{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T07:51:41Z","timestamp":1781596301456,"version":"3.54.5"},"reference-count":26,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100010023","name":"Natural Science Research of Jiangsu Higher Education Institutions of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100010023","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100017549","name":"Science and Technology Innovation 2025 Major Project of Ningbo","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100017549","id-type":"DOI","asserted-by":"publisher"}]},{"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":["Engineering Applications of Artificial Intelligence"],"published-print":{"date-parts":[[2026,9]]},"DOI":"10.1016\/j.engappai.2026.115267","type":"journal-article","created":{"date-parts":[[2026,5,31]],"date-time":"2026-05-31T14:38:01Z","timestamp":1780238281000},"page":"115267","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"P1","title":["Fault severity assessment for centrifugal pump rotors based on multiple residual neural networks"],"prefix":"10.1016","volume":"179","author":[{"given":"Liang","family":"Dong","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Siyuan","family":"Hu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chuanhan","family":"Fan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Runan","family":"Hua","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Honggang","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weihua","family":"Shi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cui","family":"Dai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"9","key":"10.1016\/j.engappai.2026.115267_bib1","doi-asserted-by":"crossref","first-page":"4113","DOI":"10.1016\/j.eswa.2013.12.026","article-title":"An approach to fault diagnosis of reciprocating compressor valves using Teager\u2013Kaiser energy operator and deep belief networks","volume":"41","author":"AlThobiani","year":"2014","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.engappai.2026.115267_bib2","article-title":"Fault prediction of centrifugal pump based on improved KNN","volume":"2021","author":"Chen","year":"2021","journal-title":"Shock Vib."},{"issue":"4","key":"10.1016\/j.engappai.2026.115267_bib3","doi-asserted-by":"crossref","first-page":"1507","DOI":"10.1016\/j.net.2023.01.009","article-title":"Cavitation state identification of centrifugal pump based on CEEMD-DRSN","volume":"55","author":"Dai","year":"2023","journal-title":"Nucl. Eng. Technol."},{"key":"10.1016\/j.engappai.2026.115267_bib4","article-title":"An experimental study on local and global optima of linear antenna array synthesis by using the sequential least squares programming","volume":"139","author":"Gong","year":"2023","journal-title":"Appl. Soft Comput."},{"key":"10.1016\/j.engappai.2026.115267_bib5","doi-asserted-by":"crossref","first-page":"490","DOI":"10.1016\/j.measurement.2016.07.054","article-title":"Hierarchical adaptive deep convolution neural network and its application to bearing fault diagnosis","volume":"93","author":"Guo","year":"2016","journal-title":"Measurement"},{"key":"10.1016\/j.engappai.2026.115267_bib6","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.measurement.2017.07.017","article-title":"A convolutional neural network based feature learning and fault diagnosis method for the condition monitoring of gearbox","volume":"111","author":"Jing","year":"2017","journal-title":"Measurement"},{"issue":"9","key":"10.1016\/j.engappai.2026.115267_bib7","doi-asserted-by":"crossref","DOI":"10.3390\/en16093707","article-title":"Integrating survival analysis with Bayesian statistics to forecast the remaining useful life of a centrifugal pump conditional to multiple fault types","volume":"16","author":"Kapuria","year":"2023","journal-title":"Energies"},{"issue":"6","key":"10.1016\/j.engappai.2026.115267_bib8","first-page":"3565","article-title":"A comparison of fuzzy clustering algorithms for bearing fault diagnosis","volume":"34","author":"Li","year":"2018","journal-title":"J. Intell. Fuzzy Syst."},{"issue":"11","key":"10.1016\/j.engappai.2026.115267_bib9","doi-asserted-by":"crossref","DOI":"10.1088\/1361-6501\/ab3072","article-title":"Enhanced generative adversarial networks for fault diagnosis of rotating machinery with imbalanced data","volume":"30","author":"Li","year":"2019","journal-title":"Meas. Sci. Technol."},{"issue":"19","key":"10.1016\/j.engappai.2026.115267_bib10","doi-asserted-by":"crossref","first-page":"8198","DOI":"10.3390\/s23198198","article-title":"Intelligent early fault diagnosis of space flywheel rotor system","volume":"23","author":"Liao","year":"2023","journal-title":"Sensors"},{"key":"10.1016\/j.engappai.2026.115267_bib11","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1016\/j.isatra.2018.04.005","article-title":"Fault diagnosis of rolling bearings with recurrent neural network-based autoencoders","volume":"77","author":"Liu","year":"2018","journal-title":"ISA (Instrum. Soc. Am.) Trans."},{"key":"10.1016\/j.engappai.2026.115267_bib12","doi-asserted-by":"crossref","DOI":"10.1016\/j.measurement.2022.111174","article-title":"Research on fault diagnosis of gas turbine rotor based on adversarial discriminative domain adaption transfer learning","volume":"196","author":"Liu","year":"2022","journal-title":"Measurement"},{"key":"10.1016\/j.engappai.2026.115267_bib13","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1016\/j.compind.2018.12.012","article-title":"Ensemble deep learning-based fault diagnosis of rotor bearing systems","volume":"105","author":"Ma","year":"2019","journal-title":"Comput. Ind."