{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,24]],"date-time":"2025-03-24T08:52:30Z","timestamp":1742806350370},"reference-count":64,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2022,7,1]],"date-time":"2022-07-01T00:00:00Z","timestamp":1656633600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2022,5,5]],"date-time":"2022-05-05T00:00:00Z","timestamp":1651708800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Computers and Electrical Engineering"],"published-print":{"date-parts":[[2022,7]]},"DOI":"10.1016\/j.compeleceng.2022.108065","type":"journal-article","created":{"date-parts":[[2022,5,24]],"date-time":"2022-05-24T12:37:09Z","timestamp":1653395829000},"page":"108065","update-policy":"http:\/\/dx.doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":18,"special_numbering":"C","title":["Two-step residual-error based approach for anomaly detection in engineering systems using variational autoencoders"],"prefix":"10.1016","volume":"101","author":[{"ORCID":"http:\/\/orcid.org\/0000-0002-3917-9242","authenticated-orcid":false,"given":"Ana","family":"Gonz\u00e1lez-Mu\u00f1iz","sequence":"first","affiliation":[]},{"given":"Ignacio","family":"D\u00edaz","sequence":"additional","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0001-5873-4494","authenticated-orcid":false,"given":"Abel A.","family":"Cuadrado","sequence":"additional","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0002-9339-3776","authenticated-orcid":false,"given":"Diego","family":"Garc\u00eda-P\u00e9rez","sequence":"additional","affiliation":[]},{"given":"Daniel","family":"P\u00e9rez","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.compeleceng.2022.108065_b1","series-title":"2016 15th IEEE international conference on machine learning and applications (ICMLA)","first-page":"195","article-title":"Toward an online anomaly intrusion detection system based on deep learning","author":"Alrawashdeh","year":"2016"},{"key":"10.1016\/j.compeleceng.2022.108065_b2","doi-asserted-by":"crossref","first-page":"278","DOI":"10.1016\/j.future.2015.01.001","article-title":"A survey of anomaly detection techniques in financial domain","volume":"55","author":"Ahmed","year":"2016","journal-title":"Future Gener Comput Syst"},{"key":"10.1016\/j.compeleceng.2022.108065_b3","series-title":"International conference on information processing in medical imaging","first-page":"146","article-title":"Unsupervised anomaly detection with generative adversarial networks to guide marker discovery","author":"Schlegl","year":"2017"},{"key":"10.1016\/j.compeleceng.2022.108065_b4","series-title":"An introduction to predictive maintenance","author":"Mobley","year":"2002"},{"issue":"3","key":"10.1016\/j.compeleceng.2022.108065_b5","doi-asserted-by":"crossref","DOI":"10.1145\/1541880.1541882","article-title":"Anomaly detection: A survey","volume":"41","author":"Chandola","year":"2009","journal-title":"ACM Comput Surv"},{"key":"10.1016\/j.compeleceng.2022.108065_b6","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1016\/j.sigpro.2013.12.026","article-title":"A review of novelty detection","volume":"99","author":"Pimentel","year":"2014","journal-title":"Signal Process"},{"key":"10.1016\/j.compeleceng.2022.108065_b7","doi-asserted-by":"crossref","first-page":"3585","DOI":"10.1109\/ACCESS.2018.2793265","article-title":"Digital twin and big data towards smart manufacturing and industry 4.0: 360 degree comparison","volume":"6","author":"Qi","year":"2018","journal-title":"IEEE Access"},{"issue":"1","key":"10.1016\/j.compeleceng.2022.108065_b8","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1016\/j.arcontrol.2004.12.002","article-title":"Model-based fault-detection and diagnosis\u2013status and applications","volume":"29","author":"Isermann","year":"2005","journal-title":"Annu Rev