{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T17:18:51Z","timestamp":1772126331598,"version":"3.50.1"},"reference-count":54,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Expert Systems with Applications"],"published-print":{"date-parts":[[2026,6]]},"DOI":"10.1016\/j.eswa.2026.131803","type":"journal-article","created":{"date-parts":[[2026,2,23]],"date-time":"2026-02-23T07:48:21Z","timestamp":1771832901000},"page":"131803","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Multivariate cyclostationary deep deconvolution (MCDD): An intelligent multivariate signal processing algorithm"],"prefix":"10.1016","volume":"315","author":[{"given":"Xiaolong","family":"Ruan","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9758-5292","authenticated-orcid":false,"given":"Rui","family":"Yuan","sequence":"additional","affiliation":[]},{"given":"Yong","family":"Lv","sequence":"additional","affiliation":[]},{"given":"Hewenxuan","family":"Li","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6477-2710","authenticated-orcid":false,"given":"Ersegun Deniz","family":"Gedikli","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5011-5370","authenticated-orcid":false,"given":"Yuejian","family":"Chen","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"issue":"2","key":"10.1016\/j.eswa.2026.131803_b0005","doi-asserted-by":"crossref","first-page":"282","DOI":"10.1016\/j.ymssp.2004.09.001","article-title":"The spectral kurtosis: A useful tool for characterising non-stationary signals","volume":"20","author":"Antoni","year":"2006","journal-title":"Mechanical Systems and Signal Processing"},{"issue":"4","key":"10.1016\/j.eswa.2026.131803_b0010","doi-asserted-by":"crossref","first-page":"987","DOI":"10.1016\/j.ymssp.2008.10.010","article-title":"Cyclostationarity by examples","volume":"23","author":"Antoni","year":"2009","journal-title":"Mechanical Systems and Signal Processing"},{"key":"10.1016\/j.eswa.2026.131803_b0015","doi-asserted-by":"crossref","first-page":"290","DOI":"10.1016\/j.ymssp.2018.05.012","article-title":"A statistical methodology for the design of condition indicators","volume":"114","author":"Antoni","year":"2019","journal-title":"Mechanical Systems and Signal Processing"},{"key":"10.1016\/j.eswa.2026.131803_b0020","doi-asserted-by":"crossref","first-page":"569","DOI":"10.1016\/j.jsv.2018.06.055","article-title":"Blind deconvolution based on cyclostationarity maximization and its application to fault identification","volume":"432","author":"Buzzoni","year":"2018","journal-title":"Journal of Sound and Vibration"},{"issue":"5","key":"10.1016\/j.eswa.2026.131803_b0025","doi-asserted-by":"crossref","first-page":"6451","DOI":"10.1109\/JSEN.2023.3348148","article-title":"Maximum spectral sparse entropy blind deconvolution for bearing fault diagnosis","volume":"24","author":"Cai","year":"2024","journal-title":"IEEE Sensors Journal"},{"key":"10.1016\/j.eswa.2026.131803_b0030","doi-asserted-by":"crossref","DOI":"10.1016\/j.measurement.2020.107804","article-title":"Blind deconvolution assisted with periodicity detection techniques and its application to bearing fault feature enhancement","volume":"159","author":"Chen","year":"2020","journal-title":"Measurement"},{"issue":"6","key":"10.1016\/j.eswa.2026.131803_b0035","doi-asserted-by":"crossref","first-page":"3637","DOI":"10.1177\/14759217231151585","article-title":"Squared envelope sparsification via blind deconvolution and its application to railway axle bearing diagnostics","volume":"22","author":"Chen","year":"2023","journal-title":"Structural Health Monitoring"},{"key":"10.1016\/j.eswa.2026.131803_b0040","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TVT.2007.905249","article-title":"Generalized Statistical Indicators-Guided Signal Blind Deconvolution for Fault Diagnosis of Railway Vehicle Axle-box Bearings","author":"Chen","year":"2024","journal-title":"IEEE Transactions on Vehicular Technology"},{"key":"10.1016\/j.eswa.2026.131803_b0045","doi-asserted-by":"crossref","DOI":"10.1016\/j.ymssp.2024.111777","article-title":"A novel weighted sparse classification framework with extended