{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,11]],"date-time":"2026-08-11T22:27:36Z","timestamp":1786487256722,"version":"build-2736575974"},"reference-count":24,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,5,16]],"date-time":"2026-05-16T00:00:00Z","timestamp":1778889600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Computers &amp; Chemical Engineering"],"published-print":{"date-parts":[[2026,9]]},"DOI":"10.1016\/j.compchemeng.2026.109710","type":"journal-article","created":{"date-parts":[[2026,5,16]],"date-time":"2026-05-16T22:15:32Z","timestamp":1778969732000},"page":"109710","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Self-Gated hierarchical transformer for robust fault detection in chemical processes"],"prefix":"10.1016","volume":"212","author":[{"given":"Zeid","family":"Al-Yafei","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2715-9084","authenticated-orcid":false,"given":"Syed","family":"Imtiaz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Salim","family":"Ahmed","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Faisal","family":"Khan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.compchemeng.2026.109710_bib0001","series-title":"arXiv preprint arXiv","article-title":"Intelligent condition monitoring of industrial plants: an overview of methodologies and uncertainty management strategies","author":"Ahang","year":"2024"},{"key":"10.1016\/j.compchemeng.2026.109710_bib0002","article-title":"A novel dynamic machine learning-based explainable fusion monitoring: application to industrial and chemical processes","volume":"6","author":"Ali","year":"2025","journal-title":"Mach. Learn.: Sci. Technol."},{"key":"10.1016\/j.compchemeng.2026.109710_bib0003","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1016\/j.chemolab.2015.05.013","article-title":"Designing supervised local neural network classifiers based on EM clustering for fault diagnosis of Tennessee Eastman process","volume":"146","author":"Ayubi Rad","year":"2015","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"10.1016\/j.compchemeng.2026.109710_bib0005","doi-asserted-by":"crossref","DOI":"10.1016\/j.jics.2025.102157","article-title":"Advancing fault diagnosis for ethyl benzene production plant: a machine learning and explainable AI approach","volume":"102","author":"Chowdhury","year":"2025","journal-title":"J. Indian Chem. Soc."},{"key":"10.1016\/j.compchemeng.2026.109710_bib0004","unstructured":"Chowdhury, S., Ghosh, A., Acharya, S., Saha, S., Pal, P.K., Roy, S., Lahiri, S.K., Transforming chemical process engineering: the role of AI and machine learning in revolutionizing process systems. Canad. J. Chemic. Eng. n\/a."},{"key":"10.1016\/j.compchemeng.2026.109710_bib0006","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."},{"key":"10.1016\/j.compchemeng.2026.109710_bib0007","series-title":"Deep Recurrent Neural Networks For Fault Detection and Classification","author":"Gonzalez","year":"2018"},{"key":"10.1016\/j.compchemeng.2026.109710_bib0008","doi-asserted-by":"crossref","first-page":"2264","DOI":"10.1109\/TII.2012.2231870","article-title":"Cold start approach for data-driven fault detection","volume":"9","author":"Grbovic","year":"2013","journal-title":"IEEE Trans. Ind. Inform."},{"key":"10.1016\/j.compchemeng.2026.109710_bib0009","doi-asserted-by":"crossref","first-page":"463","DOI":"10.1016\/j.psep.2022.07.019","article-title":"XFDDC: eXplainable fault detection diagnosis and correction framework for chemical process systems","volume":"165","author":"Harinarayan","year":"2022","journal-title":"Proc. Saf. Environ. Prot."},{"key":"10.1016\/j.compchemeng.2026.109710_bib0010","series-title":"2014 International Conference on Mechatronics and Control (ICMC)","first-page":"2194","article-title":"Fault classification on Tennessee Eastman process: PCA and SVM","author":"Jing","year":"2014"},{"key":"10.1016\/j.compchemeng.2026.109710_bib0011","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1016\/j.jprocont.2021.04.003","article-title":"Industrial process fault detection based on deep highly-sensitive feature capture","volume":"102","author":"Liu","year":"2021","journal-title":"J. Proc. Control."},{"key":"10.1016\/j.compchemeng.2026.109710_bib0012","doi-asserted-by":"crossref","first-page":"513","DOI":"10.1515\/revce-2017-0069","article-title":"A review of data-driven fault detection and diagnosis methods: applications in chemical process systems","volume":"36","author":"Nor","year":"2020","journal-title":"Rev. Chem. Eng."