Control"},{"key":"10.1016\/j.compeleceng.2022.108065_b9","doi-asserted-by":"crossref","first-page":"935","DOI":"10.1016\/j.egypro.2017.05.213","article-title":"Model based fault detection and diagnosis of doubly fed induction generators\u2013a review","volume":"117","author":"Balasubramanian","year":"2017","journal-title":"Energy Procedia"},{"issue":"3","key":"10.1016\/j.compeleceng.2022.108065_b10","first-page":"244","article-title":"Robust fault detection for wind turbines using reference model-based approach","volume":"29","author":"Nazir","year":"2017","journal-title":"J King Saud Univ-Eng Sci"},{"key":"10.1016\/j.compeleceng.2022.108065_b11","series-title":"Deep learning for anomaly detection: A survey","author":"Chalapathy","year":"2019"},{"key":"10.1016\/j.compeleceng.2022.108065_b12","series-title":"International conference on knowledge-based and intelligent information and engineering systems","first-page":"1242","article-title":"Electric power system anomaly detection using neural networks","author":"Martinelli","year":"2004"},{"key":"10.1016\/j.compeleceng.2022.108065_b13","doi-asserted-by":"crossref","unstructured":"Sakurada M, Yairi T. Anomaly detection using autoencoders with nonlinear dimensionality reduction. In: Proceedings of the MLSDA 2014 2nd workshop on machine learning for sensory data analysis. 2014, p. 4\u201311.","DOI":"10.1145\/2689746.2689747"},{"issue":"5","key":"10.1016\/j.compeleceng.2022.108065_b14","doi-asserted-by":"crossref","first-page":"1308","DOI":"10.3390\/s18051308","article-title":"Residual error based anomaly detection using auto-encoder in smd machine sound","volume":"18","author":"Oh","year":"2018","journal-title":"Sensors"},{"key":"10.1016\/j.compeleceng.2022.108065_b15","series-title":"Auto-encoding variational bayes","author":"Kingma","year":"2013"},{"key":"10.1016\/j.compeleceng.2022.108065_b16","series-title":"A hierarchical latent vector model for learning long-term structure in music","author":"Roberts","year":"2018"},{"key":"10.1016\/j.compeleceng.2022.108065_b17","series-title":"Grammar variational autoencoder","author":"Kusner","year":"2017"},{"key":"10.1016\/j.compeleceng.2022.108065_b18","series-title":"Advances in neural information processing systems","first-page":"2352","article-title":"Variational autoencoder for deep learning of images, labels and captions","author":"Pu","year":"2016"},{"key":"10.1016\/j.compeleceng.2022.108065_b19","series-title":"2017 IEEE winter conference on applications of computer vision (WACV)","first-page":"1133","article-title":"Deep feature consistent variational autoencoder","author":"Hou","year":"2017"},{"issue":"1","key":"10.1016\/j.compeleceng.2022.108065_b20","article-title":"Variational autoencoder based anomaly detection using reconstruction probability","volume":"2","author":"An","year":"2015","journal-title":"Spec Lect IE"},{"key":"10.1016\/j.compeleceng.2022.108065_b21","doi-asserted-by":"crossref","unstructured":"Xu H, Chen W, Zhao N, Li Z, Bu J, Li Z, Liu Y, Zhao Y, Pei D, Feng Y et al. Unsupervised anomaly detection via variational auto-encoder for seasonal kpis in web applications. In: Proceedings of the 2018 world wide web conference. 2018, p. 187\u201396.","DOI":"10.1145\/3178876.3185996"},{"key":"10.1016\/j.compeleceng.2022.108065_b22","doi-asserted-by":"crossref","DOI":"10.1016\/j.cviu.2020.102920","article-title":"Video anomaly detection and localization via Gaussian mixture fully convolutional variational autoencoder","author":"Fan","year":"2020","journal-title":"Comput Vis Image Underst"},{"key":"10.1016\/j.compeleceng.2022.108065_b23","series-title":"2019 IEEE international conference on image processing (ICIP)","first-page":"3372","article-title":"Recognizing fall actions from videos using reconstruction error of variational autoencoder","author":"Zhou","year":"2019"},{"key":"10.1016\/j.compeleceng.2022.108065_b24","series-title":"2020 IEEE 44th annual