{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T07:57:50Z","timestamp":1781251070223,"version":"3.54.1"},"reference-count":47,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100012253","name":"Guangxi University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100012253","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Digital Signal Processing"],"published-print":{"date-parts":[[2026,10]]},"DOI":"10.1016\/j.dsp.2026.106322","type":"journal-article","created":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T23:52:20Z","timestamp":1780617140000},"page":"106322","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Standardized collaborative multiscale Manhattan entropy: A nonlinear time series metric for train bearing fault diagnosis"],"prefix":"10.1016","volume":"182","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-2240-2038","authenticated-orcid":false,"given":"Hongchuang","family":"Tan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yiheng","family":"Su","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Suchao","family":"Xie","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Enci","family":"Yan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Deqiang","family":"He","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.dsp.2026.106322_bib0001","doi-asserted-by":"crossref","DOI":"10.1016\/j.dsp.2025.105454","article-title":"Fault diagnosis method for rolling bearing based on attention mechanism and BiTCN model","volume":"167","author":"Cui","year":"2025","journal-title":"Digit. Signal. Process."},{"key":"10.1016\/j.dsp.2026.106322_bib0002","doi-asserted-by":"crossref","DOI":"10.1016\/j.dsp.2025.105530","article-title":"Tunable sparsity fusion sparse coding-driven sparse representation classification for planetary gearbox fault diagnosis","volume":"168","author":"Jing","year":"2026","journal-title":"Digit. Signal. Process."},{"key":"10.1016\/j.dsp.2026.106322_bib0003","doi-asserted-by":"crossref","DOI":"10.1016\/j.dsp.2025.105011","article-title":"Fault diagnosis of high-speed rolling bearings based on multi-feature fusion fuzzy c-means","volume":"159","author":"Luo","year":"2025","journal-title":"Digit. Signal. Process."},{"key":"10.1016\/j.dsp.2026.106322_bib0004","doi-asserted-by":"crossref","DOI":"10.1016\/j.dsp.2023.104345","article-title":"Fault diagnosis of high-speed rolling bearing in the whole life cycle based on improved grey wolf optimizer-least squares support vector machines","volume":"145","author":"Li","year":"2024","journal-title":"Digit. Signal. Process."},{"key":"10.1016\/j.dsp.2026.106322_bib0005","doi-asserted-by":"crossref","DOI":"10.1016\/j.ijmecsci.2023.108509","article-title":"Sensible multiscale symbol dynamic entropy for fault diagnosis of bearing","volume":"256","author":"Tan","year":"2023","journal-title":"Int. J. Mech. Sci."},{"key":"10.1016\/j.dsp.2026.106322_bib0006","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2024.111682","article-title":"Imbalanced deep transfer network for fault diagnosis of high-speed train traction motor bearings","volume":"293","author":"Liu","year":"2024","journal-title":"Knowl.-Based Syst."},{"key":"10.1016\/j.dsp.2026.106322_bib0007","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1016\/j.isatra.2021.01.060","article-title":"Sparse representation theory for support vector machine kernel function selection and its application in high-speed bearing fault diagnosis","volume":"118","author":"Wang","year":"2021","journal-title":"Isa. T."},{"key":"10.1016\/j.dsp.2026.106322_bib0008","doi-asserted-by":"crossref","DOI":"10.1016\/j.ymssp.2024.111346","article-title":"Ensefgram: an optimal demodulation band selection method for the early fault diagnosis of high-speed train bearings","volume":"213","author":"Wang","year":"2024","journal-title":"Mech. Syst. Signal Pr."},{"key":"10.1016\/j.dsp.2026.106322_bib0009","doi-asserted-by":"crossref","first-page":"403","DOI":"10.1016\/j.isatra.2024.01.023","article-title":"Incipient fault diagnosis of metro train bearing under strong wheel-rail impact interferences using improved complementary CELMDAN and mixture correntropy-based adaptive feature enhancement","volume":"147","author":"Chen","year":"2024","journal-title":"Isa. T."