},{"key":"10.1016\/j.engappai.2026.115267_bib14","doi-asserted-by":"crossref","first-page":"190","DOI":"10.1016\/j.ymssp.2019.02.055","article-title":"Deep residual learning with demodulated time-frequency features for fault diagnosis of planetary gearbox under nonstationary running conditions","volume":"127","author":"Ma","year":"2019","journal-title":"Mech. Syst. Signal Process."},{"issue":"12","key":"10.1016\/j.engappai.2026.115267_bib15","doi-asserted-by":"crossref","first-page":"10865","DOI":"10.1109\/TIE.2019.2959492","article-title":"Macroscopic\u2013microscopic attention in LSTM networks based on fusion features for gear remaining life prediction","volume":"67","author":"Qin","year":"2019","journal-title":"IEEE Trans. Ind. Electron."},{"key":"10.1016\/j.engappai.2026.115267_bib16","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.compind.2019.01.001","article-title":"Generative adversarial networks for data augmentation in machine fault diagnosis","volume":"106","author":"Shao","year":"2019","journal-title":"Comput. Ind."},{"key":"10.1016\/j.engappai.2026.115267_bib17","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1007\/s12293-018-0277-2","article-title":"Novel paralleled extreme learning machine networks for fault diagnosis of wind turbine drivetrain","volume":"11","author":"Wang","year":"2019","journal-title":"Memetic Computing"},{"key":"10.1016\/j.engappai.2026.115267_bib18","doi-asserted-by":"crossref","first-page":"457","DOI":"10.1016\/j.isatra.2019.07.001","article-title":"A novel deep learning based fault diagnosis approach for chemical process with extended deep belief network","volume":"96","author":"Wang","year":"2020","journal-title":"ISA (Instrum. Soc. Am.) Trans."},{"key":"10.1016\/j.engappai.2026.115267_bib19","article-title":"Research on the rotor fault diagnosis method based on QPSO-VMD-PCA-SVM","volume":"10","author":"Wang","year":"2022","journal-title":"Front. Energy Res."},{"key":"10.1016\/j.engappai.2026.115267_bib20","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.knosys.2016.10.022","article-title":"A novel intelligent method for bearing fault diagnosis based on affinity propagation clustering and adaptive feature selection","volume":"116","author":"Wei","year":"2017","journal-title":"Knowl. Base Syst."},{"key":"10.1016\/j.engappai.2026.115267_bib21","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1016\/j.measurement.2018.05.040","article-title":"Semi-automated diagnosis of bearing faults based on a hidden Markov model of the vibration signals","volume":"127","author":"Xin","year":"2018","journal-title":"Measurement"},{"key":"10.1016\/j.engappai.2026.115267_bib22","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2020.105484","article-title":"Multiscale cascading deep belief network for fault identification of rotating machinery under various working conditions","volume":"193","author":"Yan","year":"2020","journal-title":"Knowl. Base Syst."},{"issue":"10","key":"10.1016\/j.engappai.2026.115267_bib23","doi-asserted-by":"crossref","first-page":"3936","DOI":"10.3390\/s22103936","article-title":"Rolling bearing fault diagnosis based on Markov transition field and residual network","volume":"22","author":"Yan","year":"2022","journal-title":"Sensors"},{"key":"10.1016\/j.engappai.2026.115267_bib24","doi-asserted-by":"crossref","first-page":"5201","DOI":"10.1007\/s12206-018-1018-7","article-title":"Rolling bearing fault diagnosis based on mean multigranulation decision-theoretic rough set and non-naive Bayesian classifier","volume":"32","author":"Yu","year":"2018","journal-title":"J. Mech. Sci. Technol."},{"key":"10.1016\/j.engappai.2026.115267_bib25","doi-asserted-by":"crossref","first-page":"526","DOI":"10.1016\/j.measurement.2019.02.022","article-title":"Rotor fault diagnosis using a convolutional neural network with symmetrized dot pattern images","volume":"138","author":"Zhu","year":"2019","journal-title":"Measurement"},{"key":"10.1016\/j.engappai.2026.115267_bib26","doi-asserted-by":"crossref","first-page":"405","DOI":"10.1007\/s10845-014-0987-3","article-title":"Bearing fault diagnosis using multiclass support vector machines with binary particle swarm optimization and regularized Fisher's criterion","volume":"28","author":"Ziani","year":"2017","journal-title":"J. Intell. Manuf."}],"container-title":["Engineering Applications of Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0952197626015514?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0952197626015514?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T06:52:34Z","timestamp":1781592754000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0952197626015514"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9]]},"references-count":26,"alternative-id":["S0952197626015514"],"URL":"https:\/\/doi.org\/10.1016\/j.engappai.2026.115267","relation":{},"ISSN":["0952-1976"],"issn-type":[{"value":"0952-1976","type":"print"}],"subject":[],"published":{"date-parts":[[2026,9]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Fault severity assessment for centrifugal pump rotors based on multiple residual neural networks","name":"articletitle","label":"Article Title"},{"value":"Engineering Applications of Artificial Intelligence","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.engappai.2026.115267","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":"115267"}}