{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T03:43:26Z","timestamp":1775274206457,"version":"3.50.1"},"reference-count":87,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2018,12,13]],"date-time":"2018-12-13T00:00:00Z","timestamp":1544659200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100003200","name":"MOSTI","doi-asserted-by":"crossref","award":["0153AB-B67"],"award-info":[{"award-number":["0153AB-B67"]}],"id":[{"id":"10.13039\/501100003200","id-type":"DOI","asserted-by":"crossref"}]},{"name":"eScience Fund","award":["0153AB-B67\/03-02-02-SF0236"],"award-info":[{"award-number":["0153AB-B67\/03-02-02-SF0236"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2020,1]]},"DOI":"10.1007\/s00521-018-3911-5","type":"journal-article","created":{"date-parts":[[2018,12,13]],"date-time":"2018-12-13T04:49:12Z","timestamp":1544676552000},"page":"447-472","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":96,"title":["Neural network applications in fault diagnosis and detection: an overview of implementations in engineering-related systems"],"prefix":"10.1007","volume":"32","author":[{"given":"Ahmad Azharuddin Azhari","family":"Mohd Amiruddin","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1821-1028","authenticated-orcid":false,"given":"Haslinda","family":"Zabiri","sequence":"additional","affiliation":[]},{"given":"Syed Ali Ammar","family":"Taqvi","sequence":"additional","affiliation":[]},{"given":"Lemma Dendena","family":"Tufa","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2018,12,13]]},"reference":[{"issue":"6","key":"3911_CR1","first-page":"2878","volume":"4","author":"DH Pandya","year":"2012","unstructured":"Pandya DH, Upadhyay SH, Harsha SP (2012) ANN based fault diagnosis of rolling element bearing using time-frequency domain feature. Int J Eng Sci Technol 4(6):2878\u20132886","journal-title":"Int J Eng Sci Technol"},{"key":"3911_CR2","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1016\/j.asoc.2013.09.024","volume":"14","author":"J Zhou","year":"2014","unstructured":"Zhou J et al (2014) Fault detection and identification spanning multiple processes by integrating PCA with neural network. Appl Soft Comput 14:4\u201311","journal-title":"Appl Soft Comput"},{"key":"3911_CR3","doi-asserted-by":"crossref","unstructured":"Gastaldello D et al (2012) Fault location in underground systems using artificial neural networks and PSCAD\/EMTDC. In: IEEE 16th international conference on intelligent engineering systems (INES) 2012. IEEE, Lisbon, pp 423\u2013427","DOI":"10.1109\/INES.2012.6249871"},{"issue":"3","key":"3911_CR4","doi-asserted-by":"crossref","first-page":"785672","DOI":"10.1155\/S1110865704310085","volume":"2004","author":"B Samanta","year":"2004","unstructured":"Samanta B, Al-Balushi KR, Al-Araimi SA (2004) Bearing fault detection using artificial neural networks and genetic algorithm. EURASIP J Adv Signal Process 2004(3):785672","journal-title":"EURASIP J Adv Signal Process"},{"issue":"7","key":"3911_CR5","doi-asserted-by":"crossref","first-page":"1645","DOI":"10.1016\/S0031-3203(03)00005-0","volume":"36","author":"A Kumar","year":"2003","unstructured":"Kumar A (2003) Neural network based detection of local textile defects. Pattern Recognit 36(7):1645\u20131659","journal-title":"Pattern Recognit"},{"issue":"Supplement C","key":"3911_CR6","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.simpat.2016.05.005","volume":"67","author":"H Mekki","year":"2016","unstructured":"Mekki H, Mellit A, Salhi H (2016) Artificial neural network-based modelling and fault detection of partial shaded photovoltaic modules. Simul Model Pract Theory 67(Supplement C):1\u201313","journal-title":"Simul Model Pract Theory"},{"issue":"2, Part 5","key":"3911_CR7","doi-asserted-by":"crossref","first-page":"547","DOI":"10.1016\/S1474-6670(17)48326-1","volume":"26","author":"BC Hwang","year":"1993","unstructured":"Hwang BC, Saif M, Jamshidi M (1993) Neural based fault detection and identification for a nuclear reactor. IFAC Proc Vol 26(2, Part 5):547\u2013550","journal-title":"IFAC Proc