{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,21]],"date-time":"2026-04-21T11:30:00Z","timestamp":1776771000108,"version":"3.51.2"},"reference-count":52,"publisher":"Springer Science and Business Media LLC","issue":"11","license":[{"start":{"date-parts":[[2022,3,7]],"date-time":"2022-03-07T00:00:00Z","timestamp":1646611200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,3,7]],"date-time":"2022-03-07T00:00:00Z","timestamp":1646611200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key R & D Program of China","doi-asserted-by":"crossref","award":["2017YFF0108800"],"award-info":[{"award-number":["2017YFF0108800"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61973071"],"award-info":[{"award-number":["61973071"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61627809"],"award-info":[{"award-number":["61627809"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Liaoning Natural Science Foundation of China","award":["2019-KF- 03-04"],"award-info":[{"award-number":["2019-KF- 03-04"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2022,6]]},"DOI":"10.1007\/s00521-022-07101-y","type":"journal-article","created":{"date-parts":[[2022,3,7]],"date-time":"2022-03-07T19:06:43Z","timestamp":1646680003000},"page":"8465-8477","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["Anomaly detection of industrial multi-sensor signals based on enhanced spatiotemporal features"],"prefix":"10.1007","volume":"34","author":[{"given":"Lin","family":"Jiang","sequence":"first","affiliation":[]},{"given":"Hang","family":"Xu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1256-1337","authenticated-orcid":false,"given":"Jinhai","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Xiangkai","family":"Shen","sequence":"additional","affiliation":[]},{"given":"Senxiang","family":"Lu","sequence":"additional","affiliation":[]},{"given":"Zhan","family":"Shi","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,3,7]]},"reference":[{"issue":"2","key":"7101_CR1","doi-asserted-by":"crossref","first-page":"330","DOI":"10.1109\/JAS.2019.1911804","volume":"7","author":"K Zhong","year":"2020","unstructured":"Zhong K, Han M, Han B (2020) Data-driven based fault prognosis for industrial systems: a concise overview. IEEE\/CAA J Autom Sin 7(2):330","journal-title":"IEEE\/CAA J Autom Sin"},{"issue":"3","key":"7101_CR2","doi-asserted-by":"crossref","first-page":"683","DOI":"10.1109\/JAS.2020.1003132","volume":"7","author":"S Barra","year":"2020","unstructured":"Barra S, Carta SM, Corriga A, Podda AS, Recupero DR (2020) Deep learning and time series-to-image encoding for financial forecasting. IEEE\/CAA J Autom Sin 7(3):683","journal-title":"IEEE\/CAA J Autom Sin"},{"issue":"7","key":"7101_CR3","doi-asserted-by":"crossref","first-page":"3877","DOI":"10.1109\/TII.2018.2885365","volume":"15","author":"J Liu","year":"2018","unstructured":"Liu J, Qu F, Hong X, Zhang H (2018) A small-sample wind turbine fault detection method with synthetic fault data using generative adversarial nets. IEEE Trans Ind Inform 15(7):3877\u20133888","journal-title":"IEEE Trans Ind Inform"},{"issue":"11","key":"7101_CR4","doi-asserted-by":"crossref","first-page":"3056","DOI":"10.1109\/TCYB.2017.2755864","volume":"48","author":"J Han","year":"2018","unstructured":"Han J, Zhang H, Wang Y, Sun X (2018) Robust fault detection for switched fuzzy systems with unknown input. IEEE Trans Cybern 48(11):3056","journal-title":"IEEE Trans Cybern"},{"issue":"7","key":"7101_CR5","doi-asserted-by":"crossref","first-page":"1883","DOI":"10.1109\/TIM.2017.2673024","volume":"66","author":"J