{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,16]],"date-time":"2026-03-16T09:42:42Z","timestamp":1773654162643,"version":"3.50.1"},"reference-count":81,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2023,7,26]],"date-time":"2023-07-26T00:00:00Z","timestamp":1690329600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,7,26]],"date-time":"2023-07-26T00:00:00Z","timestamp":1690329600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100003593","name":"Conselho Nacional de Desenvolvimento Cient\u00edfico e Tecnol\u00f3gico","doi-asserted-by":"publisher","award":["140913\/2019-0"],"award-info":[{"award-number":["140913\/2019-0"]}],"id":[{"id":"10.13039\/501100003593","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003593","name":"Conselho Nacional de Desenvolvimento Cient\u00edfico e Tecnol\u00f3gico","doi-asserted-by":"publisher","award":["311153\/2021-6"],"award-info":[{"award-number":["311153\/2021-6"]}],"id":[{"id":"10.13039\/501100003593","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Evolving Systems"],"published-print":{"date-parts":[[2024,4]]},"DOI":"10.1007\/s12530-023-09523-y","type":"journal-article","created":{"date-parts":[[2023,7,26]],"date-time":"2023-07-26T11:01:56Z","timestamp":1690369316000},"page":"611-633","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Enhancing fault detection and diagnosis systems for a chemical process: a study on convolutional neural networks and transfer learning"],"prefix":"10.1007","volume":"15","author":[{"given":"Ana Cl\u00e1udia Oliveira","family":"e Souza","sequence":"first","affiliation":[]},{"suffix":"Jr.","given":"Maur\u00edcio Bezerra","family":"de Souza","sequence":"additional","affiliation":[]},{"given":"Fl\u00e1vio Vasconcelos","family":"da Silva","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,7,26]]},"reference":[{"key":"9523_CR1","doi-asserted-by":"publisher","unstructured":"Abdelkrim C, Meridjet MS, Boutasseta N, Boulanouar L (2019) Detection and classification of bearing faults in industrial geared motors using temporal features and adaptive neuro-fuzzy inference system. https:\/\/doi.org\/10.1016\/j.heliyon.2019.e02046. Heliyon","DOI":"10.1016\/j.heliyon.2019.e02046"},{"key":"9523_CR2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-94463-0","volume-title":"Neural networks and deep learning \u2013 a Textbook","author":"CC Aggarwal","year":"2018","unstructured":"Aggarwal CC (2018) Neural networks and deep learning \u2013 a Textbook, vol 1. Springer Nature, ed. Switzerland"},{"key":"9523_CR3","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1145\/3292500.3330701","volume":"19","author":"T Akiba","year":"2019","unstructured":"Akiba T, Sano S, Yanase T, Ohta T, Koyama M (2019) Optuna: a next-generation hyperparameter optimization framework. KDD Appl Data Sci Track 19:4\u20138. https:\/\/doi.org\/10.1145\/3292500.3330701","journal-title":"KDD Appl Data Sci Track"},{"key":"9523_CR4","doi-asserted-by":"publisher","first-page":"214","DOI":"10.1016\/j.engappai.2017.10.020","volume":"68","author":"G Andonovski","year":"2018","unstructured":"Andonovski G, Mu\u0161i\u010d G, Bla\u017ei\u010d S, \u0160krjanc I (2018) Evolving model identification for process monitoring and prediction of non-linear systems. Eng Appl Artif Intell 68:214\u2013221. https:\/\/doi.org\/10.1016\/j.engappai.2017.10.020","journal-title":"Eng Appl Artif Intell"},{"issue":"3","key":"9523_CR6","doi-asserted-by":"publisher","first-page":"683","DOI":"10.1007\/s12555-010-0323-4","volume":"8","author":"S Baniardalani","year":"2010","unstructured":"Baniardalani S, Askari J, Lunze J (2010) Qualitative model based fault diagnosis using a threshold level. Int J Control Autom Syst 8(3):683\u2013694. https:\/\/doi.org\/10.1007\/s12555-010-0323-4","journal-title":"Int J Control Autom Syst"},{"key":"9523_CR5","doi-asserted-by":"publisher","first-page":"309","DOI":"10.1016\/j.ifacol.2015.08.199","volume":"48","author":"A Bathelt","year":"2014","unstructured":"Bathelt A, Ricker NL, Jelali M (2014) Revision of the Tennessee Eastman