{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T16:49:47Z","timestamp":1775666987590,"version":"3.50.1"},"reference-count":33,"publisher":"Tech Science Press","issue":"3","license":[{"start":{"date-parts":[[2025,5,25]],"date-time":"2025-05-25T00:00:00Z","timestamp":1748131200000},"content-version":"vor","delay-in-days":144,"URL":"https:\/\/doi.org\/10.32604\/TSP-CROSSMARKPOLICY"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["CMC"],"published-print":{"date-parts":[[2025]]},"DOI":"10.32604\/cmc.2025.062625","type":"journal-article","created":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T04:56:20Z","timestamp":1742964980000},"page":"4699-4723","update-policy":"https:\/\/doi.org\/10.32604\/tsp-crossmarkpolicy","source":"Crossref","is-referenced-by-count":8,"title":["Rolling Bearing Fault Diagnosis Based on Cross-Attention Fusion WDCNN and BILSTM"],"prefix":"10.32604","volume":"83","author":[{"given":"Yingyong","family":"Zou","sequence":"first","affiliation":[]},{"given":"Xingkui","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Tao","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Yu","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Long","family":"Li","sequence":"additional","affiliation":[]},{"given":"Wenzhuo","family":"Zhao","sequence":"additional","affiliation":[]}],"member":"17807","published-online":{"date-parts":[[2025]]},"reference":[{"key":"ref1","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1016\/j.triboint.2015.12.037","article-title":"A review on signal processing techniques utilized in the fault diagnosis of rolling element bearings","volume":"96","author":"Rai","year":"2016","journal-title":"Tribol Int"},{"key":"ref2","doi-asserted-by":"crossref","first-page":"252","DOI":"10.1016\/j.ymssp.2015.02.008","article-title":"A summary of fault modelling and predictive health monitoring of rolling element bearings","volume":"60","author":"El-Thalji","year":"2015","journal-title":"Mech Syst Signal Process"},{"key":"ref3","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1016\/j.cja.2020.11.014","article-title":"Nonlinear resonance characteristics of a dual-rotor system with a local defect on the inner ring of the inter-shaft bearing","volume":"34","author":"Yi","year":"2021","journal-title":"Chin J Aeronaut"},{"key":"ref4","doi-asserted-by":"crossref","first-page":"7286","DOI":"10.1016\/j.jfranklin.2020.04.024","article-title":"An unsupervised fault diagnosis method for rolling bearing using STFT and generative neural networks","volume":"357","author":"Tao","year":"2020","journal-title":"J Frankl Inst"},{"key":"ref5","doi-asserted-by":"crossref","first-page":"821","DOI":"10.1109\/TTE.2020.2981880","article-title":"STFT cluster analysis for DC pulsed load monitoring and fault detection on naval shipboard power systems","volume":"6","author":"Maqsood","year":"2020","journal-title":"IEEE Trans Transp Electrif"},{"key":"ref6","doi-asserted-by":"crossref","first-page":"453","DOI":"10.1016\/j.measurement.2018.04.063","article-title":"Non-stationary vibration feature extraction method based on sparse decomposition and order tracking for gearbox fault diagnosis","volume":"124","author":"Li","year":"2018","journal-title":"Measurement"},{"key":"ref7","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/j.compind.2018.12.001","article-title":"Intelligent fault diagnosis of rotating machinery based on one-dimensional convolutional neural network","volume":"108","author":"Wu","year":"2019","journal-title":"Comput Ind"},{"key":"ref8","doi-asserted-by":"crossref","first-page":"1693","DOI":"10.3390\/s20061693","article-title":"Rolling-element bearing fault diagnosis using improved LeNet-5 network","volume":"20","author":"Wan","year":"2020","journal-title":"Sensors"},{"key":"ref9","doi-asserted-by":"crossref","first-page":"531","DOI":"10.1109\/TSP.2013.2288675","article-title":"Variational mode decomposition","volume":"62","author":"Dragomiretskiy","year":"2014","journal-title":"IEEE Trans Signal