{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T07:51:44Z","timestamp":1781596304577,"version":"3.54.5"},"reference-count":50,"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":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"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","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":[[2026,9]]},"DOI":"10.1016\/j.engappai.2026.115259","type":"journal-article","created":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T03:06:03Z","timestamp":1780023963000},"page":"115259","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"P1","title":["Interpretable process monitoring and hierarchical root cause analysis based on attention-recalibrated multi-scale deep slow feature analysis"],"prefix":"10.1016","volume":"179","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-0502-3855","authenticated-orcid":false,"given":"Xi","family":"Tu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5622-8686","authenticated-orcid":false,"given":"Xuefeng","family":"Yan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"17","key":"10.1016\/j.engappai.2026.115259_bib1","doi-asserted-by":"crossref","first-page":"7849","DOI":"10.1021\/ie9018947","article-title":"Reconstruction-based contribution for process monitoring with kernel principal component analysis","volume":"49","author":"Alcala","year":"2010","journal-title":"Ind. Eng. Chem. Res."},{"issue":"9","key":"10.1016\/j.engappai.2026.115259_bib2","doi-asserted-by":"crossref","first-page":"238","DOI":"10.1007\/s10462-024-10873-5","article-title":"Blockchain, artificial intelligence, and healthcare: the tripod of future\u2014a narrative review","volume":"57","author":"Bathula","year":"2024","journal-title":"Artif. Intell. Rev."},{"issue":"4","key":"10.1016\/j.engappai.2026.115259_bib3","doi-asserted-by":"crossref","first-page":"721","DOI":"10.1007\/s40534-025-00393-5","article-title":"Drive-by damage detection and localization exploiting continuous wavelet transform and multiple sparse autoencoders","volume":"33","author":"Bernardini","year":"2025","journal-title":"Railway Eng. Sci."},{"key":"10.1016\/j.engappai.2026.115259_bib4","doi-asserted-by":"crossref","first-page":"581","DOI":"10.1016\/j.psep.2021.10.036","article-title":"A novel orthogonal self-attentive variational autoencoder method for interpretable chemical process fault detection and identification","volume":"156","author":"Bi","year":"2021","journal-title":"Process Saf. Environ. Prot."},{"issue":"4","key":"10.1016\/j.engappai.2026.115259_bib5","doi-asserted-by":"crossref","first-page":"3649","DOI":"10.1109\/TASE.2021.3129247","article-title":"Fault-prototypical adapted network for cross-domain industrial intelligent diagnosis","volume":"19","author":"Chai","year":"2022","journal-title":"IEEE Trans. Autom. Sci. Eng."},{"key":"10.1016\/j.engappai.2026.115259_bib6","first-page":"1","article-title":"Dynamic non-gaussian and nonlinear industrial process monitoring using deep analysis of hybrid characteristics","volume":"74","author":"Chen","year":"2025","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"10.1016\/j.engappai.2026.115259_bib7","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2024.108295","article-title":"Predictive process model monitoring using long short-term memory networks","volume":"133","author":"De Smedt","year":"2024","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.engappai.2026.115259_bib8","series-title":"Proc. China Automation Congress (CAC)","first-page":"3744","article-title":"A novel LSTM-1DCNN-based deep network for fault diagnosis in chemical process","author":"Ding","year":"2022"},{"issue":"3","key":"10.1016\/j.engappai.2026.115259_bib9","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.engappai.2026.115259_bib10","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2024.109624","article-title":"A generative-adversarial-network-based temporal raw trace data augmentation framework for fault detection in semiconductor manufacturing","volume":"139","author":"Fan","year":"2025","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.engappai.2026.115259_bib11","doi-asserted-by":"crossref","first-page":"383","DOI":"10.1016\/j.isatra.2021.03.013","article-title":"Semi-supervised meta-learning networks with squeeze-and-excitation attention for few-shot fault diagnosis","volume":"120","author":"Feng","year":"2022","journal-title":"ISA Trans."