process model. IFAC-PapersOnLine 48:309\u2013314. https:\/\/doi.org\/10.1016\/j.ifacol.2015.08.199","journal-title":"IFAC-PapersOnLine"},{"key":"9523_CR7","doi-asserted-by":"publisher","first-page":"840","DOI":"10.1002\/ceat.200800486","volume":"32","author":"RM Behbahani","year":"2009","unstructured":"Behbahani RM, Jazayeri-Rad H, Hajmirzaee S (2009) Fault detection and diagnosis in a sour gas absorption column using neural networks. Chem Eng Technol 32:840\u2013845. https:\/\/doi.org\/10.1002\/ceat.200800486","journal-title":"Chem Eng Technol"},{"key":"9523_CR8","doi-asserted-by":"publisher","unstructured":"Botalb A, Moinuddin M, Al-Saggaf UM, Ali SSA (2018) Contrasting Convolutional Neural Network (CNN) with Multi-Layer Perceptron (MLP) for Big Data Analysis. International Conference on Intelligent and Advanced System (ICIAS) 1\u20135. https:\/\/doi.org\/10.1109\/ICIAS.2018.8540626","DOI":"10.1109\/ICIAS.2018.8540626"},{"key":"9523_CR9","unstructured":"Braatz RD (2020) Tennessee Eastman problem simulation data. Massachusetts Institute of Technology. http:\/\/web.mit.edu\/braatzgroup\/links.html. Accessed 20 December 2021"},{"key":"9523_CR10","unstructured":"C\u00e2mara MM (2019) GitHub. tep2py. https:\/\/github.com\/camaramm\/tep2py. Accessed 20 December 2021"},{"key":"9523_CR11","doi-asserted-by":"publisher","first-page":"339","DOI":"10.1109\/TII.2019.2917233","volume":"16","author":"Z Chen","year":"2019","unstructured":"Chen Z, Gryllias K, Li W (2019) Intelligent fault diagnosis for rotary machinery using transferable convolutional neural network. IEEE Trans Industr Inf 16:339\u2013349. https:\/\/doi.org\/10.1109\/TII.2019.2917233","journal-title":"IEEE Trans Industr Inf"},{"key":"9523_CR12","doi-asserted-by":"publisher","first-page":"6139","DOI":"10.3390\/s20216139","volume":"20","author":"H Cheng","year":"2020","unstructured":"Cheng H, Liu Y, Huang D, Xu C, Wu J (2020) A novel ensemble adaptive sparse bayesian transfer learning machine for nonlinear large-scale process monitoring. Sensors 20:6139. https:\/\/doi.org\/10.3390\/s20216139","journal-title":"Sensors"},{"key":"9523_CR13","doi-asserted-by":"publisher","first-page":"243","DOI":"10.1016\/S0169-7439(99)00061-1","volume":"50","author":"LH Chiang","year":"2000","unstructured":"Chiang LH, Russell EL, Braatz RD (2000) Fault diagnosis in chemical processes using Fisher discriminant analysis, discriminant partial least squares, and principal component analysis. Chemometr Intell Lab Syst 50:243\u2013252. https:\/\/doi.org\/10.1016\/S0169-7439(99)00061-1","journal-title":"Chemometr Intell Lab Syst"},{"issue":"3","key":"9523_CR14","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1177\/014233129802000302","volume":"20","author":"T Dalton","year":"1998","unstructured":"Dalton T, Patton R (1998) Model-based fault diagnosis of a two-pump system. Trans Inst Meas Control 20(3):115\u2013124. https:\/\/doi.org\/10.1177\/014233129802000302","journal-title":"Trans Inst Meas Control"},{"key":"9523_CR15","doi-asserted-by":"publisher","first-page":"245","DOI":"10.1016\/0098-1354(93)80018-I","volume":"17","author":"JJ Downs","year":"1993","unstructured":"Downs JJ, Vogel EF (1993) A plant-wide industrial process control problem. Comput Chem Eng 17:245\u2013255. https:\/\/doi.org\/10.1016\/0098-1354(93)80018-I","journal-title":"Comput Chem Eng"},{"key":"9523_CR16","doi-asserted-by":"publisher","unstructured":"Gao Y, Yang T, Xing N, Xu M Fault Detection and Diagnosis for Spacecraft using Principal Component Analysis and Support Vector Machines. 2012 7th IEEE Conference on Industrial, Electronics (2012) and Applications (ICIEA). https:\/\/doi.org\/10.1109\/ICIEA.2012.6361054","DOI":"10.1109\/ICIEA.2012.6361054"},{"key":"9523_CR17","volume-title":"Deep learning. 1 ed","author":"I Goodfellow","year":"2016","unstructured":"Goodfellow I, Bengio Y, Courville A (2016) Deep learning. 