Vol"},{"key":"3911_CR8","volume-title":"Issues of fault diagnosis for dynamic systems","author":"RJ Patton","year":"2013","unstructured":"Patton RJ, Frank PM, Clark RN (2013) Issues of fault diagnosis for dynamic systems. Springer, Berlin"},{"issue":"1","key":"3911_CR9","doi-asserted-by":"crossref","first-page":"740","DOI":"10.1016\/j.asoc.2007.06.002","volume":"8","author":"S Rajakarunakaran","year":"2008","unstructured":"Rajakarunakaran S et al (2008) Artificial neural network approach for fault detection in rotary system. Appl Soft Comput 8(1):740\u2013748","journal-title":"Appl Soft Comput"},{"key":"3911_CR10","first-page":"302","volume-title":"Communications in Computer and Information Science","author":"Syed A. Taqvi","year":"2017","unstructured":"Taqvi S et al (2017) Artificial neural network for anomalies detection in distillation column. In: Modeling, design and simulation of systems: 17th Asia simulation conference, AsiaSim 2017, Melaka, Malaysia. Springer, Singapore"},{"key":"3911_CR11","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1016\/j.agwat.2016.03.015","volume":"171","author":"E L\u00f3pez-Mata","year":"2016","unstructured":"L\u00f3pez-Mata E et al (2016) Development of a direct-solution algorithm for determining the optimal crop planning of farms using deficit irrigation. Agric Water Manag 171:173\u2013187","journal-title":"Agric Water Manag"},{"issue":"3\u20134","key":"3911_CR12","doi-asserted-by":"crossref","first-page":"232","DOI":"10.1016\/j.jprocont.2007.07.004","volume":"18","author":"S Choudhury","year":"2008","unstructured":"Choudhury S, Jain M, Shah S (2008) Stiction-definition, modelling, detection and quantification. J Process Control 18(3\u20134):232\u2013243","journal-title":"J Process Control"},{"key":"3911_CR13","doi-asserted-by":"crossref","first-page":"354","DOI":"10.1007\/978-1-4615-5149-2","volume-title":"Robust model-based fault diagnosis for dynamic systems","author":"J Chen","year":"1999","unstructured":"Chen J, Patton RJ (1999) Robust model-based fault diagnosis for dynamic systems. Kluwer, New York, p 354"},{"issue":"6","key":"3911_CR14","doi-asserted-by":"crossref","first-page":"1392","DOI":"10.1109\/72.809084","volume":"10","author":"GPJ Schmitz","year":"1999","unstructured":"Schmitz GPJ, Aldrich C, Gouws FS (1999) ANN-DT: an algorithm for extraction of decision trees from artificial neural networks. IEEE Trans Neural Netw 10(6):1392\u20131401","journal-title":"IEEE Trans Neural Netw"},{"issue":"Supplement C","key":"3911_CR15","doi-asserted-by":"crossref","first-page":"365","DOI":"10.1016\/j.asoc.2016.08.039","volume":"49","author":"T Muhammad","year":"2016","unstructured":"Muhammad T, Halim Z (2016) Employing artificial neural networks for constructing metadata-based model to automatically select an appropriate data visualization technique. Appl Soft Comput 49(Supplement C):365\u2013384","journal-title":"Appl Soft Comput"},{"issue":"1","key":"3911_CR16","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/S0169-2070(97)00044-7","volume":"14","author":"G Zhang","year":"1998","unstructured":"Zhang G, Eddy Patuwo B, Hu MY (1998) Forecasting with artificial neural networks: the state of the art. Int J Forecast 14(1):35\u201362","journal-title":"Int J Forecast"},{"issue":"4","key":"3911_CR17","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1016\/0968-090X(95)00009-8","volume":"3","author":"M Dougherty","year":"1995","unstructured":"Dougherty M (1995) A review of neural networks applied to transport. Transp Res Part C Emerg Technol 3(4):247\u2013260","journal-title":"Transp Res Part C Emerg Technol"},{"issue":"2","key":"3911_CR18","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1006\/jaer.2000.0559","volume":"77","author":"DS Jayas","year":"2000","unstructured":"Jayas DS, Paliwal J, Visen NS (2000) Review paper (AE\u2014automation and emerging technologies): multi-layer neural networks for image analysis of agricultural products. J Agric Eng Res 77(2):119\u2013128","journal-title":"J Agric Eng