},{"key":"10.1016\/j.engappai.2026.115259_bib12","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.chemolab.2013.02.001","article-title":"Performance-driven ensemble learning ICA model for improved Non-gaussian process monitoring","volume":"123","author":"Ge","year":"2013","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"10.1016\/j.engappai.2026.115259_bib13","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2025.112701","article-title":"Furnace temperature prediction model in municipal solid waste incineration process based on slow feature analysis and sparse stochastic configuration network","volume":"162","author":"Guo","year":"2025","journal-title":"Eng. Appl. Artif. Intell."},{"issue":"1","key":"10.1016\/j.engappai.2026.115259_bib14","first-page":"1729","article-title":"DNNBoT: deep neural network-based botnet detection and classification","volume":"71","author":"Haq","year":"2021","journal-title":"Comput. Mater. Contin."},{"key":"10.1016\/j.engappai.2026.115259_bib15","first-page":"1","article-title":"Enhanced dynamic dual-latent variable model for multirate process monitoring and its industrial application","volume":"72","author":"He","year":"2023","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"10.1016\/j.engappai.2026.115259_bib16","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.chemolab.2017.07.013","article-title":"Slow feature analysis based on online feature reordering and feature selection for dynamic chemical process monitoring","volume":"169","author":"Huang","year":"2017","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"10.1016\/j.engappai.2026.115259_bib17","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.conengprac.2019.05.021","article-title":"Nonlinear process monitoring using kernel dictionary learning with application to aluminum electrolysis process","volume":"89","author":"Huang","year":"2019","journal-title":"Control Eng. Pract."},{"key":"10.1016\/j.engappai.2026.115259_bib18","doi-asserted-by":"crossref","DOI":"10.1016\/j.cep.2025.110605","article-title":"A dynamic PCA and transformers coupled reduced-order model for transient solid-liquid flows in stirred tanks","volume":"219","author":"Jiang","year":"2026","journal-title":"Chem. Eng. Process. Process Intensif."},{"key":"10.1016\/j.engappai.2026.115259_bib19","first-page":"1","article-title":"A general quality-related nonlinear process monitoring approach based on input\u2013output kernel PLS","volume":"72","author":"Kong","year":"2023","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"10.1016\/j.engappai.2026.115259_bib20","first-page":"1","article-title":"Toward interpretable process monitoring: slow feature analysis-aided autoencoder for spatiotemporal process feature learning","volume":"71","author":"Li","year":"2022","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"10.1016\/j.engappai.2026.115259_bib21","doi-asserted-by":"crossref","DOI":"10.1016\/j.aei.2026.104408","article-title":"Graph autoencoder with causal relationship inference for fault detection and root cause identification in complex industrial process","volume":"72","author":"Li","year":"2026","journal-title":"Adv. Eng. Inform."},{"key":"10.1016\/j.engappai.2026.115259_bib22","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2024.123271","article-title":"Automatic segmentation of dynamic and static models based on high order slow feature analysis and principal component analysis for multiphase batch monitoring","volume":"248","author":"Liu","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.engappai.2026.115259_bib23","doi-asserted-by":"crossref","DOI":"10.1016\/j.aei.2025.103895","article-title":"Advanced batch monitoring: auto-segmentation of complex processes using slow feature partial least squares","volume":"69","author":"Liu","year":"2026","journal-title":"Adv. Eng. Inform."},{"issue":"3","key":"10.1016\/j.engappai.2026.115259_bib24","doi-asserted-by":"crossref","first-page":"3062","DOI":"10.1109\/TNNLS.2023.3328399","article-title":"A spatial\u2013temporal variational graph attention autoencoder using interactive information for fault detection in complex industrial processes","volume":"35","author":"Lv","year":"2024","journal-title":"IEEE Transact. Neural Networks Learn. Syst."},{"key":"10.1016\/j.engappai.2026.115259_bib25","series-title":"ICCCE 2018","first-page":"141","article-title":"Text message classification using supervised machine learning algorithms","author":"Merugu","year":"2019"},{"issue":"4","key":"10.1016\/j.engappai.2026.115259_bib26","doi-asserted-by":"crossref","first-page":"15541","DOI":"10.48084\/etasr.7548","article-title":"Identification and improvement of image similarity using autoencoder","volume":"14","author":"Merugu","year":"2024","journal-title":"Eng. Technol. Appl. Sci. Res."