title":"Comput Chem Eng"},{"issue":"5","key":"10.1016\/j.isatra.2025.12.006_bib0255","doi-asserted-by":"crossref","first-page":"383","DOI":"10.1016\/0098-1354(94)88019-0","article-title":"Base control for the Tennessee Eastman problem","volume":"18","author":"McAvoy","year":"1994","journal-title":"Comput Chem Eng"},{"key":"10.1016\/j.isatra.2025.12.006_bib0260","series-title":"2016 INT. Workshop CPS smart Water NETW (cyswater)","first-page":"31","article-title":"SWAT: a water treatment testbed for research and training on ICS security","author":"Mathur","year":"2016"}],"container-title":["ISA Transactions"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0019057825006986?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0019057825006986?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T20:36:05Z","timestamp":1781210165000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0019057825006986"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7]]},"references-count":52,"alternative-id":["S0019057825006986"],"URL":"https:\/\/doi.org\/10.1016\/j.isatra.2025.12.006","relation":{},"ISSN":["0019-0578"],"issn-type":[{"value":"0019-0578","type":"print"}],"subject":[],"published":{"date-parts":[[2026,7]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"A bimodal framework for nonstationary process monitoring via collaborative contrastive and adversarial unsupervised learning","name":"articletitle","label":"Article Title"},{"value":"ISA Transactions","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.isatra.2025.12.006","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2025 International Society of Automation. Published by Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}]}}