Feng","year":"2017","unstructured":"Feng J, Li F, Lu S, Liu J, Ma D (2017) Injurious or noninjurious defect identification from mfl images in pipeline inspection using convolutional neural network. IEEE Trans Instrum Meas 66(7):1883","journal-title":"IEEE Trans Instrum Meas"},{"issue":"3","key":"7101_CR6","doi-asserted-by":"crossref","first-page":"790","DOI":"10.1109\/JAS.2020.1003099","volume":"7","author":"H Zhang","year":"2020","unstructured":"Zhang H, Li Y, Lv Z, Sangaiah AK, Huang T (2020) A real-time and ubiquitous network attack detection based on deep belief network and support vector machine. IEEE\/CAA J Autom Sin 7(3):790","journal-title":"IEEE\/CAA J Autom Sin"},{"key":"7101_CR7","doi-asserted-by":"publisher","first-page":"457","DOI":"10.1016\/j.energy.2019.04.016","volume":"176","author":"FH Jufri","year":"2019","unstructured":"Jufri FH, Oh S, Jung J (2019) Development of Photovoltaic abnormal condition detection system using combined regression and Support Vector. Energy 176:457. https:\/\/doi.org\/10.1016\/j.energy.2019.04.016","journal-title":"Energy"},{"key":"7101_CR8","unstructured":"Zhang H, Jiang L, Liu J, Qu F (2020) IEEE Transactions on Automation Science and Engineering, IEEE Transactions on Automation Science and Engineering pp 1\u201310"},{"key":"7101_CR9","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1016\/j.cose.2019.03.009","volume":"84","author":"Z Song","year":"2019","unstructured":"Song Z, Liu Z (2019) Abnormal detection method of industrial control system based on behavior model. Comput Secur 84:166","journal-title":"Comput Secur"},{"issue":"2","key":"7101_CR10","doi-asserted-by":"crossref","first-page":"601","DOI":"10.1109\/TNNLS.2018.2846646","volume":"30","author":"S Gao","year":"2019","unstructured":"Gao S, Zhou M, Wang Y, Cheng J, Yachi H, Wang J (2019) IEEE transactions on neural networks and learning systems. IEEE Trans Neural Netw Learn Syst 30(2):601","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"3","key":"7101_CR11","doi-asserted-by":"crossref","first-page":"599","DOI":"10.1007\/s00521-012-1263-0","volume":"24","author":"M Sheikhan","year":"2014","unstructured":"Sheikhan M, Jadidi Z (2014) Flow-based anomaly detection in high-speed links using modified GSA-optimized neural network. Neural Comput Appl 24(3):599","journal-title":"Neural Comput Appl"},{"issue":"3","key":"7101_CR12","first-page":"1","volume":"31","author":"J Wang","year":"2018","unstructured":"Wang J, Xia L, Hu X, Xiao Y (2018) Abnormal event detection with semi-supervised sparse topic model. Neural Comput Appl 31(3):1","journal-title":"Neural Comput Appl"},{"issue":"6","key":"7101_CR13","doi-asserted-by":"crossref","first-page":"3091","DOI":"10.1109\/TGRS.2018.2790583","volume":"56","author":"Y Wang","year":"2018","unstructured":"Wang Y, Lee L, Xue B, Wang L, Song M, Yu C, Li S, Chang C (2018) A posteriori hyperspectral anomaly detection for unlabeled classification. IEEE Trans Geosci Remote Sens 56(6):3091","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"7101_CR14","first-page":"1","volume":"251","author":"J Liu","year":"2020","unstructured":"Liu J, Wang P, Jiang D, Nan J, Zhu W (2020) An integrated data-driven framework for surface water quality anomaly detection and early warning. J Clean Prod 251:1","journal-title":"J Clean Prod"},{"issue":"11","key":"7101_CR15","doi-asserted-by":"crossref","first-page":"3597","DOI":"10.1109\/TCYB.2016.2572609","volume":"47","author":"Y Yuan","year":"2017","unstructured":"Yuan Y, Feng Y, Lu X (2017) Statistical hypothesis detector for abnormal event detection in crowded scenes. IEEE Trans Cybern 