1 ed. MIT Press, Cambridge"},{"key":"9523_CR18","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/s12530-010-9001-2","volume":"1","author":"L Hartert","year":"2010","unstructured":"Hartert L, Mouchaweh MS, Billaudel P (2010) A semi-supervised dynamic version of fuzzy K-Nearest neighbours to monitor evolving systems. Evol Syst 1:3\u201315. https:\/\/doi.org\/10.1007\/s12530-010-9001-2","journal-title":"Evol Syst"},{"key":"9523_CR19","doi-asserted-by":"publisher","first-page":"470","DOI":"10.1016\/j.ifacol.2018.09.380","volume":"51","author":"S Heo","year":"2018","unstructured":"Heo S, Lee JH (2018) Fault detection and classification using artificial neural networks. IFAC Papers Online 51:470\u2013475. https:\/\/doi.org\/10.1016\/j.ifacol.2018.09.380","journal-title":"IFAC Papers Online"},{"key":"9523_CR20","doi-asserted-by":"publisher","first-page":"249","DOI":"10.1016\/j.neucom.2021.04.112","volume":"459","author":"SCH Hoi","year":"2021","unstructured":"Hoi SCH, Sahoo D, Lu J, Zhao P (2021) Online learning: a comprehensive survey. Neurocomputing 459:249\u2013289. https:\/\/doi.org\/10.1016\/j.neucom.2021.04.112","journal-title":"Neurocomputing"},{"issue":"3","key":"9523_CR21","doi-asserted-by":"publisher","first-page":"574","DOI":"10.1113\/jphysiol.1959.sp006308","volume":"124","author":"DH Hubel","year":"1959","unstructured":"Hubel DH, Wiesel T (1959) Receptive fields of single neurones in the cat\u2019s striate cortex. J Physiol 124(3):574\u2013591. https:\/\/doi.org\/10.1113\/jphysiol.1959.sp006308","journal-title":"J Physiol"},{"key":"9523_CR22","doi-asserted-by":"publisher","first-page":"75","DOI":"10.4028\/www.scientific.net\/AMM.735.75","volume":"735","author":"NE Hussin","year":"2015","unstructured":"Hussin NE, Johari A, Kidam K, Hashim H (2015) Major hazards of process equipment failures in the chemical process industry. Appl Mech Mater 735:75\u201379. https:\/\/doi.org\/10.4028\/www.scientific.net\/AMM.735.75","journal-title":"Appl Mech Mater"},{"key":"9523_CR23","doi-asserted-by":"publisher","DOI":"10.1007\/3-540-30368-5","volume-title":"Fault-Diagnosis Systems: an introduction from fault detection to fault tolerance","author":"R Isermann","year":"2006","unstructured":"Isermann R (2006) Fault-Diagnosis Systems: an introduction from fault detection to fault tolerance, 1 edn. Springer, Germany","edition":"1"},{"key":"9523_CR24","doi-asserted-by":"publisher","unstructured":"Karimi I, Salahshoor K (2012) A new fault detection and diagnosis approach for a distillation column based on a combined PCA and ANFIS scheme. 2012 24th Chinese Control and Decision Conference (CCDC). https:\/\/doi.org\/10.1109\/CCDC.2012.6244542","DOI":"10.1109\/CCDC.2012.6244542"},{"key":"9523_CR25","doi-asserted-by":"publisher","unstructured":"Khalifani S, Darvishzadeh R, Azad N, Rahmani RS (2022) Prediction of sunflower grain yield under normal and salinity stress by RBF, MLP and, CNN models. Ind Crops Prod 189(115762). https:\/\/doi.org\/10.1016\/j.indcrop.2022.115762","DOI":"10.1016\/j.indcrop.2022.115762"},{"key":"9523_CR26","unstructured":"Kingma DP, Ba JL (2015) Adam: a method for stochastic optimization. 3rd International Conference for Learning Representations (ICLR 2015)"},{"key":"9523_CR32","doi-asserted-by":"publisher","unstructured":"Knowledge transfer from simulation to physical processes. Comput Chem Eng. https:\/\/doi.org\/10.1016\/j.compchemeng.2020.106904","DOI":"10.1016\/j.compchemeng.2020.106904"},{"key":"9523_CR27","doi-asserted-by":"publisher","first-page":"4889","DOI":"10.1021\/ie000586y","volume":"40","author":"T Larsson","year":"2001","unstructured":"Larsson T et al (2001) Self-optimizing control of a large-scale plant: the Tennessee Eastman process. Ind Eng Chem Res 40:4889\u20134901. https:\/\/doi.org\/10.1021\/ie000586y","journal-title":"Ind Eng Chem Res"},{"key":"9523_CR28","doi-asserted-by":"publisher","first-page":"559","DOI":"10.1016\/j.isatra.2010.06.007","volume":"49","author":"CK Lau","year":"2010","unstructured":"Lau CK, Heng YS, Hussain MA, Mohamad Nor MI (2010) Fault diagnosis of the polypropylene production process (UNIPOL PP) using ANFIS. ISA Trans 49:559\u2013566. https:\/\/doi.org\/10.1016\/j.isatra.2010.06.007","journal-title":"ISA