Process"},{"key":"ref10","doi-asserted-by":"crossref","first-page":"1100","DOI":"10.1016\/j.energy.2019.03.057","article-title":"Vibration fault diagnosis of wind turbines based on variational mode decomposition and energy entropy","volume":"174","author":"Chen","year":"2019","journal-title":"Energy"},{"key":"ref11","doi-asserted-by":"crossref","first-page":"2708","DOI":"10.1177\/1475921720970856","article-title":"An adaptive and efficient variational mode decomposition and its application for bearing fault diagnosis","volume":"20","author":"Jiang","year":"2021","journal-title":"Struct Health Monit"},{"key":"ref12","doi-asserted-by":"crossref","first-page":"462","DOI":"10.1016\/j.ymssp.2018.06.055","article-title":"Data-driven time-frequency analysis method based on variational mode decomposition and its application to gear fault diagnosis in variable working conditions","volume":"116","author":"Li","year":"2019","journal-title":"Mech Syst Signal Process"},{"key":"ref13","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.measurement.2018.08.002","article-title":"Study on planetary gear fault diagnosis based on variational mode decomposition and deep neural networks","volume":"130","author":"Li","year":"2018","journal-title":"Measurement"},{"key":"ref14","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1109\/JSEN.2020.3015884","article-title":"Early fault detection of planetary gearbox based on acoustic emission and improved variational mode decomposition","volume":"21","author":"Liu","year":"2021","journal-title":"IEEE Sens J"},{"key":"ref15","doi-asserted-by":"crossref","first-page":"7496","DOI":"10.1109\/TIE.2020.3003649","article-title":"Multiscale convolutional attention network for predicting remaining useful life of machinery","volume":"68","author":"Wang","year":"2021","journal-title":"IEEE Trans Ind Electron"},{"key":"ref16","doi-asserted-by":"crossref","first-page":"9521","DOI":"10.1109\/TIE.2019.2924605","article-title":"Remaining useful life prediction based on a double-convolutional neural network architecture","volume":"66","author":"Yang","year":"2019","journal-title":"IEEE Trans Ind Electron"},{"key":"ref17","doi-asserted-by":"crossref","first-page":"111570","DOI":"10.1016\/j.measurement.2022.111570","article-title":"Cross-attribute adaptation networks: distilling transferable features from multiple sampling-frequency source domains for fault diagnosis of wind turbine gearboxes","volume":"200","author":"Li","year":"2022","journal-title":"Measurement"},{"key":"ref18","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1186\/s40537-021-00444-8","article-title":"Review of deep learning: concepts, CNN architectures, challenges, applications, future directions","volume":"8","author":"Alzubaidi","year":"2021","journal-title":"J Big Data"},{"key":"ref19","doi-asserted-by":"crossref","first-page":"208","DOI":"10.1016\/j.ress.2018.11.011","article-title":"Deep learning-based remaining useful life estimation of bearings using multi-scale feature extraction","volume":"182","author":"Li","year":"2019","journal-title":"Reliab Eng Syst Saf"},{"key":"ref20","doi-asserted-by":"crossref","first-page":"74793","DOI":"10.1109\/ACCESS.2020.2989371","article-title":"An adaptive anti-noise neural network for bearing fault diagnosis under noise and varying load conditions","volume":"8","author":"Jin","year":"2020","journal-title":"IEEE Access"},{"key":"ref21","doi-asserted-by":"crossref","first-page":"425","DOI":"10.3390\/s17020425","article-title":"A new deep learning model for fault diagnosis with good anti-noise and domain adaptation ability on raw vibration signals","volume":"17","author":"Zhang","year":"2017","journal-title":"Sensors"},{"key":"ref22","series-title":"2019 18th IEEE International Conference on Machine Learning and Applications (ICMLA)","first-page":"613","article-title":"LSTM based bearing fault diagnosis of electrical machines using motor current signal","author":"Sabir","year":"2019 Dec 16\u201319"},{"key":"ref23","series-title":"ICC 2019\u20142019 IEEE International Conference