Res"},{"issue":"2","key":"3911_CR19","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/0263-2241(96)00012-7","volume":"17","author":"M Catelani","year":"1996","unstructured":"Catelani M, Gori M (1996) On the application of neural networks to fault diagnosis of electronic analog circuit. Measurement 17(2):73\u201380","journal-title":"Measurement"},{"issue":"2","key":"3911_CR20","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1109\/82.823545","volume":"47","author":"M Aminian","year":"2000","unstructured":"Aminian M, Aminian F (2000) Neural-network based analog-circuit fault diagnosis using wavelet transform as preprocessor. IEEE Trans Circuits Syst II Analog Digit Signal Process 47(2):151","journal-title":"IEEE Trans Circuits Syst II Analog Digit Signal Process"},{"issue":"3","key":"3911_CR21","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1109\/82.558453","volume":"44","author":"R Spina","year":"1997","unstructured":"Spina R, Upadhyaya S (1997) Linear circuit fault diagnosis using neuromorphic analyzers. IEEE Trans Circuits Syst II Analog Digit Signal Process 44(3):188\u2013196","journal-title":"IEEE Trans Circuits Syst II Analog Digit Signal Process"},{"issue":"5","key":"3911_CR22","doi-asserted-by":"crossref","first-page":"352","DOI":"10.1016\/S1532-0464(03)00034-0","volume":"35","author":"S Dreiseitl","year":"2002","unstructured":"Dreiseitl S, Ohno-Machado L (2002) Logistic regression and artificial neural network classification models: a methodology review. J Biomed Inform 35(5):352\u2013359","journal-title":"J Biomed Inform"},{"key":"3911_CR23","volume-title":"Artificial neural networks for the modelling and fault diagnosis of technical processes","author":"K Patan","year":"2008","unstructured":"Patan K (2008) Artificial neural networks for the modelling and fault diagnosis of technical processes. Springer, Berlin"},{"issue":"1","key":"3911_CR24","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/S0954-1810(98)00011-9","volume":"13","author":"M Hussain","year":"1999","unstructured":"Hussain M (1999) Review of the applications of neural networks in chemical process control\u2014simulation and online implementation. Artif Intell Eng 13(1):55\u201368","journal-title":"Artif Intell Eng"},{"issue":"3","key":"3911_CR25","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1109\/37.55120","volume":"10","author":"NV Bhat","year":"1990","unstructured":"Bhat NV et al (1990) Modeling chemical process systems via neural computation. IEEE Control Syst Mag 10(3):24\u201330","journal-title":"IEEE Control Syst Mag"},{"key":"3911_CR26","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-642-57760-4","volume-title":"Neural networks for control","author":"WT Miller","year":"1995","unstructured":"Miller WT, Werbos PJ, Sutton RS (1995) Neural networks for control. MIT Press, Cambridge"},{"issue":"2","key":"3911_CR27","doi-asserted-by":"crossref","first-page":"242","DOI":"10.1109\/72.80237","volume":"1","author":"PJ Antsaklis","year":"1990","unstructured":"Antsaklis PJ (1990) Neural networks for control systems. IEEE Trans Neural Netw 1(2):242\u2013244","journal-title":"IEEE Trans Neural Netw"},{"issue":"1","key":"3911_CR28","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/0967-0661(94)90577-0","volume":"2","author":"HN Koivo","year":"1994","unstructured":"Koivo HN (1994) Artificial neural networks in fault diagnosis and control. Control Eng Pract 2(1):89\u2013101","journal-title":"Control Eng Pract"},{"issue":"1","key":"3911_CR29","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1109\/72.80202","volume":"1","author":"KS Narendra","year":"1990","unstructured":"Narendra KS, Parthasarathy K (1990) Identification and control of dynamical systems using neural networks. IEEE Trans Neural Netw 1(1):4\u201327","journal-title":"IEEE Trans Neural Netw"},{"key":"3911_CR30","volume-title":"Nonlinear system identification: from classical approaches to neural networks and fuzzy models","author":"O Nelles","year":"2013","unstructured":"Nelles O (2013) Nonlinear system identification: from classical approaches to neural networks and fuzzy