},{"key":"10.1016\/j.engappai.2026.115259_bib27","series-title":"Smoothgrad: Removing Noise by Adding Noise","author":"Smilkov","year":"2017"},{"key":"10.1016\/j.engappai.2026.115259_bib28","series-title":"Proc. International Joint Conference on Neural Networks (IJCNN)","first-page":"2346","article-title":"An efficient incremental kernel principal component analysis for online feature selection","author":"Takeuchi","year":"2007"},{"key":"10.1016\/j.engappai.2026.115259_bib29","doi-asserted-by":"crossref","DOI":"10.1016\/j.measurement.2025.118249","article-title":"Manifold constrained dynamic latent variable model for industrial process monitoring","volume":"256","author":"Tong","year":"2025","journal-title":"Measurement"},{"key":"10.1016\/j.engappai.2026.115259_bib30","doi-asserted-by":"crossref","DOI":"10.1016\/j.psep.2026.108443","article-title":"A sample distribution enhanced variational autoencoder-generative adversarial network for process monitoring of nonlinear uncertain systems","volume":"208","author":"Tu","year":"2026","journal-title":"Process Saf. Environ. Prot."},{"key":"10.1016\/j.engappai.2026.115259_bib31","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2019.105527","article-title":"A probabilistic principal component analysis-based approach in process monitoring and fault diagnosis with application in wastewater treatment plant","volume":"82","author":"Wang","year":"2019","journal-title":"Appl. Soft Comput."},{"key":"10.1016\/j.engappai.2026.115259_bib32","series-title":"Proc. 10th IEEE Data Driven Control and Learning Systems Conference (DDCLS)","first-page":"495","article-title":"Monitoring of wastewater treatment process based on slow feature analysis variational autoencoder","author":"Wang","year":"2021"},{"issue":"5","key":"10.1016\/j.engappai.2026.115259_bib33","doi-asserted-by":"crossref","first-page":"6492","DOI":"10.1109\/TII.2022.3204555","article-title":"KPCA-CCA-based quality-related fault detection and diagnosis method for nonlinear process monitoring","volume":"19","author":"Wang","year":"2023","journal-title":"IEEE Trans. Ind. Inf."},{"key":"10.1016\/j.engappai.2026.115259_bib34","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2023.107839","article-title":"Anomaly detection using large-scale multimode industrial data: an integration method of nonstationary kernel and autoencoder","volume":"131","author":"Wang","year":"2024","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.engappai.2026.115259_bib35","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2023.107237","article-title":"Unsupervised heat balance indicator construction based on variational autoencoder and its application to aluminum electrolysis process monitoring","volume":"127","author":"Wang","year":"2024","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.engappai.2026.115259_bib36","doi-asserted-by":"crossref","DOI":"10.1016\/j.aei.2025.104026","article-title":"Rearranged soft-introspective orthogonality regularized convolutional autoencoder for fault detection of multivariate processes","volume":"69","author":"Wang","year":"2026","journal-title":"Adv. Eng. Inform."},{"key":"10.1016\/j.engappai.2026.115259_bib37","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2025.110792","article-title":"Self-learning stationary subspace analysis for fault detection of industrial processes with varying operation conditions","volume":"153","author":"Wu","year":"2025","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.engappai.2026.115259_bib38","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1016\/j.jprocont.2020.12.006","article-title":"Decentralized dynamic process monitoring based on manifold regularized slow feature analysis","volume":"98","author":"Xu","year":"2021","journal-title":"J. Process Control"},{"key":"10.1016\/j.engappai.2026.115259_bib39","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1016\/j.neucom.2018.12.024","article-title":"Multimode process monitoring based on robust dictionary learning with application to aluminium electrolysis process","volume":"332","author":"Yang","year":"2019","journal-title":"Neurocomputing"},{"key":"10.1016\/j.engappai.2026.115259_bib40","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2025.112236","article-title":"Adaptive chemical industrial processes fault detection model based on sparse filtering-based improved mixed-gaussian probabilistic principal component analysis considering low-probability events","volume":"162","author":"Yang","year":"2025","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.engappai.2026.115259_bib41","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2025.112321","article-title":"Zero-shot learning augmented slow feature analysis for semantic-aware industrial process fault detection","volume":"162","author":"Yang","year":"2025","journal-title":"Eng. Appl. Artif. Intell."