on Communications (ICC)","first-page":"1","article-title":"Modified bi-directional LSTM neural networks for rolling bearing fault diagnosis","author":"Qiu","year":"2019 May 20\u201324"},{"key":"ref24","doi-asserted-by":"crossref","first-page":"103799","DOI":"10.1016\/j.advengsoft.2024.103799","article-title":"Multi-size wide kernel convolutional neural network for bearing fault diagnosis","volume":"198","author":"Kumar","year":"2024","journal-title":"Adv Eng Softw"},{"key":"ref25","first-page":"4395","article-title":"Fault diagnosis method of rolling bearing based on MSCNN-LSTM","volume":"79","author":"Wu","year":"2024","journal-title":"Comput Mater Contin"},{"key":"ref26","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1016\/j.isatra.2020.10.054","article-title":"Fault diagnosis of rolling bearings using an Improved Multi-Scale Convolutional Neural Network with Feature Attention mechanism","volume":"110","author":"Xu","year":"2021","journal-title":"ISA Trans"},{"key":"ref27","doi-asserted-by":"crossref","first-page":"1695","DOI":"10.1109\/TMECH.2022.3223358","article-title":"Attention-based bilinear feature fusion method for bearing fault diagnosis","volume":"28","author":"Wang","year":"2023","journal-title":"IEEE\/ASME Trans Mechatron"},{"key":"ref28","doi-asserted-by":"crossref","first-page":"1831","DOI":"10.3390\/s24061831","article-title":"Convolutional neural network with attention mechanism and visual vibration signal analysis for bearing fault diagnosis","volume":"24","author":"Zhang","year":"2024","journal-title":"Sensors"},{"key":"ref29","doi-asserted-by":"crossref","first-page":"096113","DOI":"10.1088\/1361-6501\/ad53f1","article-title":"Twins transformer: rolling bearing fault diagnosis based on cross-attention fusion of time and frequency domain features","volume":"35","author":"Gao","year":"2024","journal-title":"Meas Sci Technol"},{"key":"ref30","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1515\/aoa-2015-0021","article-title":"Deep belief neural networks and bidirectional long-short term memory hybrid for speech recognition","volume":"40","author":"Brocki","year":"2015","journal-title":"Arch Acoust"},{"key":"ref31","series-title":"International Conference on Machine Learning","first-page":"1604","article-title":"Long short-term memory over recursive structures","author":"Zhu","year":"2015 Jul 6\u201311"},{"key":"ref32","doi-asserted-by":"crossref","first-page":"470","DOI":"10.1016\/j.isatra.2021.11.028","article-title":"Attention-guided joint learning CNN with noise robustness for bearing fault diagnosis and vibration signal denoising","volume":"128","author":"Wang","year":"2022","journal-title":"ISA Trans"},{"key":"ref33","doi-asserted-by":"crossref","first-page":"114094","DOI":"10.1016\/j.eswa.2020.114094","article-title":"A hybrid fine-tuned VMD and CNN scheme for untrained compound fault diagnosis of rotating machinery with unequal-severity faults","volume":"167","author":"Dibaj","year":"2021","journal-title":"Expert Syst Appl"}],"container-title":["Computers, Materials & Continua"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/cdn.techscience.cn\/files\/cmc\/2025\/TSP_CMC-83-3\/TSP_CMC_62625\/TSP_CMC_62625.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,17]],"date-time":"2025-11-17T01:30:02Z","timestamp":1763343002000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.techscience.com\/cmc\/v83n3\/61018"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"references-count":33,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2025]]},"published-print":{"date-parts":[[2025]]}},"URL":"https:\/\/doi.org\/10.32604\/cmc.2025.062625","relation":{},"ISSN":["1546-2226"],"issn-type":[{"value":"1546-2226","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"2024-12-23","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-02-19","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-05-19","order":2,"name":"published","label":"Published Online","group":{"name":"publication_history","label":"Publication History"}}]}}