47(11):3597","journal-title":"IEEE Trans Cybern"},{"issue":"5","key":"7101_CR16","doi-asserted-by":"crossref","first-page":"1269","DOI":"10.1007\/s00521-018-3814-5","volume":"32","author":"T Andrysiak","year":"2020","unstructured":"Andrysiak T (2020) Sparse representation and overcomplete dictionary learning for anomaly detection in electrocardiograms. Neural Comput Appl 32(5):1269","journal-title":"Neural Comput Appl"},{"key":"7101_CR17","doi-asserted-by":"publisher","first-page":"505","DOI":"10.1016\/j.asoc.2018.12.029","volume":"76","author":"B Wang","year":"2019","unstructured":"Wang B, Mao Z (2019) Outlier detection based on Gaussian process with application to industrial processes. Appl Soft Comput 76:505. https:\/\/doi.org\/10.1016\/j.asoc.2018.12.029","journal-title":"Appl Soft Comput"},{"key":"7101_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.gsf.2020.03.017","author":"S Zheng","year":"2020","unstructured":"Zheng S, Zhu Y, Li D, Cao Z, Deng Q, Phoon K (2020) Probabilistic outlier detection for sparse multivariate geotechnical site investigation data using Bayesian learning. Geosci Front. https:\/\/doi.org\/10.1016\/j.gsf.2020.03.017","journal-title":"Geosci Front"},{"key":"7101_CR19","doi-asserted-by":"crossref","unstructured":"Gong D, Liu L, Le V, Saha B, Mansour MR, Venkatesh S, Hengel AVD (2020) Memorizing Normality to Detect Anomaly: Memory-Augmented Deep Autoencoder for Unsupervised Anomaly Detection. In: 2019 IEEE\/CVF international conference on computer vision (ICCV) pp 1705\u20131714","DOI":"10.1109\/ICCV.2019.00179"},{"issue":"5","key":"7101_CR20","doi-asserted-by":"crossref","first-page":"1844","DOI":"10.1109\/TCYB.2019.2894283","volume":"50","author":"X Luo","year":"2020","unstructured":"Luo X, Zhou M, Li S, Hu L, Shang M (2020) Non-negativity constrained missing data estimation for high-dimensional and sparse matrices from industrial applications. IEEE Trans Cybern 50(5):1844","journal-title":"IEEE Trans Cybern"},{"issue":"1","key":"7101_CR21","first-page":"175","volume":"31","author":"J Cai","year":"2019","unstructured":"Cai J, Zhang X, Xie S (2019) Video crowd detection and abnormal behavior model detection based on machine learning method. Neural Comput Appl 31(1):175","journal-title":"Neural Comput Appl"},{"key":"7101_CR22","doi-asserted-by":"crossref","unstructured":"Fu M, Liu J, Zang D, Lu S (2020) Anomaly detection of complex MFL measurements using low-rank recovery in pipeline transportation inspection, IEEE Trans Instrum Meas, pp 1\u201312","DOI":"10.1109\/TIM.2020.2974543"},{"issue":"7","key":"7101_CR23","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2018.2845559","volume":"56","author":"F Li","year":"2018","unstructured":"Li F, Zhang X, Zhang L, Jiang D, Zhang Y (2018) Exploiting structured sparsity for hyperspectral anomaly detection. IEEE Trans Geoence Remote Sens 56(7):1","journal-title":"IEEE Trans Geoence Remote Sens"},{"key":"7101_CR24","doi-asserted-by":"crossref","first-page":"647","DOI":"10.1016\/j.renene.2020.04.148","volume":"157","author":"N Renstrom","year":"2020","unstructured":"Renstrom N, Bangalore P, Highcock E (2020) System-wide anomaly detection in wind turbines using deep autoencoders. Renew Energy 157:647","journal-title":"Renew Energy"},{"key":"7101_CR25","doi-asserted-by":"crossref","first-page":"825","DOI":"10.1016\/j.renene.2018.05.024","volume":"127","author":"H Zhao","year":"2018","unstructured":"Zhao H, Liu H, Hu W, Yan X (2018) Anomaly detection and fault analysis of wind turbine components based on deep learning network. Renew Energy 127:825","journal-title":"Renew