discriminative dictionary for data-driven bearing fault diagnosis","volume":"222","author":"Cui","year":"2025","journal-title":"Mechanical Systems and Signal Processing"},{"key":"10.1016\/j.eswa.2026.131803_b0050","doi-asserted-by":"crossref","first-page":"372","DOI":"10.1016\/j.ress.2019.01.006","article-title":"Gated recurrent unit based recurrent neural network for remaining useful life prediction of nonlinear deterioration process","volume":"185","author":"Chen","year":"2019","journal-title":"Reliability Engineering & System Safety"},{"key":"10.1016\/j.eswa.2026.131803_b0055","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2024.110596","article-title":"Explicit speed-integrated LSTM network for non-stationary gearbox vibration representation and fault detection under varying speed conditions","volume":"254","author":"Chen","year":"2025","journal-title":"Reliability Engineering & System Safety"},{"issue":"2","key":"10.1016\/j.eswa.2026.131803_b0060","doi-asserted-by":"crossref","first-page":"906","DOI":"10.1016\/j.ymssp.2006.02.005","article-title":"Enhancement of autoregressive model based gear tooth fault detection technique by the use of minimum entropy deconvolution filter","volume":"21","author":"Endo","year":"2007","journal-title":"Mechanical Systems and Signal Processing"},{"key":"10.1016\/j.eswa.2026.131803_b0065","doi-asserted-by":"crossref","DOI":"10.1016\/j.ymssp.2020.107258","article-title":"Use of cyclostationary properties of vibration signals to identify gear wear mechanisms and track wear evolution","volume":"150","author":"Feng","year":"2021","journal-title":"Mechanical Systems and Signal Processing"},{"key":"10.1016\/j.eswa.2026.131803_b0070","doi-asserted-by":"crossref","DOI":"10.1016\/j.sigpro.2021.107997","article-title":"Extracting cyclo-stationarity of repetitive transients from envelope spectrum based on prior-unknown blind deconvolution technique","volume":"183","author":"He","year":"2021","journal-title":"Signal Processing"},{"key":"10.1016\/j.eswa.2026.131803_b0075","doi-asserted-by":"crossref","DOI":"10.1016\/j.measurement.2020.108329","article-title":"Optimized minimum generalized Lp\/Lq deconvolution for recovering repetitive impacts from a vibration mixture","volume":"168","author":"He","year":"2021","journal-title":"Measurement"},{"key":"10.1016\/j.eswa.2026.131803_b0080","doi-asserted-by":"crossref","unstructured":"Helm D and Timusk M (2023) Wavelet Denoising Applied to Hardware Redundant Systems for Rolling Element Bearing Fault Detection. Journal of Dynamics, Monitoring and Diagnostics. Epub ahead of print 5 June 2023. DOI: 10.37965\/jdmd.2023.231.","DOI":"10.37965\/jdmd.2023.231"},{"key":"10.1016\/j.eswa.2026.131803_b0085","doi-asserted-by":"crossref","DOI":"10.1007\/s40857-021-00224-7","article-title":"A Comparison of Machine Health Indicators based on the Impulsiveness of Vibration Signals","author":"Hou","year":"2021","journal-title":"Acoustics Australia."},{"key":"10.1016\/j.eswa.2026.131803_b0090","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2025.111092","article-title":"Physics-informed deep learning for virtual rail train trajectory following control","volume":"261","author":"Ji","year":"2025","journal-title":"Reliability Engineering & System Safety"},{"key":"10.1016\/j.eswa.2026.131803_b0095","series-title":"Signal Processing. Epub ahead of print 2017","article-title":"A geometrical investigation on the generalized lp\/lq norm for blind deconvolution","author":"Jia","year":"2017"},{"key":"10.1016\/j.eswa.2026.131803_b0100","doi-asserted-by":"crossref","first-page":"433","DOI":"10.1016\/j.jsv.2016.10.005","article-title":"Investigation on the kurtosis filter and the derivation of convolutional sparse filter for impulsive signature enhancement","volume":"386","author":"Jia","year":"2017","journal-title":"Journal of Sound and Vibration"},{"key":"10.1016\/j.eswa.2026.131803_b0105","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1016\/j.ymssp.2017.09.018","article-title":"Sparse filtering with the generalized lp \/ lq norm and its applications to the condition monitoring of rotating