},{"key":"10.1016\/j.dsp.2026.106322_bib0010","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/j.precisioneng.2023.09.007","article-title":"A novel autoencoder modeling method for intelligent assessment of bearing health based on short-time fourier transform and ensemble strategy","volume":"85","author":"Hao","year":"2024","journal-title":"Precis. Eng."},{"key":"10.1016\/j.dsp.2026.106322_bib0011","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1016\/j.measurement.2019.05.049","article-title":"Application of a new EWT-based denoising technique in bearing fault diagnosis","volume":"144","author":"Chegini","year":"2019","journal-title":"Measurement"},{"key":"10.1016\/j.dsp.2026.106322_bib0012","doi-asserted-by":"crossref","DOI":"10.1016\/j.apacoust.2024.110349","article-title":"Empirical variational mode extraction and its application in bearing fault diagnosis","volume":"228","author":"Pang","year":"2025","journal-title":"Appl. Acoust."},{"key":"10.1016\/j.dsp.2026.106322_bib0013","doi-asserted-by":"crossref","DOI":"10.1016\/j.ymssp.2022.108834","article-title":"Weak fault feature extraction of rolling bearings based on improved ensemble noise-reconstructed EMD and adaptive threshold denoising","volume":"171","author":"Yin","year":"2022","journal-title":"Mech. Syst. Signal Pr."},{"key":"10.1016\/j.dsp.2026.106322_bib0014","doi-asserted-by":"crossref","DOI":"10.1016\/j.measurement.2024.116259","article-title":"AGWO-PSO-VMD-TEFCG-AlexNet bearing fault diagnosis method under strong noise","volume":"242","author":"Shen","year":"2025","journal-title":"Measurement"},{"key":"10.1016\/j.dsp.2026.106322_bib0015","doi-asserted-by":"crossref","DOI":"10.1016\/j.measurement.2024.115328","article-title":"Bearing fault diagnosis based on POA-VMD with GADF-swin transformer transfer learning network","volume":"238","author":"Dai","year":"2024","journal-title":"Measurement"},{"key":"10.1016\/j.dsp.2026.106322_bib0016","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2024.110643","article-title":"Distance similarity entropy: a sensitive nonlinear feature extraction method for rolling bearing fault diagnosis","volume":"255","author":"Wang","year":"2025","journal-title":"Reliab. Eng. Syst. Safe."},{"issue":"6","key":"10.1016\/j.dsp.2026.106322_bib0017","doi-asserted-by":"crossref","first-page":"2297","DOI":"10.1073\/pnas.88.6.2297","article-title":"Approximate entropy as a measure of system complexity","volume":"88","author":"Pincus","year":"1991","journal-title":"P. Natl. Acad. Sci. USA"},{"issue":"6","key":"10.1016\/j.dsp.2026.106322_bib0018","doi-asserted-by":"crossref","first-page":"H2039","DOI":"10.1152\/ajpheart.2000.278.6.H2039","article-title":"Physiological time-series analysis using approximate entropy and sample entropy","volume":"278","author":"Richman","year":"2000","journal-title":"Am. J. Physiol.-Heart C."},{"issue":"17","key":"10.1016\/j.dsp.2026.106322_bib0019","doi-asserted-by":"crossref","DOI":"10.1103\/PhysRevLett.88.174102","article-title":"Permutation entropy: a natural complexity measure for time series","volume":"88","author":"Bandt","year":"2002","journal-title":"Phys. Rev. Lett."},{"issue":"1","key":"10.1016\/j.dsp.2026.106322_bib0020","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/j.medengphy.2008.04.005","article-title":"Measuring complexity using FuzzyEn, ApEn, and SampEn","volume":"31","author":"Chen","year":"2009","journal-title":"Med. Eng. Phys."},{"issue":"5","key":"10.1016\/j.dsp.2026.106322_bib0021","doi-asserted-by":"crossref","first-page":"610","DOI":"10.1109\/LSP.2016.2542881","article-title":"Dispersion entropy: a measure for time-series analysis","volume":"23","author":"Rostaghi","year":"2016","journal-title":"IEEE Signal. Proc. Let."},{"issue":"1","key":"10.1016\/j.dsp.2026.106322_bib0022","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1016\/j.ymssp.2013.04.006","article-title":"Quantitative diagnosis of a spall-like fault of a rolling element bearing by empirical mode decomposition and the approximate entropy method","volume":"40","author":"Zhao","year":"2013","journal-title":"Mech. Syst. Signal Pr."},{"key":"10.1016\/j.dsp.2026.106322_bib0023","doi-asserted-by":"crossref","DOI":"10.1016\/j.jsv.2024.118910","article-title":"Detection of ship echo signals in reverberation background based on sample entropy and multiscale sample entropy","volume":"599","author":"Li","year":"2025","journal-title":"J. Sound. Vib."