Energy"},{"issue":"1","key":"7101_CR26","doi-asserted-by":"crossref","first-page":"320","DOI":"10.1109\/JAS.2017.7510781","volume":"5","author":"Z Cai","year":"2018","unstructured":"Cai Z, Zhu W (2018) Feature selection for multi-label classification using neighborhood preservation. IEEE\/CAA J Autom Sin 5(1):320","journal-title":"IEEE\/CAA J Autom Sin"},{"issue":"3","key":"7101_CR27","doi-asserted-by":"crossref","first-page":"703","DOI":"10.1109\/JAS.2019.1911447","volume":"6","author":"H Liu","year":"2019","unstructured":"Liu H, Zhou M, Liu Q (2019) An embedded feature selection method for imbalanced data classification. IEEE\/CAA J Autom Sin 6(3):703","journal-title":"IEEE\/CAA J Autom Sin"},{"issue":"1","key":"7101_CR28","doi-asserted-by":"crossref","first-page":"393","DOI":"10.1109\/TII.2019.2938527","volume":"16","author":"R Nawaratne","year":"2020","unstructured":"Nawaratne R, Alahakoon D, De Silva D, Yu X (2020) Spatiotemporal anomaly detection using deep learning for real-time video surveillance. IEEE Trans Industr Inf 16(1):393","journal-title":"IEEE Trans Industr Inf"},{"key":"7101_CR29","doi-asserted-by":"crossref","first-page":"2395","DOI":"10.1109\/TIP.2019.2948286","volume":"29","author":"S Lee","year":"2020","unstructured":"Lee S, Kim HG, Ro YM (2020) BMAN: bidirectional multi-scale aggregation networks for abnormal event detection. IEEE Trans Image Process 29:2395","journal-title":"IEEE Trans Image Process"},{"issue":"1","key":"7101_CR30","doi-asserted-by":"crossref","first-page":"246","DOI":"10.1109\/TMM.2018.2846411","volume":"21","author":"W Chu","year":"2019","unstructured":"Chu W, Xue H, Yao C, Cai D (2019) Sparse coding guided spatiotemporal feature learning for abnormal event detection in large videos. IEEE Trans Multimed 21(1):246","journal-title":"IEEE Trans Multimed"},{"issue":"4","key":"7101_CR31","doi-asserted-by":"crossref","first-page":"1878","DOI":"10.1109\/TIP.2017.2781299","volume":"27","author":"C Jia","year":"2018","unstructured":"Jia C, Shao M, Li S, Zhao H, Fu Y (2018) Stacked denoising tensor auto-encoder for action recognition with spatiotemporal corruptions. IEEE Trans Image Process 27(4):1878","journal-title":"IEEE Trans Image Process"},{"issue":"10","key":"7101_CR32","first-page":"5002","volume":"27","author":"L Trung-Nghia","year":"2017","unstructured":"Trung-Nghia L, Akihiro S (2017) Video salient object detection using spatiotemporal deep features. IEEE Trans Image Process 27(10):5002","journal-title":"IEEE Trans Image Process"},{"issue":"7","key":"7101_CR33","doi-asserted-by":"crossref","first-page":"3286","DOI":"10.1109\/TIP.2019.2895466","volume":"28","author":"JH Bappy","year":"2019","unstructured":"Bappy JH, Simons C, Nataraj L, Manjunath BS, Roy-Chowdhury AK (2019) Hybrid LSTM and encoder decoder architecture for detection of image forgeries. IEEE Trans Image Process 28(7):3286","journal-title":"IEEE Trans Image Process"},{"key":"7101_CR34","doi-asserted-by":"publisher","first-page":"297","DOI":"10.1016\/j.neucom.2018.08.067","volume":"318","author":"Y Tian","year":"2018","unstructured":"Tian Y, Zhang K, Li J, Lin X, Yang B (2018) LSTM-based traffic flow prediction with missing data. Neurocomputing 318:297. https:\/\/doi.org\/10.1016\/j.neucom.2018.08.067","journal-title":"Neurocomputing"},{"key":"7101_CR35","doi-asserted-by":"publisher","first-page":"216","DOI":"10.1016\/j.patrec.2019.06.030","volume":"130","author":"D Dotti","year":"2020","unstructured":"Dotti D, Popa M, Asteriadis S (2020) A hierarchical autoencoder learning model for path prediction and abnormality detection. Pattern Recognit