machinery","volume":"102","author":"Jia","year":"2018","journal-title":"Mechanical Systems and Signal Processing"},{"key":"10.1016\/j.eswa.2026.131803_b0110","doi-asserted-by":"crossref","DOI":"10.1016\/j.ymssp.2025.112888","article-title":"Adaptive weighted data fusion-driven multi-layer discriminative dictionary learning method for intelligent fault diagnosis of rotating machinery","volume":"235","author":"Jiang","year":"2025","journal-title":"Mechanical Systems and Signal Processing"},{"key":"10.1016\/j.eswa.2026.131803_b0115","doi-asserted-by":"crossref","DOI":"10.1016\/j.ymssp.2020.107323","article-title":"Maximum average kurtosis deconvolution and its application for the impulsive fault feature enhancement of rotating machinery","volume":"149","author":"Liang","year":"2021","journal-title":"Mechanical Systems and Signal Processing"},{"key":"10.1016\/j.eswa.2026.131803_b0120","first-page":"1","article-title":"Multivariate phase Space Warping-based Degradation Tracking and remaining Useful Life Prediction of Rolling Bearings","author":"Liu","year":"2024","journal-title":"IEEE Transactions on Reliability"},{"key":"10.1016\/j.eswa.2026.131803_b0125","doi-asserted-by":"crossref","DOI":"10.1016\/j.ymssp.2021.108376","article-title":"Box-cox-sparse-measures-based blind filtering: Understanding the difference between the maximum kurtosis deconvolution and the minimum entropy deconvolution","volume":"165","author":"L\u00f3pez","year":"2022","journal-title":"Mechanical Systems and Signal Processing"},{"key":"10.1016\/j.eswa.2026.131803_b0130","first-page":"1","article-title":"Variable-Bandwidth Self-Convergent Variational Mode Decomposition and its Application to Fault Diagnosis of Rolling Bearing","volume":"73","author":"Lv","year":"2024","journal-title":"IEEE Transactions on Instrumentation and Measurement"},{"key":"10.1016\/j.eswa.2026.131803_b0135","doi-asserted-by":"crossref","first-page":"461","DOI":"10.1016\/j.ymssp.2016.05.036","article-title":"Multipoint Optimal Minimum Entropy Deconvolution and Convolution Fix: Application to vibration fault detection","volume":"82","author":"McDonald","year":"2017","journal-title":"Mechanical Systems and Signal Processing"},{"key":"10.1016\/j.eswa.2026.131803_b0140","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1016\/j.ymssp.2012.06.010","article-title":"Maximum correlated Kurtosis deconvolution and application on gear tooth chip fault detection","volume":"33","author":"McDonald","year":"2012","journal-title":"Mechanical Systems and Signal Processing"},{"key":"10.1016\/j.eswa.2026.131803_b0145","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2025.128735","article-title":"Spectral feature-informed difference multi-modes decomposition for compound bearing fault diagnosis","volume":"294","author":"Meng","year":"2025","journal-title":"Expert Systems with Applications"},{"issue":"10","key":"10.1016\/j.eswa.2026.131803_b0150","doi-asserted-by":"crossref","DOI":"10.1088\/0957-0233\/27\/10\/105004","article-title":"Sparse maximum harmonics-to-noise-ratio deconvolution for weak fault signature detection in bearings","volume":"27","author":"Miao","year":"2016","journal-title":"Measurement Science and Technology"},{"key":"10.1016\/j.eswa.2026.131803_b0155","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1016\/j.ymssp.2017.01.033","article-title":"Application of an improved maximum correlated kurtosis deconvolution method for fault diagnosis of rolling element bearings","volume":"92","author":"Miao","year":"2017","journal-title":"Mechanical Systems and Signal Processing"},{"key":"10.1016\/j.eswa.2026.131803_b0160","doi-asserted-by":"crossref","first-page":"455","DOI":"10.1016\/j.isatra.2020.12.058","article-title":"Period-oriented multi-hierarchy deconvolution and its application for bearing fault diagnosis","volume":"114","author":"Miao","year":"2021","journal-title":"ISA Transactions"},{"key":"10.1016\/j.eswa.2026.131803_b0165","doi-asserted-by":"crossref","DOI":"10.1016\/j.ymssp.2023.110110","article-title":"Deep network-based maximum correlated kurtosis deconvolution: A novel deep deconvolution for bearing fault