Trans"},{"key":"9523_CR29","doi-asserted-by":"publisher","unstructured":"Lecun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11):2278\u20132324. https:\/\/doi.org\/10.1109\/5.726791","DOI":"10.1109\/5.726791"},{"key":"9523_CR30","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y Lecun","year":"2015","unstructured":"Lecun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521:436\u2013444. https:\/\/doi.org\/10.1038\/nature14539","journal-title":"Nature"},{"key":"9523_CR31","unstructured":"Li W, Gu S, Zhang X, Chen T (2020) Transfer learning for process fault diagnosis"},{"key":"9523_CR33","doi-asserted-by":"publisher","first-page":"108548","DOI":"10.1016\/j.buildenv.2021.108548","volume":"207","author":"T Li","year":"2022","unstructured":"Li T, Zhao Y, Zhang C, Zhou K, Zhang X (2022) A semantic model-based fault detection approach for building energy systems. Build Environ 207:108548. https:\/\/doi.org\/10.1016\/j.buildenv.2021.108548","journal-title":"Build Environ"},{"key":"9523_CR34","doi-asserted-by":"publisher","first-page":"1104","DOI":"10.1016\/j.ijrefrig.2006.12.012","volume":"30","author":"J Liang","year":"2007","unstructured":"Liang J, Du R (2007) Model-based Fault Detection and diagnosis of HVAC systems using support Vector Machine method. Int J Refrig 30:1104\u20131114. https:\/\/doi.org\/10.1016\/j.ijrefrig.2006.12.012","journal-title":"Int J Refrig"},{"key":"9523_CR35","doi-asserted-by":"publisher","first-page":"171423","DOI":"10.1109\/ACCESS.2019.2956052","volume":"7","author":"Q Liu","year":"2019","unstructured":"Liu Q, Huang C (2019) Fault diagnosis method based on transfer convolutional neural networks. IEEE Access 7:171423\u2013171430","journal-title":"IEEE Access"},{"key":"9523_CR36","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1016\/0098-1354(94)00057-U","volume":"19","author":"PR Lyman","year":"1995","unstructured":"Lyman PR, Georgakis M (1995) Plant-wide control of the Tennessee Eastman process. Comput Chem Eng 19:321\u2013331. https:\/\/doi.org\/10.1016\/0098-1354(94)00057-U","journal-title":"Comput Chem Eng"},{"key":"9523_CR38","doi-asserted-by":"publisher","first-page":"1627","DOI":"10.1016\/j.jprocont.2009.07.011","volume":"19","author":"S Mahadevan","year":"2009","unstructured":"Mahadevan S, Shah SL (2009) Fault detection and diagnosis in process data using one-class support vector machines. J Process Control 19:1627\u20131639. https:\/\/doi.org\/10.1016\/j.jprocont.2009.07.011","journal-title":"J Process Control"},{"key":"9523_CR39","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1007\/s12530-017-9206-8","volume":"9","author":"F Majdani","year":"2018","unstructured":"Majdani F, Petrovski A, Doolan D (2018) Evolving ANN\u2013based sensors for a context\u2013aware cyber physical system of an offshore gas turbine. Evol Syst 9:119\u2013133. https:\/\/doi.org\/10.1007\/s12530-017-9206-8","journal-title":"Evol Syst"},{"key":"9523_CR37","doi-asserted-by":"publisher","first-page":"279","DOI":"10.1016\/S0952-1976(97)00054-7","volume":"11","author":"FD McKenzie","year":"1998","unstructured":"McKenzie FD, Gonzalez AJ, Morris R (1998) An integrated model-based approach for real-time on-line diagnosis of complex systems. Eng Appl Artif Intell 11:279\u2013291. https:\/\/doi.org\/10.1016\/S0952-1976(97)00054-7","journal-title":"Eng Appl Artif Intell"},{"key":"9523_CR40","doi-asserted-by":"publisher","unstructured":"Medina E, Petraglia MR, Gomes JGRC, Petraglia A (2017) Comparison of CNN and MLP classifiers for algae detection in underwater pipelines. Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA). https:\/\/doi.org\/10.1109\/IPTA.2017.8310098","DOI":"10.1109\/IPTA.2017.8310098"},{"key":"9523_CR41","doi-asserted-by":"publisher","first-page":"107038","DOI":"10.1016\/j.triboint.2021.107038","volume":"160","author":"MVM Oliveira","year":"2021","unstructured":"Oliveira MVM, Cunha BZ, Daniel GB (2021) A model-based technique to identify lubrication condition of hydrodynamic bearings using the rotor vibrational response. Tribol Int 160:107038. https:\/\/doi.org\/10.1016\/j.triboint.2021.107038","journal-title":"Tribol Int"},{"key":"9523_CR42","doi-asserted-by":"publisher","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","volume":"22","author":"SJ Pan","year":"2009","unstructured":"Pan SJ, Yang Q (2009) A survey on transfer learning. IEEE Trans Knowl Data Eng 22:1345\u20131359. https:\/\/doi.org\/10.1109\/TKDE.2009.191","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"9523_CR43","doi-asserted-by":"publisher","DOI":"10.3390\/s19214612","author":"P Park","year":"2019","unstructured":"Park P, Di Marco P, Shin H, Bang J (2019) Fault detection and diagnosis using combined uutoencoder and long short-term memory network. Sensors. https:\/\/doi.org\/10.3390\/s19214612","journal-title":"Sensors"},{"key":"9523_CR44","doi-asserted-by":"publisher","unstructured":"Pu X, Li C (2021) Online semisupervised broad learning system for industrial fault diagnosis. IEEE Trans Industr Inf 17(10). https:\/\/doi.org\/10.1109\/TII.2020.3048990","DOI":"10.1109\/TII.2020.3048990"},{"key":"9523_CR45","doi-asserted-by":"publisher","unstructured":"Renton G, Chatelain C, Adam S, Kermorvant C, Paquet T (2017) Handwritten text line segmentation using fully convolutional network. 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), pp\u00a05\u20139. https:\/\/doi.org\/10.1109\/ICDAR.2017.321","DOI":"10.1109\/ICDAR.2017.321"},{"key":"9523_CR46","unstructured":"Ricker NL (2005) Tennessee Eastman Challenge Archive. http:\/\/depts.washington.edu\/control\/LARRY\/TE\/download.html. Accessed 20 December 2021"},{"key":"9523_CR47","doi-asserted-by":"publisher","DOI":"10.7910\/DVN\/6C3JR1","author":"CA Rieth","year":"2017","unstructured":"Rieth CA, Amsel BD, Tran R, Cook MB (2017) Additional Tennessee Eastman Process Simulation Data for Anomaly Detection Evaluation. Harvard Dataverse, V1. https:\/\/doi.org\/10.7910\/DVN\/6C3JR1"},{"key":"9523_CR48","doi-asserted-by":"publisher","first-page":"914","DOI":"10.1016\/j.energy.2015.06.042","volume":"89","author":"K Rostek","year":"2015","unstructured":"Rostek K, Morytko L, Jankowska A (2015) Early detection and prediction of leaks in fluidized-bed boilers using artificial neural networks. Energy 89:914\u2013923. https:\/\/doi.org\/10.1016\/j.energy.2015.06.042","journal-title":"Energy"},{"key":"9523_CR49","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1016\/S0169-7439(00)00058-7","volume":"51","author":"EL Russell","year":"2000","unstructured":"Russell EL, Chiang LH, Braatz RD (2000) Fault detection in industrial processes using canonical variate analysis and dynamic principal component analysis. Chemometr Intell Lab Syst 51:81\u201393. https:\/\/doi.org\/10.1016\/S0169-7439(00)00058-7","journal-title":"Chemometr Intell Lab Syst"},{"key":"9523_CR50","doi-asserted-by":"publisher","first-page":"115983","DOI":"10.1016\/j.eswa.2021.115983","volume":"189","author":"MR Santos","year":"2022","unstructured":"Santos MR, Costa BSJ, Bezerra CG, Andonovski G, Guedes LA (2022) An evolving approach for fault diagnosis of dynamic systems. Expert Syst Appl 189:115983. https:\/\/doi.org\/10.1016\/j.eswa.2021.115983","journal-title":"Expert Syst Appl"},{"key":"9523_CR51","doi-asserted-by":"publisher","DOI":"10.1155\/2023\/1264345","author":"A Saxena","year":"2023","unstructured":"Saxena A, Kumar R, Rawat AK, Majid M, Singh J, Devakirubakaran S, Singh GK (2023) Abnormal health monitoring and assessment of a three-phase induction motor using a supervised CNN-RNN-based machine learning algorithm. Math Probl Eng. https:\/\/doi.org\/10.1155\/2023\/1264345","journal-title":"Math Probl Eng"},{"key":"9523_CR52","doi-asserted-by":"publisher","first-page":"640","DOI":"10.1109\/TPAMI.2016.2572683","volume":"39","author":"E Shelhamer","year":"2017","unstructured":"Shelhamer E, Long J, Darrell T (2017) Fully convolutional networks for semantic segmentation. IEEE Trans Pattern Anal Mach Intell 39:640\u2013651. https:\/\/doi.org\/10.1109\/TPAMI.2016.2572683","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"9523_CR53","doi-asserted-by":"publisher","unstructured":"Shi