models. Springer, Berlin"},{"key":"3911_CR31","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-540-40046-2","volume-title":"New soft computing techniques for system modeling, pattern classification and image processing","author":"L Rutkowski","year":"2004","unstructured":"Rutkowski L, Rutkowski L (2004) New soft computing techniques for system modeling, pattern classification and image processing. Springer, Berlin"},{"key":"3911_CR32","unstructured":"Zhang J, Man K (1998) Time series prediction using RNN in multi-dimension embedding phase space. In: 1998 IEEE international conference on systems, man, and cybernetics. IEEE"},{"key":"3911_CR33","volume-title":"Neural networks: a comprehensive foundation","author":"S Haykin","year":"1994","unstructured":"Haykin S (1994) Neural networks: a comprehensive foundation. Prentice Hall PTR, Upper Saddle River"},{"key":"3911_CR34","volume-title":"Identification of nonlinear systems using neural networks and polynomial models: a block-oriented approach","author":"A Janczak","year":"2004","unstructured":"Janczak A (2004) Identification of nonlinear systems using neural networks and polynomial models: a block-oriented approach, vol 310. Springer, Berlin"},{"issue":"1","key":"3911_CR35","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1016\/j.jprocont.2004.04.001","volume":"15","author":"K Patan","year":"2005","unstructured":"Patan K, Parisini T (2005) Identification of neural dynamic models for fault detection and isolation: the case of a real sugar evaporation process. J Process Control 15(1):67\u201379","journal-title":"J Process Control"},{"issue":"1","key":"3911_CR36","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/S0952-1976(96)00072-3","volume":"10","author":"PM Frank","year":"1997","unstructured":"Frank PM, K\u00f6ppen-Seliger B (1997) New developments using AI in fault diagnosis. Eng Appl Artif Intell 10(1):3\u201314","journal-title":"Eng Appl Artif Intell"},{"issue":"2\u20133","key":"3911_CR37","doi-asserted-by":"crossref","first-page":"248","DOI":"10.3166\/ejc.7.248-286","volume":"7","author":"J Calado","year":"2001","unstructured":"Calado J et al (2001) Soft computing approaches to fault diagnosis for dynamic systems. Eur J Control 7(2\u20133):248\u2013286","journal-title":"Eur J Control"},{"key":"3911_CR38","volume-title":"Fault diagnosis: models, artificial intelligence, applications","author":"J Korbicz","year":"2012","unstructured":"Korbicz J et al (2012) Fault diagnosis: models, artificial intelligence, applications. Springer, Berlin"},{"issue":"4","key":"3911_CR39","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1177\/014233129201400402","volume":"14","author":"J Zhang","year":"1992","unstructured":"Zhang J, Roberts PD (1992) On-line process fault diagnosis using neural network techniques. Trans Inst Meas Control 14(4):179\u2013188","journal-title":"Trans Inst Meas Control"},{"issue":"1","key":"3911_CR40","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/S0169-7439(97)00061-0","volume":"39","author":"D Svozil","year":"1997","unstructured":"Svozil D, Kvasnicka V, Pospichal J (1997) Introduction to multi-layer feed-forward neural networks. Chemometr Intell Lab Syst 39(1):43\u201362","journal-title":"Chemometr Intell Lab Syst"},{"key":"3911_CR41","doi-asserted-by":"crossref","unstructured":"Stinchcombe M, White H (1989) Universal approximation using feedforward networks with non-sigmoid hidden layer activation functions. In: International 1989 joint conference on neural networks, Washington DC, USA, vol 1, pp 613\u2013617","DOI":"10.1109\/IJCNN.1989.118640"},{"issue":"Supplement C","key":"3911_CR42","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/j.protcy.2013.12.159","volume":"11","author":"NM Nawi","year":"2013","unstructured":"Nawi NM, Atomi WH, Rehman MZ (2013) The effect of data pre-processing on optimized training of artificial neural networks. Procedia Technol 11(Supplement C):32\u201339","journal-title":"Procedia Technol"},{"key":"3911_CR43","doi-asserted-by":"crossref","first-page":"548","DOI":"10.1016\/j.petrol.2016.06.029","volume":"145","author":"M