},{"key":"10.1016\/j.compchemeng.2026.109710_bib0013","series-title":"arXiv preprint arXiv","article-title":"Combining SHAP and causal analysis for interpretable fault detection in industrial processes","author":"Santos","year":"2025"},{"key":"10.1016\/j.compchemeng.2026.109710_bib0014","doi-asserted-by":"crossref","unstructured":"Schoch, F., Graf, P., Schmieg, T., Wittenberg, C., Lanquillon, C., Stache, N., 2024. Deep anomaly detection with extended transformer-based model on Tennessee Eastman process dataset.","DOI":"10.1109\/CSIT65290.2024.10982582"},{"key":"10.1016\/j.compchemeng.2026.109710_bib0015","first-page":"109","article-title":"Fault detection using CUSUM based techniques with application to the Tennessee Eastman process","volume":"43","author":"Shams","year":"2010","journal-title":"IFAC Proc"},{"key":"10.1016\/j.compchemeng.2026.109710_bib0016","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1002\/cben.202000027","article-title":"A review on data-driven learning approaches for fault detection and diagnosis in chemical processes","volume":"8","author":"Taqvi","year":"2021","journal-title":"ChemBioEng Rev."},{"key":"10.1016\/j.compchemeng.2026.109710_bib0017","series-title":"Fault Diagnosis of Chemical Process Plant Using Artificial Intelligence, Artificial Intelligence For Chemical Sciences","first-page":"337","author":"Tekawade","year":"2025"},{"key":"10.1016\/j.compchemeng.2026.109710_bib0018","article-title":"Attention is all you need","volume":"30","author":"Vaswani","year":"2017","journal-title":"Adv. Neural Inf. Proc. Syst."},{"key":"10.1016\/j.compchemeng.2026.109710_bib0019","doi-asserted-by":"crossref","first-page":"480","DOI":"10.1016\/j.psep.2022.09.039","article-title":"A novel deep learning model based on target transformer for fault diagnosis of chemical process","volume":"167","author":"Wei","year":"2022","journal-title":"Proc. Saf. Environ. Prot."},{"key":"10.1016\/j.compchemeng.2026.109710_bib0020","series-title":"2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA)","first-page":"745","article-title":"A hierarchical deep neural network for fault diagnosis on Tennessee-Eastman process","author":"Xie","year":"2015"},{"key":"10.1016\/j.compchemeng.2026.109710_bib0021","article-title":"Deep learning in visual computing and signal processing","volume":"2017","author":"Xie","year":"2017","journal-title":"Appl. Comput. Intell. Soft Comput."},{"key":"10.1016\/j.compchemeng.2026.109710_bib0022","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.jprocont.2020.06.001","article-title":"Manifold regularized stacked autoencoders-based feature learning for fault detection in industrial processes","volume":"92","author":"Yu","year":"2020","journal-title":"J. Proc. Control."},{"key":"10.1016\/j.compchemeng.2026.109710_bib0023","first-page":"1","article-title":"Generalized transformer in fault diagnosis of Tennessee Eastman process","volume":"34","author":"Zhang","year":"2022","journal-title":"Neural Comput. Appl."},{"key":"10.1016\/j.compchemeng.2026.109710_bib0024","doi-asserted-by":"crossref","first-page":"660","DOI":"10.1016\/j.psep.2022.12.055","article-title":"Exploring global attention mechanism on fault detection and diagnosis for complex engineering processes","volume":"170","author":"Zhou","year":"2023","journal-title":"Proc. Saf. Environ. Prot."}],"container-title":["Computers &amp; Chemical Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0098135426001638?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0098135426001638?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,8,11]],"date-time":"2026-08-11T21:30:05Z","timestamp":1786483805000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0098135426001638"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9]]},"references-count":24,"alternative-id":["S0098135426001638"],"URL":"https:\/\/doi.org\/10.1016\/j.compchemeng.2026.109710","relation":{},"ISSN":["0098-1354"],"issn-type":[{"value":"0098-1354","type":"print"}],"subject":[],"published":{"date-parts":[[2026,9]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Self-Gated hierarchical transformer for robust fault detection in chemical processes","name":"articletitle","label":"Article Title"},{"value":"Computers & Chemical Engineering","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.compchemeng.2026.109710","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 The Authors. Published by Elsevier Ltd.","name":"copyright","label":"Copyright"}],"article-number":"109710"}}