},{"key":"10.1016\/j.dsp.2026.106322_bib0024","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1016\/j.mechmachtheory.2015.11.010","article-title":"Hierarchical fuzzy entropy and improved support vector machine based binary tree approach for rolling bearing fault diagnosis","volume":"98","author":"Li","year":"2016","journal-title":"Mech. Mach. Theory"},{"key":"10.1016\/j.dsp.2026.106322_bib0025","doi-asserted-by":"crossref","first-page":"536","DOI":"10.1016\/j.isatra.2023.09.009","article-title":"A new fault feature extraction method of rolling bearings based on the improved self-selection ICEEMDAN-permutation entropy","volume":"143","author":"Xiao","year":"2023","journal-title":"Isa. T."},{"issue":"3","key":"10.1016\/j.dsp.2026.106322_bib0026","doi-asserted-by":"crossref","DOI":"10.1088\/1361-6501\/aca81b","article-title":"Hierarchical dispersion lempel\u2013ziv complexity for fault diagnosis of rolling bearing","volume":"34","author":"Li","year":"2023","journal-title":"Meas. Sci. Technol."},{"issue":"2","key":"10.1016\/j.dsp.2026.106322_bib0027","doi-asserted-by":"crossref","first-page":"259","DOI":"10.3390\/e23020259","article-title":"Rolling bearing fault diagnosis based on refined composite multi-scale approximate entropy and optimized probabilistic neural network","volume":"23","author":"Ma","year":"2021","journal-title":"Entropy-Switz"},{"key":"10.1016\/j.dsp.2026.106322_bib0028","series-title":"Bearing fault diagnosis using modified multi-scale sample entropy and one-against-rest feature selection","first-page":"1","author":"Qin","year":"2021"},{"issue":"4","key":"10.1016\/j.dsp.2026.106322_bib0029","doi-asserted-by":"crossref","first-page":"212","DOI":"10.3390\/e20040212","article-title":"A feature extraction method using improved multi-scale entropy for rolling bearing fault diagnosis","volume":"20","author":"Ju","year":"2018","journal-title":"Entropy-Switz"},{"key":"10.1016\/j.dsp.2026.106322_bib0030","doi-asserted-by":"crossref","DOI":"10.1016\/j.measurement.2020.107574","article-title":"Rolling bearing fault diagnosis using generalized refined composite multiscale sample entropy and optimized support vector machine","volume":"156","author":"Wang","year":"2020","journal-title":"Measurement"},{"key":"10.1016\/j.dsp.2026.106322_bib0031","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1016\/j.mechmachtheory.2014.03.014","article-title":"A rolling bearing fault diagnosis method based on multi-scale fuzzy entropy and variable predictive model-based class discrimination","volume":"78","author":"Zheng","year":"2014","journal-title":"Mech. Mach. Theory"},{"key":"10.1016\/j.dsp.2026.106322_bib0032","doi-asserted-by":"crossref","DOI":"10.1016\/j.dsp.2022.103884","article-title":"Fault diagnosis of bearing based on refined piecewise composite multivariate multiscale fuzzy entropy","volume":"133","author":"Jin","year":"2023","journal-title":"Digit. Signal. Process."},{"key":"10.1016\/j.dsp.2026.106322_bib0033","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1016\/j.isatra.2021.05.042","article-title":"Generalized refined composite multiscale fuzzy entropy and multi-cluster feature selection based intelligent fault diagnosis of rolling bearing","volume":"123","author":"Zheng","year":"2022","journal-title":"Isa. T."},{"key":"10.1016\/j.dsp.2026.106322_bib0034","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2021.106883","article-title":"A new bearing fault diagnosis approach combining sensitive statistical features with improved multiscale permutation entropy method","volume":"218","author":"Minhas","year":"2021","journal-title":"Knowl.-Based Syst."},{"key":"10.1016\/j.dsp.2026.106322_bib0035","doi-asserted-by":"crossref","DOI":"10.1016\/j.measurement.2020.107748","article-title":"A novel method of composite multiscale weighted permutation entropy and machine learning for fault complex system fault diagnosis","volume":"158","author":"He","year":"2020","journal-title":"Measurement"},{"key":"10.1016\/j.dsp.2026.106322_bib0036","doi-asserted-by":"crossref","DOI":"10.1016\/j.measurement.2022.111190","article-title":"Rolling mill bearings fault diagnosis based on improved multivariate variational mode decomposition and multivariate composite multiscale weighted permutation entropy","volume":"195","author":"Zhao","year":"2022","journal-title":"Measurement"},{"key":"10.1016\/j.dsp.2026.106322_bib0037","doi-asserted-by":"crossref","DOI":"10.1016\/j.apacoust.2021.108271","article-title":"Multivariate hierarchical multiscale fluctuation dispersion entropy: applications to fault diagnosis of rotating machinery","volume":"182","author":"Zhou","year":"2021","journal-title":"Appl. Acoust."