Al-Naser","year":"2016","unstructured":"Al-Naser M, Elshafei M, Al-sarkhi A (2016) Artificial neural network application for multiphase flow patterns detection: a new approach. J Petrol Sci Eng 145:548\u2013564","journal-title":"J Petrol Sci Eng"},{"key":"3911_CR44","volume-title":"Fault detection and diagnosis in engineering systems","author":"J Gertler","year":"1998","unstructured":"Gertler J (1998) Fault detection and diagnosis in engineering systems. Marcel Dekker, New York"},{"issue":"5","key":"3911_CR45","doi-asserted-by":"crossref","first-page":"709","DOI":"10.1016\/S0967-0661(97)00053-1","volume":"5","author":"R Isermann","year":"1997","unstructured":"Isermann R, Ball\u00e9 P (1997) Trends in the application of model-based fault detection and diagnosis of technical processes. Control Eng Pract 5(5):709\u2013719","journal-title":"Control Eng Pract"},{"issue":"3","key":"3911_CR46","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1016\/S0098-1354(02)00161-8","volume":"27","author":"V Venkatasubramanian","year":"2003","unstructured":"Venkatasubramanian V, Rengaswamy R, Kavuri SN (2003) A review of process fault detection and diagnosis: part II: qualitative models and search strategies. Comput Chem Eng 27(3):313\u2013326","journal-title":"Comput Chem Eng"},{"issue":"3","key":"3911_CR47","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1016\/S0098-1354(02)00162-X","volume":"27","author":"V Venkatasubramanian","year":"2003","unstructured":"Venkatasubramanian V et al (2003) A review of process fault detection and diagnosis: part III: process history based methods. Comput Chem Eng 27(3):327\u2013346","journal-title":"Comput Chem Eng"},{"issue":"3","key":"3911_CR48","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1016\/S0098-1354(02)00160-6","volume":"27","author":"V Venkatasubramanian","year":"2003","unstructured":"Venkatasubramanian V (2003) A review of process fault detection and diagnosis: part I: quantitative model-based methods. Comput Chem Eng 27(3):293\u2013311","journal-title":"Comput Chem Eng"},{"issue":"1","key":"3911_CR49","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/S0167-7012(00)00201-3","volume":"43","author":"IA Basheer","year":"2000","unstructured":"Basheer IA, Hajmeer M (2000) Artificial neural networks: fundamentals, computing, design, and application. J Microbiol Methods 43(1):3\u201331","journal-title":"J Microbiol Methods"},{"issue":"2","key":"3911_CR50","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1007\/BF01189880","volume":"12","author":"FJ \u015amieja","year":"1993","unstructured":"\u015amieja FJ (1993) Neural network constructive algorithms: trading generalization for learning efficiency? Circuits Syst Signal Process 12(2):331\u2013374","journal-title":"Circuits Syst Signal Process"},{"issue":"8","key":"3911_CR51","doi-asserted-by":"crossref","first-page":"826","DOI":"10.1016\/j.jclinepi.2009.11.020","volume":"63","author":"D Westreich","year":"2010","unstructured":"Westreich D, Lessler J, Funk MJ (2010) Propensity score estimation: neural networks, support vector machines, decision trees (CART), and meta-classifiers as alternatives to logistic regression. J Clin Epidemiol 63(8):826\u2013833","journal-title":"J Clin Epidemiol"},{"issue":"6","key":"3911_CR52","doi-asserted-by":"crossref","first-page":"627","DOI":"10.1080\/0003684042000217661","volume":"36","author":"D Sant\u00edn","year":"2004","unstructured":"Sant\u00edn D, Delgado FJ, Vali\u00f1o A (2004) The measurement of technical efficiency: a neural network approach. Appl Econ 36(6):627\u2013635","journal-title":"Appl Econ"},{"key":"3911_CR53","doi-asserted-by":"crossref","DOI":"10.4324\/9780203451519","volume-title":"An introduction to neural networks","author":"K Gurney","year":"1997","unstructured":"Gurney K (1997) An introduction to neural networks. UCL Press, London"},{"key":"3911_CR54","unstructured":"Demuth HB, Beale MH (2000) Neural network toolbox; for use with MATLAB; computation, visualization, programming; user's guide, version 4. Math Works"},{"key":"3911_CR55","first-page":"346","volume-title":"Neural