diagnosis","volume":"189","author":"Miao","year":"2023","journal-title":"Mechanical Systems and Signal Processing"},{"issue":"2","key":"10.1016\/j.eswa.2026.131803_b0170","doi-asserted-by":"crossref","first-page":"1949","DOI":"10.1109\/TIE.2022.3156156","article-title":"Feature Mode Decomposition: New Decomposition Theory for Rotating Machinery Fault Diagnosis","volume":"70","author":"Miao","year":"2023","journal-title":"IEEE Transactions on Industrial Electronics"},{"key":"10.1016\/j.eswa.2026.131803_b0175","unstructured":"Ngiam J, Koh PW, Chen Z, et al. (2011) Sparse filtering. In: Proceedings of the 24th International Conference on Neural Information Processing Systems, Red Hook, NY, USA, 12 December 2011, pp. 1125\u20131133. NIPS\u201911. Curran Associates Inc."},{"key":"10.1016\/j.eswa.2026.131803_b0180","doi-asserted-by":"crossref","DOI":"10.1016\/j.ymssp.2019.106556","article-title":"Blind filters based on envelope spectrum sparsity indicators for bearing and gear vibration-based condition monitoring","volume":"138","author":"Peeters","year":"2020","journal-title":"Mechanical Systems and Signal Processing"},{"key":"10.1016\/j.eswa.2026.131803_b0185","doi-asserted-by":"crossref","DOI":"10.1016\/j.ymssp.2023.110351","article-title":"Use of generalized Gaussian cyclostationarity for blind deconvolution and its application to bearing fault diagnosis under non-Gaussian conditions","volume":"196","author":"Peng","year":"2023","journal-title":"Mechanical Systems and Signal Processing"},{"issue":"3","key":"10.1016\/j.eswa.2026.131803_b0190","doi-asserted-by":"crossref","first-page":"574","DOI":"10.1016\/j.ymssp.2007.09.011","article-title":"Indicators of cyclostationarity: Theory and application to gear fault monitoring","volume":"22","author":"Raad","year":"2008","journal-title":"Mechanical Systems and Signal Processing"},{"issue":"2","key":"10.1016\/j.eswa.2026.131803_b0195","doi-asserted-by":"crossref","first-page":"485","DOI":"10.1016\/j.ymssp.2010.07.017","article-title":"Rolling element bearing diagnostics\u2014A tutorial","volume":"25","author":"Randall","year":"2011","journal-title":"Mechanical Systems and Signal Processing"},{"issue":"9","key":"10.1016\/j.eswa.2026.131803_b0200","doi-asserted-by":"crossref","DOI":"10.1088\/1361-6501\/ad4fb2","article-title":"Iterative feature mode decomposition: A novel adaptive denoising method for mechanical fault diagnosis","volume":"35","author":"Ruan","year":"2024","journal-title":"Measurement Science and Technology"},{"key":"10.1016\/j.eswa.2026.131803_b0205","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2024.108102","article-title":"A novel bearing intelligent fault diagnosis method based on spectrum sparse deep deconvolution","volume":"133","author":"Shi","year":"2024","journal-title":"Engineering Applications of Artificial Intelligence"},{"key":"10.1016\/j.eswa.2026.131803_b0210","doi-asserted-by":"crossref","DOI":"10.1016\/j.ymssp.2019.106330","article-title":"Deep separable convolutional network for remaining useful life prediction of machinery","volume":"134","author":"Wang","year":"2019","journal-title":"Mechanical Systems and Signal Processing"},{"key":"10.1016\/j.eswa.2026.131803_b0215","doi-asserted-by":"crossref","first-page":"360","DOI":"10.1016\/j.ymssp.2018.02.034","article-title":"Some further thoughts about spectral kurtosis, spectral L2\/L1 norm, spectral smoothness index and spectral Gini index for characterizing repetitive transients","volume":"108","author":"Wang","year":"2018","journal-title":"Mechanical Systems and Signal Processing"},{"key":"10.1016\/j.eswa.2026.131803_b0220","doi-asserted-by":"crossref","DOI":"10.1016\/j.aei.2022.101721","article-title":"Maximum average impulse energy ratio deconvolution and its application for periodic fault impulses enhancement of rolling bearing","volume":"53","author":"Wang","year":"2022","journal-title":"Advanced Engineering Informatics"},{"key":"10.1016\/j.eswa.2026.131803_b0225","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2023.107031","article-title":"C-ECAFormer: A new lightweight fault diagnosis framework towards heavy noise