J, Peng D, Peng Z, Zhang Z, Goebel K, Wu D (2022) Planetary gearbox fault diagnosis using bidirectional-convolutional LSTM networks. Mech Syst Signal Process 162(107996). https:\/\/doi.org\/10.1016\/j.ymssp.2021.107996","DOI":"10.1016\/j.ymssp.2021.107996"},{"key":"9523_CR54","doi-asserted-by":"publisher","first-page":"395","DOI":"10.1016\/j.cie.2005.01.009","volume":"48","author":"HJ Shin","year":"2005","unstructured":"Shin HJ, Eom D, Kim S (2005) One-class support vector machines\u2014an application in machine fault detection and classification. Comput Ind Eng 48:395\u2013408. https:\/\/doi.org\/10.1016\/j.cie.2005.01.009","journal-title":"Comput Ind Eng"},{"key":"9523_CR55","doi-asserted-by":"publisher","first-page":"104","DOI":"10.1016\/j.compchemeng.2016.04.011","volume":"91","author":"Y Shu","year":"2016","unstructured":"Shu Y et al (2016) Abnormal situation management: Challenges and opportunities in the big data era. Comput Chem Eng 91:104\u2013113. https:\/\/doi.org\/10.1016\/j.compchemeng.2016.04.011","journal-title":"Comput Chem Eng"},{"key":"9523_CR56","doi-asserted-by":"publisher","first-page":"341","DOI":"10.1016\/j.mechatronics.2006.01.002","volume":"16","author":"S Simani","year":"2006","unstructured":"Simani S, Fantuzzi C (2006) Dynamic system identification and model-based fault diagnosis of an industrial gas turbine prototype. Mechatronics 16:341\u2013363. https:\/\/doi.org\/10.1016\/j.mechatronics.2006.01.002","journal-title":"Mechatronics"},{"key":"9523_CR57","unstructured":"Souza ACO (2021) new-tep-datasets. v1. https:\/\/github.com\/anasouza26\/new_tep_datasets. Accessed 20 December 2021"},{"key":"9523_CR58","unstructured":"Springenberg JT, Dosovitskiy A, Brox T, Riedmiller M (2015) Striving for simplicity: the all convolutional net. International Conference on Learning Representations (ICLR), pp\u00a01\u201314"},{"key":"9523_CR59","doi-asserted-by":"publisher","first-page":"362","DOI":"10.1016\/j.asoc.2014.02.008","volume":"19","author":"P Subbaraj","year":"2014","unstructured":"Subbaraj P, Kannapiran B (2014) Fault detection and diagnosis of pneumatic valve using adaptive neuro-fuzzy inference system approach. Appl Soft Comput 19:362\u2013371. https:\/\/doi.org\/10.1016\/j.asoc.2014.02.008","journal-title":"Appl Soft Comput"},{"key":"9523_CR60","doi-asserted-by":"publisher","DOI":"10.3390\/rs13163211","author":"T Tian","year":"2021","unstructured":"Tian T, Chu Z, Hu Q, Ma L (2021) Class-wise fully convolutional network for semantic segmentation of remote sensing images. Remote Sens. https:\/\/doi.org\/10.3390\/rs13163211","journal-title":"Remote Sens"},{"key":"9523_CR61","doi-asserted-by":"publisher","unstructured":"Tidriri K, Chatti N, Verron S, Tiplica T (2018) Model-based fault detection and diagnosis of complex chemical processes: A case study of the Tennessee Eastman process. Proceeding of the Institution of Mechanical Engineers Part I - Journal of Systems and Control Engineering 232(6):742\u2013760. https:\/\/doi.org\/10.1177\/0959651818764510","DOI":"10.1177\/0959651818764510"},{"key":"9523_CR62","doi-asserted-by":"publisher","DOI":"10.1016\/j.spl.2018.02.038","author":"JL Torrecilla","year":"2018","unstructured":"Torrecilla JL, Romo J (2018) Stat Probab Lett 136:15\u201319. https:\/\/doi.org\/10.1016\/j.spl.2018.02.038. Data learning from big data"},{"key":"9523_CR63","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1007\/s12530-014-9119-8","volume":"6","author":"H Toubakh","year":"2015","unstructured":"Toubakh H, Sayed-Mouchaweh M (2015) Hybrid dynamic data-driven approach for drift-like fault detection in wind turbines. Evol Syst 6:115\u2013129. https:\/\/doi.org\/10.1007\/s12530-014-9119-8","journal-title":"Evol Syst"},{"issue":"2","key":"9523_CR68","doi-asserted-by":"publisher","first-page":"466","DOI":"10.1002\/aic.16489","volume":"65","author":"V Venkatasubramanian","year":"2019","unstructured":"Venkatasubramanian V (2019) The promise of artificial intelligence in chemical engineering: is it