computers, software, and applications conference (COMPSAC)","first-page":"334","article-title":"RVAE-ABFA: Robust anomaly detection for highdimensional data using variational autoencoder","author":"Gao","year":"2020"},{"issue":"8","key":"10.1016\/j.compeleceng.2022.108065_b25","doi-asserted-by":"crossref","first-page":"115","DOI":"10.3390\/aerospace7080115","article-title":"Unsupervised anomaly detection in flight data using convolutional variational auto-encoder","volume":"7","author":"Memarzadeh","year":"2020","journal-title":"Aerospace"},{"key":"10.1016\/j.compeleceng.2022.108065_b26","series-title":"2019 IEEE 17th international conference on industrial informatics (INDIN), Vol. 1","first-page":"214","article-title":"Comparison of semi-supervised deep neural networks for anomaly detection in industrial processes","author":"Chadha","year":"2019"},{"key":"10.1016\/j.compeleceng.2022.108065_b27","series-title":"ICASSP 2020-2020 IEEE international conference on acoustics, speech and signal processing (ICASSP)","first-page":"4322","article-title":"Anomaly detection for time series using VAE-LSTM hybrid model","author":"Lin","year":"2020"},{"key":"10.1016\/j.compeleceng.2022.108065_b28","series-title":"Proceedings of the 2002 international joint conference on neural networks. IJCNN\u201902 (Cat. No. 02CH37290), Vol. 3","first-page":"2070","article-title":"Residual generation and visualization for understanding novel process conditions","author":"Diaz","year":"2002"},{"key":"10.1016\/j.compeleceng.2022.108065_b29","series-title":"Towards a rigorous science of interpretable machine learning","author":"Doshi-Velez","year":"2017"},{"key":"10.1016\/j.compeleceng.2022.108065_b30","series-title":"An overview of gradient descent optimization algorithms","author":"Ruder","year":"2016"},{"issue":"3","key":"10.1016\/j.compeleceng.2022.108065_b31","first-page":"1","article-title":"Learning representations by back-propagating errors","volume":"5","author":"Rumelhart","year":"1988","journal-title":"Cogn Model"},{"issue":"4\u20135","key":"10.1016\/j.compeleceng.2022.108065_b32","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1007\/BF00332918","article-title":"Auto-association by multilayer perceptrons and singular value decomposition","volume":"59","author":"Bourlard","year":"1988","journal-title":"Biol Cybernet"},{"key":"10.1016\/j.compeleceng.2022.108065_b33","series-title":"Advances in neural information processing systems","first-page":"3","article-title":"Autoencoders, minimum description length and Helmholtz free energy","author":"Hinton","year":"1994"},{"issue":"7553","key":"10.1016\/j.compeleceng.2022.108065_b34","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"issue":"8","key":"10.1016\/j.compeleceng.2022.108065_b35","doi-asserted-by":"crossref","first-page":"1798","DOI":"10.1109\/TPAMI.2013.50","article-title":"Representation learning: A review and new perspectives","volume":"35","author":"Bengio","year":"2013","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"10","key":"10.1016\/j.compeleceng.2022.108065_b36","doi-asserted-by":"crossref","first-page":"2108","DOI":"10.1109\/TIFS.2015.2446438","article-title":"Single sample face recognition via learning deep supervised autoencoders","volume":"10","author":"Gao","year":"2015","journal-title":"IEEE Trans Inf Forensics Secur"},{"key":"10.1016\/j.compeleceng.2022.108065_b37","doi-asserted-by":"crossref","unstructured":"Tewari A, Zollhofer M, Kim H, Garrido P, Bernard F, Perez P, Theobalt C. Mofa: Model-based deep convolutional face autoencoder for unsupervised monocular reconstruction. In: Proceedings of the IEEE international conference on computer vision. 