and small samples","volume":"126","author":"Wang","year":"2023","journal-title":"Engineering Applications of Artificial Intelligence"},{"issue":"1","key":"10.1016\/j.eswa.2026.131803_b0230","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1016\/0016-7142(78)90005-4","article-title":"Minimum entropy deconvolution","volume":"16","author":"Wiggins","year":"1978","journal-title":"Geoexploration"},{"issue":"2","key":"10.1016\/j.eswa.2026.131803_b0235","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1504\/IJHM.2023.130520","article-title":"An improved gated convolutional neural network for rolling bearing fault diagnosis with imbalanced data","volume":"6","author":"Xi","year":"2023","journal-title":"International Journal of Hydromechatronics"},{"key":"10.1016\/j.eswa.2026.131803_b0240","series-title":"Engineering Applications of Artificial Intelligence. Epub ahead of print 2023","article-title":"Feature selection and feature learning in machine learning applications for gas turbines: A review","author":"Xie","year":"2023"},{"key":"10.1016\/j.eswa.2026.131803_b0245","first-page":"1","article-title":"Fault diagnosis of hydraulic system based on D-S evidence theory and SVM","author":"Yin","year":"2024","journal-title":"International Journal of Hydromechatronics 7(1). Inderscience Publishers"},{"key":"10.1016\/j.eswa.2026.131803_b0250","doi-asserted-by":"crossref","first-page":"349","DOI":"10.1016\/j.ymssp.2016.04.033","article-title":"Kurtosis based weighted sparse model with convex optimization technique for bearing fault diagnosis","volume":"80","author":"Zhang","year":"2016","journal-title":"Mechanical Systems and Signal Processing"},{"issue":"7","key":"10.1016\/j.eswa.2026.131803_b0255","doi-asserted-by":"crossref","first-page":"7514","DOI":"10.1109\/JSEN.2023.3248285","article-title":"Multivariate Dynamic Mode Decomposition and its Application to Bearing Fault Diagnosis","volume":"23","author":"Zhang","year":"2023","journal-title":"IEEE Sensors Journal"},{"issue":"2","key":"10.1016\/j.eswa.2026.131803_b0260","doi-asserted-by":"crossref","first-page":"1092","DOI":"10.1109\/TASE.2022.3179457","article-title":"A Blind Deconvolution Approach based on Spectral Harmonics-to-Noise Ratio for Rotating Machinery Condition monitoring","volume":"20","author":"Zhou","year":"2023","journal-title":"IEEE Transactions on Automation Science and Engineering"},{"issue":"3","key":"10.1016\/j.eswa.2026.131803_b0265","doi-asserted-by":"crossref","first-page":"3639","DOI":"10.1109\/TASE.2023.3282844","article-title":"Multi-Node Feature Learning Network based on Maximum Spectral Harmonics-to-Noise Ratio Deconvolution for Machine Condition monitoring","volume":"21","author":"Zhou","year":"2024","journal-title":"IEEE Transactions on Automation Science and Engineering"},{"key":"10.1016\/j.eswa.2026.131803_b0270","doi-asserted-by":"crossref","DOI":"10.1016\/j.neuroimage.2023.120142","article-title":"Unsupervised representation learning of spontaneous MEG data with nonlinear ICA","volume":"274","author":"Zhu","year":"2023","journal-title":"NeuroImage"}],"container-title":["Expert Systems with Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0957417426007165?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0957417426007165?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T16:12:02Z","timestamp":1772122322000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0957417426007165"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6]]},"references-count":54,"alternative-id":["S0957417426007165"],"URL":"https:\/\/doi.org\/10.1016\/j.eswa.2026.131803","relation":{},"ISSN":["0957-4174"],"issn-type":[{"value":"0957-4174","type":"print"}],"subject":[],"published":{"date-parts":[[2026,6]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Multivariate cyclostationary deep deconvolution (MCDD): An intelligent multivariate signal processing algorithm","name":"articletitle","label":"Article Title"},{"value":"Expert Systems with Applications","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.eswa.2026.131803","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"131803"}}