here. finally? AlChE Journal 65(2):466\u2013478. https:\/\/doi.org\/10.1002\/aic.16489","journal-title":"finally? AlChE Journal"},{"key":"9523_CR64","doi-asserted-by":"publisher","first-page":"699","DOI":"10.1016\/0098-1354(90)87081-Y","volume":"14","author":"V Venkatasubramanian","year":"1990","unstructured":"Venkatasubramanian V, Vaidyanathan R, Yamamoto Y (1990) Process fault detection and diagnosis using neural networks\u2014I. steady-state processes. Comput Chem Eng 14:699\u2013712. https:\/\/doi.org\/10.1016\/0098-1354(90)87081-Y","journal-title":"Comput Chem Eng"},{"key":"9523_CR65","doi-asserted-by":"publisher","first-page":"293","DOI":"10.1016\/S0098-1354(02)00160-6","volume":"27","author":"V Venkatasubramanian","year":"2003","unstructured":"Venkatasubramanian V et al (2003a) A review of process fault detection and diagnosis, part I: quantitative model-based methods. Comput Chem Eng 27:293\u2013311. https:\/\/doi.org\/10.1016\/S0098-1354(02)00160-6","journal-title":"Comput Chem Eng"},{"key":"9523_CR66","doi-asserted-by":"publisher","first-page":"313","DOI":"10.1016\/S0098-1354(02)00161-8","volume":"27","author":"V Venkatasubramanian","year":"2003","unstructured":"Venkatasubramanian V et al (2003b) A review of process fault detection and diagnosis, part II: qualitative models and search strategies. Comput Chem Eng 27:313\u2013326. https:\/\/doi.org\/10.1016\/S0098-1354(02)00161-8","journal-title":"Comput Chem Eng"},{"key":"9523_CR67","doi-asserted-by":"publisher","first-page":"327","DOI":"10.1016\/S0098-1354(02)00162-X","volume":"27","author":"V Venkatasubramanian","year":"2003","unstructured":"Venkatasubramanian V et al (2003c) A review of process fault detection and diagnosis, part III: process history based methods. Comput Chem Eng 27:327\u2013346. https:\/\/doi.org\/10.1016\/S0098-1354(02)00162-X","journal-title":"Comput Chem Eng"},{"key":"9523_CR69","doi-asserted-by":"publisher","first-page":"3515","DOI":"10.1002\/aic.10982","volume":"52","author":"H Wang","year":"2006","unstructured":"Wang H, Li P, Gao F, Song Z, Ding SX (2006) Kernel classifier with adaptive structure and fixed memory for process diagnosis. AIChE J 52:3515\u20133531. https:\/\/doi.org\/10.1002\/aic.10982","journal-title":"AIChE J"},{"issue":"5","key":"9523_CR71","doi-asserted-by":"publisher","first-page":"1268","DOI":"10.1109\/JAS.2019.1911618","volume":"6","author":"X Wang","year":"2019","unstructured":"Wang X, Liu X, Li Y (2019) An incremental model transfer method for complex process fault diagnosis. IEEE\/CAA J Automatica Sinica 6(5):1268\u20131280. https:\/\/doi.org\/10.1109\/JAS.2019.1911618","journal-title":"IEEE\/CAA J Automatica Sinica"},{"key":"9523_CR70","doi-asserted-by":"publisher","first-page":"108851","DOI":"10.1016\/j.knosys.2022.108851","volume":"248","author":"K Wang","year":"2022","unstructured":"Wang K, Zhou W, Mo Y, Yuan X, Wang Y, Yang C (2022) New mode cold start monitoring in industrial processes: a solution of spatial\u2013temporal feature transfer. Knowl Based Syst 248:108851. https:\/\/doi.org\/10.1016\/j.knosys.2022.108851","journal-title":"Knowl Based Syst"},{"key":"9523_CR72","doi-asserted-by":"publisher","unstructured":"Wu H, Zhao J (2018) Deep convolutional neural network model based chemical process fault diagnosis. Computers & Chemical Engineering 115:185\u2013197. https:\/\/doi.org\/10.1016\/j.compchemeng.2018.04.009","DOI":"10.1016\/j.compchemeng.2018.04.009"},{"key":"9523_CR73","doi-asserted-by":"publisher","DOI":"10.1016\/j.compchemeng.2020.106731","author":"H Wu","year":"2020","unstructured":"Wu H, Zhao J (2020) Fault detection and diagnosis based on transfer learning for multimode chemical processes. Comput Chem Eng. https:\/\/doi.org\/10.1016\/j.compchemeng.2020.106731","journal-title":"Comput Chem Eng"},{"key":"9523_CR74","doi-asserted-by":"publisher","unstructured":"Xavier GM, Seixas JM (2018) Fault detection and diagnosis in a chemical process using long short-term memory recurrent neural network. 