{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T20:01:00Z","timestamp":1783972860385,"version":"3.55.0"},"reference-count":48,"publisher":"Oxford University Press (OUP)","issue":"9","license":[{"start":{"date-parts":[[2025,9,12]],"date-time":"2025-09-12T00:00:00Z","timestamp":1757635200000},"content-version":"vor","delay-in-days":11,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Digital Twin Acoustic Fault Diagnosis and Control Technology and Application for Deep-sea Ships","award":["XTCX-KJ-2024-19"],"award-info":[{"award-number":["XTCX-KJ-2024-19"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,9,2]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Bearings are core components of rotating machinery, and their failures can cause significant production accidents. Current multi-source data fusion methods and independent network architectures show limited diagnostic performance on small and medium-sized datasets. To address multi-sensor data fusion and feature extraction in bearing fault diagnosis, we propose MCWT-WCFormer, a lightweight convolution-Transformer hybrid network with three key modules: multi-channel wavelet transform (MCWT), channel-spatial attention net (CSAN), and wave-convolution and attention fusion net (WAFN). (1) MCWT transforms multi-source signals into grayscale images through wavelet transform, stacks them into RGB format, integrating time-frequency information. (2) CSAN generates spatial and channel information descriptors and dynamically weights feature maps. (3) WAFN extracts high-frequency features of the Transformer\u2019s Key by introducing wavelet transform convolution, realizing joint learning of local-global features. MCWT-WCFormer optimizes efficiency and performance by leveraging the inductive bias of convolutional neural network and the scalability of the Transformer. Cross-evaluated on HUST-gearbox (Huazhong University of Science and Technology gearbox) and SHU-TSTB (Shanghai University Transmission Simulation Test Bench) datasets, MCWT-WCFormer achieves 98.12% \u00b1 0.17% and 98.03% \u00b1 0.12% accuracy, respectively, with a single sample diagnosis time of about 4.2 ms while having the lowest complexity (43.25 GFLOPs) and parameters (5.91 M). It can be integrated into industrial digital twin systems cost-effectively while supporting new metrics like cyclic-correntropy. It is extendable to rotating machinery health management like steam turbines and wind turbine gearboxes.<\/jats:p>","DOI":"10.1093\/jcde\/qwaf080","type":"journal-article","created":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T12:03:48Z","timestamp":1757592228000},"page":"82-100","source":"Crossref","is-referenced-by-count":6,"title":["WCFormer: A wavelet-enhanced CNN-transformer hybrid network for bearing fault diagnosis using multi-sensor signal fusion"],"prefix":"10.1093","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-0706-0148","authenticated-orcid":false,"given":"Xinjie","family":"Cao","sequence":"first","affiliation":[{"name":"School of Mechatronic Engineering and Automation, Shanghai University , Shanghai 200444 ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zenggui","family":"Gao","sequence":"additional","affiliation":[{"name":"School of Mechatronic Engineering and Automation, Shanghai University , Shanghai 200444 ,","place":["China"]},{"name":"Shanghai Key Laboratory of Intelligent Manufacturing and Robotics, Shanghai University , Shanghai 200444 ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongjiang","family":"Lu","sequence":"additional","affiliation":[{"name":"School of Mechatronic Engineering and Automation, Shanghai University , Shanghai 200444 ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingwei","family":"Tang","sequence":"additional","affiliation":[{"name":"School of Mechatronic Engineering and Automation, Shanghai University , Shanghai 200444 ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuyan","family":"Yao","sequence":"additional","affiliation":[{"name":"School of Mechatronic Engineering and Automation, Shanghai University , Shanghai 200444 ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1520-6506","authenticated-orcid":false,"given":"Lilan","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Mechatronic Engineering