smithing: supervised learning in feedforward artificial neural networks","author":"RD Reed","year":"1998","unstructured":"Reed RD, Marks RJ (1998) Neural smithing: supervised learning in feedforward artificial neural networks. MIT Press, Cambridge, p 346"},{"issue":"1","key":"3911_CR56","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1162\/neco.1997.9.1.205","volume":"9","author":"R Setiono","year":"1997","unstructured":"Setiono R (1997) Extracting rules from neural networks by pruning and hidden-unit splitting. Neural Comput 9(1):205\u2013225","journal-title":"Neural Comput"},{"key":"3911_CR57","first-page":"46","volume":"6","author":"G Garson","year":"1991","unstructured":"Garson G (1991) Interpreting neural-network connections. AI Expert 6:46\u201351","journal-title":"AI Expert"},{"key":"3911_CR58","volume-title":"Neural networks and learning machines","author":"SS Haykin","year":"2009","unstructured":"Haykin SS, Haykin SS (2009) Neural networks and learning machines, 3rd edn. Prentice Hall, New York","edition":"3"},{"issue":"2","key":"3911_CR59","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1109\/72.279181","volume":"5","author":"Y Bengio","year":"1994","unstructured":"Bengio Y, Simard P, Frasconi P (1994) Learning long-term dependencies with gradient descent is difficult. Trans Neural Netw 5(2):157\u2013166","journal-title":"Trans Neural Netw"},{"key":"3911_CR60","volume-title":"Neural network design","author":"M Hagan","year":"2014","unstructured":"Hagan M, Demuth H, Beale M, Jes\u00fas O (2014) Neural network design. University of Colorado, Boulder"},{"key":"3911_CR61","doi-asserted-by":"crossref","first-page":"545","DOI":"10.1007\/978-0-387-39940-9_133","volume-title":"Encyclopedia of database systems","author":"L Chen","year":"2009","unstructured":"Chen L (2009) Curse of dimensionality. In: Liu L, \u00d6Zsu MT (eds) Encyclopedia of database systems. Springer, Boston, pp 545\u2013546"},{"key":"3911_CR62","unstructured":"Smith LI (2002) A tutorial on Principal Components Analysis. Computer Science Technical Report No. OUCS-2002-12. http:\/\/hdl.handle.net\/10523\/7534 . Accessed 2 Feb 2018"},{"key":"3911_CR63","unstructured":"Merry RJE (2005) Wavelet theory and applications: a literature study, p 41. https:\/\/pure.tue.nl\/ws\/files\/4376957\/612762.pdf . Accessed 20 Dec 2017"},{"key":"3911_CR64","first-page":"33","volume":"4","author":"JS Dolley Shukla","year":"2013","unstructured":"Dolley Shukla JS (2013) Wavelets: basic concepts. Int J Electr Electron Eng Telecommun 4:33","journal-title":"Int J Electr Electron Eng Telecommun"},{"issue":"7","key":"3911_CR65","doi-asserted-by":"crossref","first-page":"674","DOI":"10.1109\/34.192463","volume":"11","author":"SG Mallat","year":"1989","unstructured":"Mallat SG (1989) A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans Pattern Anal Mach Intell 11(7):674\u2013693","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"7","key":"3911_CR66","doi-asserted-by":"crossref","first-page":"841","DOI":"10.1016\/S0255-2701(03)00103-X","volume":"43","author":"R Sharma","year":"2004","unstructured":"Sharma R et al (2004) Neural network applications for detecting process faults in packed towers. Chem Eng Process 43(7):841\u2013847","journal-title":"Chem Eng Process"},{"issue":"5","key":"3911_CR67","doi-asserted-by":"crossref","first-page":"751","DOI":"10.1006\/mssp.1997.0090","volume":"11","author":"BA Paya","year":"1997","unstructured":"Paya BA, Esat II, Badi MNM (1997) Artificial neural network based fault diagnostics of rotating machinery using wavelet transforms as a preprocessor. Mech Syst Signal Process 11(5):751\u2013765","journal-title":"Mech Syst Signal Process"},{"key":"3911_CR68","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1080\/00051144.2017.1343328","volume":"58","author":"L Banjanovic-Mehmedovic","year":"2017","unstructured":"Banjanovic-Mehmedovic L et al (2017) Neural network based data-driven modelling of anomaly detection in thermal power plant. Automatika 