2018 International Joint Conference on Neural Networks (IJCNN). https:\/\/doi.org\/10.1109\/IJCNN.2018.8489385","DOI":"10.1109\/IJCNN.2018.8489385"},{"key":"9523_CR75","doi-asserted-by":"publisher","unstructured":"Xie D, Bai L (2015) A hierarchical deep neural network for fault diagnosis on Tennessee-Eastman process. 2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA). https:\/\/doi.org\/10.1109\/ICMLA.2015.208","DOI":"10.1109\/ICMLA.2015.208"},{"key":"9523_CR76","doi-asserted-by":"publisher","first-page":"2453","DOI":"10.1002\/cjce.23491","volume":"97","author":"Z Xie","year":"2019","unstructured":"Xie Z, Yang X, Li A, Ji Z (2019) Fault diagnosis in Industrial Chemical processes using optimal probabilistic neural network. Can J Chem Eng 97:2453\u20132464. https:\/\/doi.org\/10.1002\/cjce.23491","journal-title":"Can J Chem Eng"},{"key":"9523_CR77","doi-asserted-by":"publisher","first-page":"662","DOI":"10.1007\/978-981-19-3923-5_57","volume":"921","author":"LZ Yong","year":"2022","unstructured":"Yong LZ, Nugroho H (2022) Acoustic anomaly detection of mechanical failure: time-distributed CNN-RNN deep learning models. Control, instrumentation and mechatronics: theory and practice. Lecture Notes in Electrical Engineering 921:662\u2013672. https:\/\/doi.org\/10.1007\/978-981-19-3923-5_57","journal-title":"Lecture Notes in Electrical Engineering"},{"key":"9523_CR79","doi-asserted-by":"publisher","first-page":"395","DOI":"10.1016\/j.compchemeng.2017.02.041","volume":"107","author":"Z Zhang","year":"2017","unstructured":"Zhang Z, Zhao J (2017) A deep belief network based fault diagnosis model for complex chemical processes. Comput Chem Eng 107:395\u2013407. https:\/\/doi.org\/10.1016\/j.compchemeng.2017.02.041","journal-title":"Comput Chem Eng"},{"key":"9523_CR80","doi-asserted-by":"publisher","first-page":"14347","DOI":"10.1109\/ACCESS.2017.2720965","volume":"5","author":"R Zhang","year":"2017","unstructured":"Zhang R, Tao H, Wu L, Guan Y (2017) Transfer learning with neural networks for bearing fault diagnosis in changing working conditions. IEEE Access 5:14347\u201314357. https:\/\/doi.org\/10.1109\/ACCESS.2017.2720965","journal-title":"IEEE Access"},{"key":"9523_CR78","doi-asserted-by":"publisher","first-page":"824","DOI":"10.1021\/acs.iecr.9b05885","volume":"59","author":"S Zhang","year":"2020","unstructured":"Zhang S, Bi K, Qiu T (2020) Bidirectional recurrent neural network-based chemical process fault diagnosis. Ind Eng Chem Res 59:824\u2013834. https:\/\/doi.org\/10.1021\/acs.iecr.9b05885","journal-title":"Ind Eng Chem Res"},{"key":"9523_CR81","doi-asserted-by":"publisher","first-page":"812","DOI":"10.1016\/j.cjche.2014.05.016","volume":"22","author":"Q Zhu","year":"2014","unstructured":"Zhu Q, Jia Y, Peng D, Xu Y (2014) Study and application of fault prediction methods with improved reservoir neural networks. Chin J Chem Eng 22:812\u2013819. https:\/\/doi.org\/10.1016\/j.cjche.2014.05.016","journal-title":"Chin J Chem Eng"}],"container-title":["Evolving Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12530-023-09523-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12530-023-09523-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12530-023-09523-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,28]],"date-time":"2024-03-28T12:23:36Z","timestamp":1711628616000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12530-023-09523-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,26]]},"references-count":81,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2024,4]]}},"alternative-id":["9523"],"URL":"https:\/\/doi.org\/10.1007\/s12530-023-09523-y","relation":{"references":[{"id-type":"uri","id":"","asserted-by":"subject"},{"id-type":"uri","id":"","asserted-by":"subject"}]},"ISSN":["1868-6478","1868-6486"],"issn-type":[{"value":"1868-6478","type":"print"},{"value":"1868-6486","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,7,26]]},"assertion":[{"value":"28 July 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 July 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 July 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}