2017, p. 1274\u201383.","DOI":"10.1109\/ICCV.2017.401"},{"issue":"2","key":"10.1016\/j.compeleceng.2022.108065_b38","doi-asserted-by":"crossref","first-page":"778","DOI":"10.1109\/TIP.2017.2771408","article-title":"Robust lstm-autoencoders for face de-occlusion in the wild","volume":"27","author":"Zhao","year":"2017","journal-title":"IEEE Trans Image Process"},{"key":"10.1016\/j.compeleceng.2022.108065_b39","series-title":"Asia-Pacific conference on simulated evolution and learning","first-page":"311","article-title":"Anomaly detection using replicator neural networks trained on examples of one class","author":"Dau","year":"2014"},{"key":"10.1016\/j.compeleceng.2022.108065_b40","series-title":"Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining","first-page":"665","article-title":"Anomaly detection with robust deep autoencoders","author":"Zhou","year":"2017"},{"key":"10.1016\/j.compeleceng.2022.108065_b41","series-title":"2019 international conference on software, telecommunications and computer networks (SoftCOM)","first-page":"1","article-title":"Anomaly-based intrusion detection using auto-encoder","author":"Nguimbous","year":"2019"},{"key":"10.1016\/j.compeleceng.2022.108065_b42","series-title":"2019 IEEE European symposium on security and privacy workshops (EuroS&PW)","first-page":"281","article-title":"Exploiting the auto-encoder residual error for intrusion detection","author":"Andresini","year":"2019"},{"key":"10.1016\/j.compeleceng.2022.108065_b43","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1016\/j.solener.2018.12.045","article-title":"An unsupervised monitoring procedure for detecting anomalies in photovoltaic systems using a one-class Support Vector Machine","volume":"179","author":"Harrou","year":"2019","journal-title":"Sol Energy"},{"key":"10.1016\/j.compeleceng.2022.108065_b44","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1016\/j.jmsy.2020.10.013","article-title":"Fault detection based on one-class deep learning for manufacturing applications limited to an imbalanced database","volume":"57","author":"Lee","year":"2020","journal-title":"J Manuf Syst"},{"key":"10.1016\/j.compeleceng.2022.108065_b45","series-title":"Sampling generative networks","author":"White","year":"2016"},{"key":"10.1016\/j.compeleceng.2022.108065_b46","series-title":"Generating sentences from a continuous space","author":"Bowman","year":"2015"},{"issue":"2","key":"10.1016\/j.compeleceng.2022.108065_b47","doi-asserted-by":"crossref","first-page":"268","DOI":"10.1021\/acscentsci.7b00572","article-title":"Automatic chemical design using a data-driven continuous representation of molecules","volume":"4","author":"G\u00f3mez-Bombarelli","year":"2018","journal-title":"ACS Cent Sci"},{"key":"10.1016\/j.compeleceng.2022.108065_b48","doi-asserted-by":"crossref","DOI":"10.1016\/j.cose.2021.102213","article-title":"ADS-B anomaly data detection model based on VAE-SVDD","volume":"104","author":"Luo","year":"2021","journal-title":"Comput Secur"},{"key":"10.1016\/j.compeleceng.2022.108065_b49","doi-asserted-by":"crossref","DOI":"10.1016\/j.ymssp.2017.11.024","article-title":"A review on the application of deep learning in system health management","author":"Khan","year":"2018","journal-title":"Mech Syst Signal Process"},{"issue":"2","key":"10.1016\/j.compeleceng.2022.108065_b50","first-page":"26","article-title":"Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude","volume":"4","author":"Tieleman","year":"2012","journal-title":"COURSERA: Neural Netw Mach Learn"},{"issue":"6789","key":"10.1016\/j.compeleceng.2022.108065_b51","doi-asserted-by":"crossref","first-page":"947","DOI":"10.1038\/35016072","article-title":"Digital selection and analogue amplification coexist in a cortex-inspired silicon circuit","volume":"405","author":"Hahnloser","year":"2000","journal-title":"Nature"},{"key":"10.1016\/j.compeleceng.2022.108065_b52","series-title":"2013 IEEE international conference on acoustics, speech and signal processing","first-page":"3517","article-title":"On rectified linear units for speech