{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T06:51:53Z","timestamp":1782888713758,"version":"3.54.5"},"reference-count":27,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2023,8,1]],"date-time":"2023-08-01T00:00:00Z","timestamp":1690848000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2023,8,1]],"date-time":"2023-08-01T00:00:00Z","timestamp":1690848000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2023,8,1]],"date-time":"2023-08-01T00:00:00Z","timestamp":1690848000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2023,8,1]],"date-time":"2023-08-01T00:00:00Z","timestamp":1690848000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2023,8,1]],"date-time":"2023-08-01T00:00:00Z","timestamp":1690848000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2023,8,1]],"date-time":"2023-08-01T00:00:00Z","timestamp":1690848000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,8,1]],"date-time":"2023-08-01T00:00:00Z","timestamp":1690848000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Engineering Applications of Artificial Intelligence"],"published-print":{"date-parts":[[2023,8]]},"DOI":"10.1016\/j.engappai.2023.106208","type":"journal-article","created":{"date-parts":[[2023,3,30]],"date-time":"2023-03-30T13:45:51Z","timestamp":1680183951000},"page":"106208","update-policy":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":17,"special_numbering":"PA","title":["Operating performance assessment method for industrial process with slowness principle-based LSTM network"],"prefix":"10.1016","volume":"123","author":[{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0000-0002-0891-6748","authenticated-orcid":false,"given":"Fei","family":"Chu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuangshuang","family":"Liao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lili","family":"Hao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pei","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yan","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"FuLi","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"7","key":"10.1016\/j.engappai.2023.106208_b1","doi-asserted-by":"crossref","first-page":"1593","DOI":"10.1016\/j.automatica.2009.02.027","article-title":"Reconstruction-based contribution for process monitoring","volume":"45","author":"Alcala","year":"2009","journal-title":"Automatica"},{"key":"10.1016\/j.engappai.2023.106208_b2","doi-asserted-by":"crossref","unstructured":"Bu,\u00a0K.Q., Wang,\u00a0F.L., Liu,\u00a0Y., 2020. Operating Performance Assessment Based on Stacked Sparse Denoising Auto-encoder for Gold Hydrometallurgy Processes. In: Proceedings of the 32nd 2020 Chinese Control and Decision Conference. CCDC 2020, pp. 2904\u20132909.","DOI":"10.1109\/CCDC49329.2020.9164842"},{"key":"10.1016\/j.engappai.2023.106208_b3","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1016\/j.conengprac.2015.10.006","article-title":"Canonical correlation analysis-based fault detection methods with application to alumina evaporation process","volume":"46","author":"Chen","year":"2016","journal-title":"Control Eng. Pract."},{"issue":"12","key":"10.1016\/j.engappai.2023.106208_b4","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."},{"issue":"9","key":"10.1016\/j.engappai.2023.106208_b5","doi-asserted-by":"crossref","first-page":"8953","DOI":"10.1109\/TIE.2020.3014574","article-title":"Siamese neural network-based supervised slow feature extraction for soft sensor application","volume":"68","author":"Chiplunkar","year":"2021","journal-title":"IEEE Trans. Ind. Electron."},{"issue":"4","key":"10.1016\/j.engappai.2023.106208_b6","first-page":"849","article-title":"Operating performance assessment method and application for complex industrial process based on ISDAE model","volume":"47","author":"Chu","year":"2021","journal-title":"Acta Automat. Sinica"},{"issue":"8","key":"10.1016\/j.engappai.2023.106208_b7","doi-asserted-by":"crossref","first-page":"3306","DOI":"10.1109\/TNNLS.2020.3015929","article-title":"Dual attention-based encoder-decoder: A customized sequence-to-sequence learning for soft sensor development","volume":"32","author":"Feng","year":"2021","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"issue":"7","key":"10.1016\/j.engappai.2023.106208_b8","first-page":"3172","article-title":"Intermittent process fault monitoring based on recurrent autoencoder","volume":"71","author":"Gao","year":"2020","journal-title":"CIESC J."},{"issue":"10","key":"10.1016\/j.engappai.2023.106208_b9","doi-asserted-by":"crossref","first-page":"2222","DOI":"10.1109\/TNNLS.2016.2582924","article-title":"LSTM: A search space odyssey","volume":"28","author":"Greff","year":"2016","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"issue":"8","key":"10.1016\/j.engappai.2023.106208_b10","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"issue":"11","key":"10.1016\/j.engappai.2023.106208_b11","doi-asserted-by":"crossref","first-page":"12868","DOI":"10.1109\/JSEN.2020.3033153","article-title":"A review on soft sensors for monitoring, control, and optimization of industrial processes","volume":"21","author":"Jiang","year":"2021","journal-title":"IEEE Sens. J."},{"issue":"12","key":"10.1016\/j.engappai.2023.106208_b12","first-page":"1","article-title":"A two-layer fuzzy synthetic strategy for operational performance assessment of an industrial hydrocracking process","volume":"93","author":"Li","year":"2019","journal-title":"Control Eng. Pract."},{"issue":"3","key":"10.1016\/j.engappai.2023.106208_b13","doi-asserted-by":"crossref","first-page":"2683","DOI":"10.1109\/TIE.2017.2745452","article-title":"Linearity evaluation and variable subset partition based hierarchical process modeling and monitoring","volume":"65","author":"Li","year":"2018","journal-title":"IEEE Trans. Ind. Electron."