},{"issue":"6","key":"10.1016\/j.engappai.2026.115259_bib42","doi-asserted-by":"crossref","first-page":"1789","DOI":"10.1007\/s13042-021-01274-z","article-title":"Fault detection of railway freight cars mechanical components based on multi-feature fusion convolutional neural network","volume":"12","author":"Ye","year":"2021","journal-title":"Int. J. Mach. Learn. Cybern."},{"key":"10.1016\/j.engappai.2026.115259_bib43","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/j.psep.2024.05.070","article-title":"A multi-scale low rank convolutional autoencoder for process monitoring of nonlinear uncertain systems","volume":"188","author":"Yin","year":"2024","journal-title":"Process Saf. Environ. Prot."},{"key":"10.1016\/j.engappai.2026.115259_bib44","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2024.108872","article-title":"Residual squeeze-and-excitation convolutional auto-encoder for fault detection and diagnosis in complex industrial processes","volume":"136","author":"Yu","year":"2024","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.engappai.2026.115259_bib45","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.compeleceng.2014.11.003","article-title":"Process fault detection based on dynamic kernel slow feature analysis","volume":"41","author":"Zhang","year":"2015","journal-title":"Comput. Electr. Eng."},{"key":"10.1016\/j.engappai.2026.115259_bib46","doi-asserted-by":"crossref","first-page":"2696","DOI":"10.1109\/ACCESS.2017.2672780","article-title":"Batch process monitoring based on Multiway global preserving kernel slow feature analysis","volume":"5","author":"Zhang","year":"2017","journal-title":"IEEE Access"},{"key":"10.1016\/j.engappai.2026.115259_bib47","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1016\/j.isatra.2018.05.005","article-title":"Batch process fault detection and identification based on discriminant global preserving kernel slow feature analysis","volume":"79","author":"Zhang","year":"2018","journal-title":"ISA Trans."},{"key":"10.1016\/j.engappai.2026.115259_bib48","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2023.106424","article-title":"Process monitoring using recurrent kalman variational auto-encoder for general complex dynamic processes","volume":"123","author":"Zhang","year":"2023","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.engappai.2026.115259_bib49","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1016\/j.psep.2023.05.025","article-title":"Gated recurrent unit-enhanced deep convolutional neural network for real-time industrial process fault diagnosis","volume":"175","author":"Zhang","year":"2023","journal-title":"Process Saf. Environ. Prot."},{"issue":"13","key":"10.1016\/j.engappai.2026.115259_bib50","doi-asserted-by":"crossref","first-page":"25108","DOI":"10.1109\/JSEN.2025.3571727","article-title":"Gas volume fraction measurement for gas-liquid two-phase flow based on dual CNN-transformer mixture neural network","volume":"25","author":"Zhang","year":"2025","journal-title":"IEEE Sens. J."}],"container-title":["Engineering Applications of Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0952197626015435?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0952197626015435?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T06:53:23Z","timestamp":1781592803000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0952197626015435"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9]]},"references-count":50,"alternative-id":["S0952197626015435"],"URL":"https:\/\/doi.org\/10.1016\/j.engappai.2026.115259","relation":{},"ISSN":["0952-1976"],"issn-type":[{"value":"0952-1976","type":"print"}],"subject":[],"published":{"date-parts":[[2026,9]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Interpretable process monitoring and hierarchical root cause analysis based on attention-recalibrated multi-scale deep slow feature analysis","name":"articletitle","label":"Article Title"},{"value":"Engineering Applications of Artificial Intelligence","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.engappai.2026.115259","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":"115259"}}