processing","author":"Zeiler","year":"2013"},{"key":"10.1016\/j.compeleceng.2022.108065_b53","series-title":"2018 Chinese control and decision conference (CCDC)","first-page":"1836","article-title":"Activation functions and their characteristics in deep neural networks","author":"Ding","year":"2018"},{"key":"10.1016\/j.compeleceng.2022.108065_b54","series-title":"Dying relu and initialization: Theory and numerical examples","author":"Lu","year":"2019"},{"key":"10.1016\/j.compeleceng.2022.108065_b55","series-title":"Empirical evaluation of rectified activations in convolutional network","author":"Xu","year":"2015"},{"key":"10.1016\/j.compeleceng.2022.108065_b56","series-title":"Activation functions: Comparison of trends in practice and research for deep learning","author":"Nwankpa","year":"2018"},{"key":"10.1016\/j.compeleceng.2022.108065_b57","series-title":"Adam: A method for stochastic optimization","author":"Kingma","year":"2014"},{"key":"10.1016\/j.compeleceng.2022.108065_b58","unstructured":"Daniluk\u00a0P, Go\u017adziewski\u00a0M, Kapka\u00a0S, Ko\u015bmider\u00a0M. Ensemble of auto-encoder based and wavenet like systems for unsupervised anomaly detection. In: Challenge on detection and classification of acoustic scenes and events (DCASE 2020 Challenge). Tech. Rep, 2020."},{"key":"10.1016\/j.compeleceng.2022.108065_b59","series-title":"Structural health monitoring based on data science techniques","first-page":"27","article-title":"Bayesian deep learning for vibration-based bridge damage detection","author":"\u00c1sgr\u00edmsson","year":"2022"},{"key":"10.1016\/j.compeleceng.2022.108065_b60","series-title":"Dataicann: datos de vibraci\u00f3n y corriente de un motor de inducci\u00f3n","author":"D\u00edaz\u00a0Blanco","year":"2019"},{"key":"10.1016\/j.compeleceng.2022.108065_b61","series-title":"Deep learning: A practitioner\u2019s approach","author":"Patterson","year":"2017"},{"key":"10.1016\/j.compeleceng.2022.108065_b62","series-title":"2015 IEEE international instrumentation and measurement technology conference (I2MTC) proceedings","first-page":"210","article-title":"Condition monitoring of a complex hydraulic system using multivariate statistics","author":"Helwig","year":"2015"},{"key":"10.1016\/j.compeleceng.2022.108065_b63","series-title":"International workshop on ambient assisted living","first-page":"91","article-title":"mHealthDroid: a novel framework for agile development of mobile health applications","author":"Banos","year":"2014"},{"issue":"Nov","key":"10.1016\/j.compeleceng.2022.108065_b64","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","author":"Maaten","year":"2008","journal-title":"J Mach Learn Res"}],"container-title":["Computers and Electrical Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0045790622003226?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0045790622003226?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2022,10,25]],"date-time":"2022-10-25T18:01:46Z","timestamp":1666720906000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0045790622003226"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7]]},"references-count":64,"alternative-id":["S0045790622003226"],"URL":"https:\/\/doi.org\/10.1016\/j.compeleceng.2022.108065","relation":{},"ISSN":["0045-7906"],"issn-type":[{"value":"0045-7906","type":"print"}],"subject":[],"published":{"date-parts":[[2022,7]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Two-step residual-error based approach for anomaly detection in engineering systems using variational autoencoders","name":"articletitle","label":"Article Title"},{"value":"Computers and Electrical Engineering","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.compeleceng.2022.108065","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2022 The Author(s). Published by Elsevier Ltd.","name":"copyright","label":"Copyright"}],"article-number":"108065"}}