},{"issue":"10","key":"10.1016\/j.engappai.2023.106208_b14","doi-asserted-by":"crossref","first-page":"1548","DOI":"10.1016\/j.jprocont.2014.08.001","article-title":"Online process operating performance assessment and nonoptimal cause identification for industrial processes","volume":"24","author":"Liu","year":"2014","journal-title":"J. Process Control"},{"issue":"1","key":"10.1016\/j.engappai.2023.106208_b15","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1109\/TSMC.2020.3043147","article-title":"Stacked broad learning system: From incremental flatted structure to deep model","volume":"51","author":"Liu","year":"2021","journal-title":"IEEE Trans. Syst. Man Cybern. Syst."},{"key":"10.1016\/j.engappai.2023.106208_b16","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.jprocont.2015.12.008","article-title":"Operating optimality assessment based on optimality related variations and nonoptimal cause identification for industrial processes","volume":"39","author":"Liu","year":"2016","journal-title":"J. Process Control"},{"issue":"10","key":"10.1016\/j.engappai.2023.106208_b17","doi-asserted-by":"crossref","first-page":"791","DOI":"10.1016\/j.mineng.2010.05.020","article-title":"The development of dynamic models for a dense medium separation circuit in coal beneficiation","volume":"23","author":"Meyer","year":"2010","journal-title":"Miner. Eng."},{"key":"10.1016\/j.engappai.2023.106208_b18","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1016\/j.jprocont.2015.12.004","article-title":"Slow feature analysis for monitoring and diagnosis of control performance","volume":"39","author":"Shang","year":"2016","journal-title":"J. Process Control"},{"issue":"11","key":"10.1016\/j.engappai.2023.106208_b19","doi-asserted-by":"crossref","first-page":"3666","DOI":"10.1002\/aic.14888","article-title":"Concurrent monitoring of operating condition deviations and process dynamics anomalies with slow feature analysis","volume":"61","author":"Shang","year":"2015","journal-title":"AIChE J."},{"key":"10.1016\/j.engappai.2023.106208_b20","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1016\/j.engappai.2017.07.004","article-title":"Sparse supervised principal component analysis (SSPCA) for dimension reduction and variable selection","volume":"65","author":"Sharifzadeh","year":"2017","journal-title":"Eng. Appl. Artif. Intell."},{"issue":"1","key":"10.1016\/j.engappai.2023.106208_b21","doi-asserted-by":"crossref","first-page":"657","DOI":"10.1109\/TIE.2014.2308133","article-title":"Data-based techniques focused on modern industry: An overview","volume":"62","author":"Yin","year":"2015","journal-title":"IEEE Trans. Ind. Electron."},{"issue":"5","key":"10.1016\/j.engappai.2023.106208_b22","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":"2021","journal-title":"IEEE Trans. Ind. Electron."},{"issue":"5","key":"10.1016\/j.engappai.2023.106208_b23","doi-asserted-by":"crossref","first-page":"3168","DOI":"10.1109\/TII.2019.2902129","article-title":"Nonlinear dynamic soft sensor modeling with supervised long short-term memory network","volume":"16","author":"Yuan","year":"2020","journal-title":"IEEE Trans. Ind. Inform."},{"issue":"6","key":"10.1016\/j.engappai.2023.106208_b24","doi-asserted-by":"crossref","first-page":"1377","DOI":"10.1002\/cjce.23665","article-title":"Deep learning for quality prediction of nonlinear dynamic processes with variable attention-based long short-term memory network","volume":"98","author":"Yuan","year":"2020","journal-title":"Canadian J. Chem. Eng."},{"key":"10.1016\/j.engappai.2023.106208_b25","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.jprocont.2022.04.013","article-title":"Machine learning-based data-driven robust optimization approach under uncertainty","volume":"115","author":"Zhang","year":"2022","journal-title":"J. Process Control"},{"issue":"6","key":"10.1016\/j.engappai.2023.106208_b26","doi-asserted-by":"crossref","first-page":"2211","DOI":"10.1109\/TCST.2016.2640946","article-title":"A dual-loop control system for dense medium coal washing processes with sampled and delayed measurements","volume":"25","author":"Zhang","year":"2017","journal-title":"IEEE Trans. Control Syst. Technol."},{"key":"10.1016\/j.engappai.2023.106208_b27","doi-asserted-by":"crossref","first-page":"395","DOI":"10.1016\/j.compchemeng.2017.02.041","article-title":"A deep belief network based fault diagnosis model for complex chemical processes","volume":"107","author":"Zhang","year":"2017","journal-title":"Comput. Chem. Eng."}],"container-title":["Engineering Applications of Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/api.elsevier.com\/content\/article\/PII:S0952197623003925?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/api.elsevier.com\/content\/article\/PII:S0952197623003925?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,10,16]],"date-time":"2025-10-16T07:35:05Z","timestamp":1760600105000},"score":1,"resource":{"primary":{"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/linkinghub.elsevier.com\/retrieve\/pii\/S0952197623003925"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8]]},"references-count":27,"alternative-id":["S0952197623003925"],"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1016\/j.engappai.2023.106208","relation":{},"ISSN":["0952-1976"],"issn-type":[{"value":"0952-1976","type":"print"}],"subject":[],"published":{"date-parts":[[2023,8]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Operating performance assessment method for industrial process with slowness principle-based LSTM network","name":"articletitle","label":"Article Title"},{"value":"Engineering Applications of Artificial Intelligence","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1016\/j.engappai.2023.106208","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2023 Published by Elsevier Ltd.","name":"copyright","label":"Copyright"}],"article-number":"106208"}}