Lett 130:216. https:\/\/doi.org\/10.1016\/j.patrec.2019.06.030","journal-title":"Pattern Recognit Lett"},{"issue":"2","key":"7101_CR36","doi-asserted-by":"crossref","first-page":"1796","DOI":"10.1109\/TSG.2019.2937043","volume":"11","author":"Y Lin","year":"2020","unstructured":"Lin Y, Wang J (2020) Probabilistic deep autoencoder for power system measurement outlier detection and reconstruction. IEEE Trans Smart Grid 11(2):1796","journal-title":"IEEE Trans Smart Grid"},{"issue":"2","key":"7101_CR37","doi-asserted-by":"crossref","first-page":"441","DOI":"10.1109\/JAS.2019.1911393","volume":"6","author":"E Principi","year":"2019","unstructured":"Principi E, Rossetti D, Squartini S, Piazza F (2019) Unsupervised electric motor fault detection by using deep autoencoders. IEEE\/CAA J Autom Sin 6(2):441","journal-title":"IEEE\/CAA J Autom Sin"},{"issue":"5","key":"7101_CR38","doi-asserted-by":"crossref","first-page":"1369","DOI":"10.1109\/TKDE.2014.2365790","volume":"27","author":"M Radovanovic","year":"2015","unstructured":"Radovanovic M, Nanopoulos A, Ivanovic M (2015) Reverse nearest neighbors in unsupervised distance-based outlier detection. IEEE Trans Knowl Data Eng 27(5):1369","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"7101_CR39","first-page":"1","volume":"80","author":"S Wang","year":"2019","unstructured":"Wang S, Wang H, Xiang S, Yu L (2019) Densely connected convolutional network block based autoencoder for panorama map compression. Signal Process: Image Commun 80:1","journal-title":"Signal Process: Image Commun"},{"key":"7101_CR40","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1016\/j.neucom.2019.07.065","volume":"365","author":"N Abiri","year":"2019","unstructured":"Abiri N, Linse B, Ed\u00e9n P, Ohlsson M (2019) Establishing strong imputation performance of a denoising autoencoder in a wide range of missing data problems. Neurocomputing 365:137","journal-title":"Neurocomputing"},{"key":"7101_CR41","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.neucom.2018.09.049","volume":"322","author":"J Wang","year":"2018","unstructured":"Wang J, Peng B, Zhang X (2018) Using a stacked residual LSTM model for sentiment intensity prediction. Neurocomputing 322:93","journal-title":"Neurocomputing"},{"key":"7101_CR42","doi-asserted-by":"crossref","unstructured":"Chong YS, Tay YH (2017) Abnormal event detection in videos using spatiotemporal autoencoder. In: international symposium on neural networks (Springer), pp 189\u2013196","DOI":"10.1007\/978-3-319-59081-3_23"},{"issue":"12","key":"7101_CR43","doi-asserted-by":"crossref","first-page":"2226","DOI":"10.1109\/TNN.2011.2168538","volume":"22","author":"H Zhang","year":"2011","unstructured":"Zhang H, Cui L, Zhang X, Luo Y (2011) Data-driven robust approximate optimal tracking control for unknown general nonlinear systems using adaptive dynamic programming method. IEEE Trans Neural Netw 22(12):2226","journal-title":"IEEE Trans Neural Netw"},{"issue":"2","key":"7101_CR44","doi-asserted-by":"crossref","first-page":"318","DOI":"10.1109\/TSMCB.2008.2004559","volume":"39","author":"H Xiong","year":"2009","unstructured":"Xiong H, Wu J, Chen J (2009) K-Means clustering versus validation measures: a data-distribution perspective. IEEE Trans Syst, Man, Cybern Part B (Cybernetics) 39(2):318","journal-title":"IEEE Trans Syst, Man, Cybern Part B (Cybernetics)"},{"issue":"8","key":"7101_CR45","doi-asserted-by":"crossref","first-page":"861","DOI":"10.1016\/j.patrec.2005.10.010","volume":"27","author":"T Fawcett","year":"2005","unstructured":"Fawcett T (2005) An introduction to ROC analysis. Pattern