58:69\u201379","journal-title":"Automatika"},{"issue":"9","key":"3911_CR69","doi-asserted-by":"crossref","first-page":"1281","DOI":"10.1016\/S0098-1354(02)00093-5","volume":"26","author":"M Misra","year":"2002","unstructured":"Misra M et al (2002) Multivariate process monitoring and fault diagnosis by multi-scale PCA. Comput Chem Eng 26(9):1281\u20131293","journal-title":"Comput Chem Eng"},{"issue":"Supplement C","key":"3911_CR70","doi-asserted-by":"crossref","first-page":"1271","DOI":"10.1016\/j.proeng.2011.08.235","volume":"15","author":"Z Feng","year":"2011","unstructured":"Feng Z, Xu T (2011) Comparison of SOM and PCA-SOM in fault diagnosis of ground-testing bed. Procedia Eng 15(Supplement C):1271\u20131276","journal-title":"Procedia Eng"},{"key":"3911_CR71","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1007\/978-3-642-28768-8_10","volume-title":"Condition monitoring of machinery in non-stationary operations: proceedings of the second international conference \u201ccondition monitoring of machinery in non-stationary operations\u201d CMMNO\u20192012","author":"R Ziani","year":"2012","unstructured":"Ziani R et al (2012) Bearing fault diagnosis using neural network and genetic algorithms with the trace criterion. In: Fakhfakh T et al (eds) Condition monitoring of machinery in non-stationary operations: proceedings of the second international conference \u201ccondition monitoring of machinery in non-stationary operations\u201d CMMNO\u20192012. Springer, Berlin, pp 89\u201396"},{"issue":"5","key":"3911_CR72","doi-asserted-by":"crossref","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(5):840\u2013845","journal-title":"Chem Eng Technol"},{"issue":"3","key":"3911_CR73","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1504\/IJCAT.2008.020953","volume":"32","author":"I Manssouri","year":"2008","unstructured":"Manssouri I, Chetouani Y, Kihel BE (2008) Using neural networks for fault detection in a distillation column. Int J Comput Appl Technol 32(3):181\u2013186","journal-title":"Int J Comput Appl Technol"},{"issue":"1","key":"3911_CR74","doi-asserted-by":"crossref","first-page":"334","DOI":"10.1186\/s40064-015-1080-x","volume":"4","author":"M Jamil","year":"2015","unstructured":"Jamil M, Sharma SK, Singh R (2015) Fault detection and classification in electrical power transmission system using artificial neural network. SpringerPlus 4(1):334","journal-title":"SpringerPlus"},{"key":"3911_CR75","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1016\/j.neucom.2012.02.014","volume":"91","author":"H Abbasi Nozari","year":"2012","unstructured":"Abbasi Nozari H et al (2012) Model-based robust fault detection and isolation of an industrial gas turbine prototype using soft computing techniques. Neurocomputing 91:29\u201347","journal-title":"Neurocomputing"},{"key":"3911_CR76","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-018-3658-z","author":"SA Taqvi","year":"2018","unstructured":"Taqvi SA, Tufa LD, Zabiri H et al (2018) Fault detection in distillation column using NARX neural network. Neural Comput Applic. https:\/\/doi.org\/10.1007\/s00521-018-3658-z","journal-title":"Neural Comput Applic"},{"issue":"8","key":"3911_CR77","doi-asserted-by":"crossref","first-page":"2157","DOI":"10.1007\/s00521-015-1990-0","volume":"27","author":"S Kiakojoori","year":"2016","unstructured":"Kiakojoori S, Khorasani K (2016) Dynamic neural networks for gas turbine engine degradation prediction, health monitoring and prognosis. Neural Comput Appl 27(8):2157\u20132192","journal-title":"Neural Comput Appl"},{"key":"3911_CR78","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1016\/j.arcontrol.2016.09.008","volume":"42","author":"K Tidriri","year":"2016","unstructured":"Tidriri K et al (2016) Bridging data-driven and model-based approaches for process fault diagnosis and health monitoring: a review of researches and future challenges. Annu Rev Control 42:63\u201381","journal-title":"Annu Rev Control"},{"key":"3911_CR79","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1007\/3-540-49430-8_2","volume-title":"Neural networks: tricks of the trade","author":"Y