},{"key":"10.1016\/j.dsp.2026.106322_bib0038","doi-asserted-by":"crossref","DOI":"10.1016\/j.measurement.2022.110843","article-title":"Fault diagnosis for rolling bearing using a hybrid hierarchical method based on scale-variable dispersion entropy and parametric t-SNE algorithm","volume":"191","author":"Jiang","year":"2022","journal-title":"Measurement"},{"key":"10.1016\/j.dsp.2026.106322_bib0039","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TIM.2022.3187717","article-title":"A novel bearing faults detection method using generalized gaussian distribution refined composite multiscale dispersion entropy","volume":"71","author":"Dhandapani","year":"2022","journal-title":"IEEE T. Instrum. Meas."},{"key":"10.1016\/j.dsp.2026.106322_bib0040","doi-asserted-by":"crossref","DOI":"10.1016\/j.compind.2024.104172","article-title":"Remaining useful life prediction model of cross-domain rolling bearing via dynamic hybrid domain adaptation and attention contrastive learning","volume":"164","author":"Lu","year":"2025","journal-title":"Comput. Ind."},{"key":"10.1016\/j.dsp.2026.106322_bib0041","doi-asserted-by":"crossref","DOI":"10.1016\/j.measurement.2024.114243","article-title":"Health condition monitoring and diagnosis of rotating machinery based on Manhattan entropy","volume":"227","author":"Tan","year":"2024","journal-title":"Measurement"},{"key":"10.1016\/j.dsp.2026.106322_bib0042","article-title":"FPGA implementation of edge-side motor fault diagnosis using a kalman filter-based empirical mode decomposition algorithm","volume":"159","author":"Li","year":"2025","journal-title":"Control Eng. Pr."},{"key":"10.1016\/j.dsp.2026.106322_bib0043","article-title":"Extreme-learning-machine-based robust integral terminal sliding mode control of bicycle robot","volume":"121","author":"Chen","year":"2022","journal-title":"Control Eng. Pr."},{"key":"10.1016\/j.dsp.2026.106322_bib0044","doi-asserted-by":"crossref","DOI":"10.1016\/j.dsp.2024.104871","article-title":"CNN intelligent diagnosis method for bearing incipient faint faults based on adaptive stochastic resonance-wave peak cross correlation sliding sampling","volume":"156","author":"Liu","year":"2025","journal-title":"Digit. Signal. Process."},{"key":"10.1016\/j.dsp.2026.106322_bib0045","article-title":"A deep learning methodology based on adaptive multiscale CNN and enhanced highway LSTM for industrial process fault diagnosis","volume":"249","author":"Zhao","year":"2024","journal-title":"Reliab. Eng. &Amp; Syst. Saf."},{"key":"10.1016\/j.dsp.2026.106322_bib0046","doi-asserted-by":"crossref","DOI":"10.1016\/j.energy.2024.133901","article-title":"Cross-operating-condition fault diagnosis of a small module reactor based on CNN-LSTM transfer learning with limited data","volume":"313","author":"Luo","year":"2024","journal-title":"Energy"},{"key":"10.1016\/j.dsp.2026.106322_bib0047","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2025.128105","article-title":"Research on rolling bearing fault diagnosis based on parallel depthwise separable ResNet neural network with attention mechanism","volume":"286","author":"Zhang","year":"2025","journal-title":"Expert. Syst. Appl."}],"container-title":["Digital Signal Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1051200426004409?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1051200426004409?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T07:09:03Z","timestamp":1781248143000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1051200426004409"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10]]},"references-count":47,"alternative-id":["S1051200426004409"],"URL":"https:\/\/doi.org\/10.1016\/j.dsp.2026.106322","relation":{},"ISSN":["1051-2004"],"issn-type":[{"value":"1051-2004","type":"print"}],"subject":[],"published":{"date-parts":[[2026,10]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Standardized collaborative multiscale Manhattan entropy: A nonlinear time series metric for train bearing fault diagnosis","name":"articletitle","label":"Article Title"},{"value":"Digital Signal Processing","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.dsp.2026.106322","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"106322"}}