Recogn Lett 27(8):861","journal-title":"Pattern Recogn Lett"},{"issue":"9","key":"7101_CR46","doi-asserted-by":"crossref","first-page":"2200","DOI":"10.1109\/TIM.2018.2813839","volume":"67","author":"F Li","year":"2018","unstructured":"Li F, Feng J, Zhang H, Liu J, Lu S, Ma D (2018) Quick reconstruction of arbitrary pipeline defect profiles from MFL measurements employing modified harmony search algorithm. IEEE Trans Instrum Meas 67(9):2200","journal-title":"IEEE Trans Instrum Meas"},{"issue":"1","key":"7101_CR47","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1109\/TII.2018.2828811","volume":"15","author":"S Lu","year":"2019","unstructured":"Lu S, Feng J, Zhang H, Liu J, Wu Z (2019) An estimation method of defect size from MFL image using visual transformation convolutional neural network. IEEE Trans Industr Inf 15(1):213","journal-title":"IEEE Trans Industr Inf"},{"key":"7101_CR48","unstructured":"Fu M, Liu J, Zhang H, Lu S (2020) Multi-sensor fusion for magnetic flux leakage defect characterization under information incompletion. IEEE Trans Ind Electron, pp 1\u201311"},{"issue":"3","key":"7101_CR49","doi-asserted-by":"crossref","first-page":"520","DOI":"10.1109\/JAS.2017.7510562","volume":"4","author":"W Zhang","year":"2017","unstructured":"Zhang W, Zhang H, Liu J, Li K, Yang D, Tian H (2017) Weather prediction with multiclass support vector machines in the fault detection of photovoltaic system. IEEE\/CAA J Autom Sin 4(3):520","journal-title":"IEEE\/CAA J Autom Sin"},{"issue":"1","key":"7101_CR50","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1109\/TIM.2017.2755918","volume":"67","author":"J Liu","year":"2017","unstructured":"Liu J, Fu M, Liu F, Feng J, Cui K (2017) Window feature-based two-stage defect identification using magnetic flux leakage measurements. IEEE Trans Instrum Meas 67(1):12","journal-title":"IEEE Trans Instrum Meas"},{"key":"7101_CR51","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2020.3022432","author":"X Zhou","year":"2020","unstructured":"Zhou X, Hu Y, Liang W, Ma J, Jin Q (2020) Variational LSTM enhanced anomaly detection for industrial big data. IEEE Trans Ind Inform. https:\/\/doi.org\/10.1109\/TII.2020.3022432","journal-title":"IEEE Trans Ind Inform"},{"issue":"5","key":"7101_CR52","doi-asserted-by":"crossref","first-page":"2710","DOI":"10.1109\/TII.2019.2893125","volume":"15","author":"Z Chen","year":"2019","unstructured":"Chen Z, Cao Y, Ding SX, Zhang K, Koenings T, Peng T, Yang C, Gui W (2019) A distributed canonical correlation analysis-based fault detection method for plant-wide process monitoring. IEEE Trans Industr Inf 15(5):2710","journal-title":"IEEE Trans Industr Inf"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-07101-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-022-07101-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-07101-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,14]],"date-time":"2022-05-14T05:51:38Z","timestamp":1652507498000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-022-07101-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,7]]},"references-count":52,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2022,6]]}},"alternative-id":["7101"],"URL":"https:\/\/doi.org\/10.1007\/s00521-022-07101-y","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,3,7]]},"assertion":[{"value":"23 February 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 February 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 March 2022","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 conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}