LeCun","year":"1998","unstructured":"LeCun Y et al (1998) Efficient BackProp. In: Orr GB, M\u00fcller K-R (eds) Neural networks: tricks of the trade. Springer, Berlin, pp 9\u201350"},{"issue":"7","key":"3911_CR80","doi-asserted-by":"crossref","first-page":"526","DOI":"10.1016\/j.ifacol.2016.07.396","volume":"49","author":"KD Starr","year":"2016","unstructured":"Starr KD, Petersen H, Bauer M (2016) Control loop performance monitoring\u2014ABB\u2019s experience over two decades. IFAC-PapersOnLine 49(7):526\u2013532","journal-title":"IFAC-PapersOnLine"},{"issue":"4","key":"3911_CR81","doi-asserted-by":"crossref","first-page":"310","DOI":"10.1007\/s00521-005-0467-y","volume":"14","author":"DM Kline","year":"2005","unstructured":"Kline DM, Berardi VL (2005) Revisiting squared-error and cross-entropy functions for training neural network classifiers. Neural Comput Appl 14(4):310\u2013318","journal-title":"Neural Comput Appl"},{"issue":"5","key":"3911_CR82","doi-asserted-by":"crossref","first-page":"641","DOI":"10.1016\/j.conengprac.2004.05.005","volume":"13","author":"MAA Shoukat Choudhury","year":"2005","unstructured":"Shoukat Choudhury MAA, Thornhill NF, Shah SL (2005) Modelling valve stiction. Control Eng Pract 13(5):641\u2013658","journal-title":"Control Eng Pract"},{"issue":"1","key":"3911_CR83","doi-asserted-by":"publisher","first-page":"81","DOI":"10.3182\/20050703-6-CZ-1902.01589","volume":"38","author":"MAAS Choudhury","year":"2005","unstructured":"Choudhury MAAS, Kariwala V, Shah SL, Douke H, Takada H, Thornhill NF (2005) A simple test to confirm control valve stiction. IFAC Proc 38(1):81\u201386. https:\/\/doi.org\/10.3182\/20050703-6-CZ-1902.01589","journal-title":"IFAC Proc"},{"key":"3911_CR84","doi-asserted-by":"crossref","DOI":"10.1007\/978-1-84882-775-2","volume-title":"Detection and diagnosis of stiction in control loops: state of the art and advanced methods","author":"M Jelali","year":"2010","unstructured":"Jelali M, Huang B (2010) Detection and diagnosis of stiction in control loops: state of the art and advanced methods. Springer, London"},{"key":"3911_CR85","first-page":"1191","volume-title":"Computer aided chemical engineering","author":"M Farenzena","year":"2009","unstructured":"Farenzena M, Trierweiler JO (2009) A novel technique to estimate valve stiction based on pattern recognition. In: de Brito Alves RM, do Nascimento CAO, Biscaia EC (eds) Computer aided chemical engineering. Elsevier, Amsterdam, pp 1191\u20131196"},{"key":"3911_CR86","doi-asserted-by":"crossref","unstructured":"Venceslau AR, Guedes LA, Silva DR (2012) Artificial neural network approach for detection and diagnosis of valve stiction. In: 2012 IEEE 17th conference on emerging technologies and factory automation (ETFA). IEEE","DOI":"10.1109\/ETFA.2012.6489768"},{"key":"3911_CR87","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1016\/j.cherd.2017.12.038","volume":"130","author":"R Bacci di Capaci","year":"2018","unstructured":"Bacci di Capaci R, Scali C (2018) Review and comparison of techniques of analysis of valve stiction: from modeling to smart diagnosis. Chem Eng Res Des 130:230\u2013265","journal-title":"Chem Eng Res Des"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-018-3911-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s00521-018-3911-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-018-3911-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,11,17]],"date-time":"2020-11-17T13:01:58Z","timestamp":1605618118000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s00521-018-3911-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,12,13]]},"references-count":87,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2020,1]]}},"alternative-id":["3911"],"URL":"https:\/\/doi.org\/10.1007\/s00521-018-3911-5","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,12,13]]},"assertion":[{"value":"15 February 2018","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 November 2018","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 December 2018","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}