and Automation, Shanghai University , Shanghai 200444 ,","place":["China"]},{"name":"Shanghai Key Laboratory of Intelligent Manufacturing and Robotics, Shanghai University , Shanghai 200444 ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2025,9,12]]},"reference":[{"key":"2025102300371323400_bib1","doi-asserted-by":"publisher","first-page":"2481","DOI":"10.1007\/s42417-022-00498-9","article-title":"A review of data-driven machinery fault diagnosis using machine learning algorithms","volume":"10","author":"Cen","year":"2022","journal-title":"Journal of Vibration Engineering & Technologies"},{"key":"2025102300371323400_bib2","first-page":"296","article-title":"Stable anti-noise fault diagnosis of rolling bearing based on CNN-BiLSTM","volume":"52","author":"Chen","year":"2022","journal-title":"J Jilin Univ (Engineering Edition)"},{"key":"2025102300371323400_bib3","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1093\/jcde\/qwaf021","article-title":"An improved RSMamba network based on multi-domain image fusion for wheelset bearing fault diagnosis under composite conditions","volume":"12","author":"Deng","year":"2025","journal-title":"Journal of Computational Design and Engineering"},{"key":"2025102300371323400_bib4","article-title":"An image is worth 16x16 words: Transformers for image recognition at scale","author":"Dosovitskiy","year":"2021"},{"key":"2025102300371323400_bib5","doi-asserted-by":"publisher","first-page":"6824","DOI":"10.48550\/arXiv.2104.11227","article-title":"Multiscale vision transformers","volume-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","author":"Fan","year":"2021"},{"key":"2025102300371323400_bib6","doi-asserted-by":"publisher","first-page":"125","DOI":"10.1007\/s42791-019-0016-y","article-title":"A comprehensive review of artificial intelligence-based approaches for rolling element bearing PHM: Shallow and deep learning","volume":"1","author":"Hamadache","year":"2019","journal-title":"JMST Advances"},{"key":"2025102300371323400_bib7","doi-asserted-by":"publisher","first-page":"770","DOI":"10.48550\/arXiv.1512.03385","article-title":"Deep residual learning for image recognition","volume-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","author":"He","year":"2016"},{"key":"2025102300371323400_bib8","doi-asserted-by":"publisher","first-page":"327","DOI":"10.1016\/j.neucom.2018.06.078","article-title":"A survey on deep learning based bearing fault diagnosis","volume":"335","author":"Hoang","year":"2019","journal-title":"Neurocomputing"},{"key":"2025102300371323400_bib9","doi-asserted-by":"publisher","first-page":"6105","DOI":"10.1109\/TII.2022.3165283","article-title":"Demagnetization fault diagnosis of permanent magnet synchronous motors using magnetic leakage signals","volume":"19","author":"Huang","year":"2022","journal-title":"IEEE Transactions on Industrial Informatics"},{"key":"2025102300371323400_bib10","doi-asserted-by":"publisher","first-page":"17139","DOI":"10.1109\/JSEN.2022.3193943","article-title":"Intelligent fault diagnosis of rotating machines based on wavelet time-frequency diagram and optimized stacked denoising auto-encoder","volume":"22","author":"Jia","year":"2022","journal-title":"IEEE Sensors Journal"},{"key":"2025102300371323400_bib11","doi-asserted-by":"publisher","first-page":"5940","DOI":"10.3390\/ma15175940","article-title":"Ball bearing fault diagnosis using recurrence analysis","volume":"15","author":"Kecik","year":"2022","journal-title":"Materials"},{"key":"2025102300371323400_bib12","doi-asserted-by":"publisher","first-page":"121521","DOI":"10.1016\/j.eswa.2023.121521","article-title":"Dual-source Gramian angular field method and its application on fault diagnosis of drilling pump fluid end","volume":"237","author":"Li","year":"2024","journal-title":"Expert Systems with Applications"},{"key":"2025102300371323400_bib13","doi-asserted-by":"publisher","first-page":"107392","DOI":"10.1016\/j.measurement.2019.107392","article-title":"Rolling bearing fault diagnosis based on improved adaptive parameterless empirical wavelet transform and sparse denoising","volume":"152","author":"Li","year":"2020","journal-title":"Measurement"},{"key":"2025102300371323400_bib14","doi-asserted-by":"publisher","first-page":"1489","DOI":"10.1109\/TPAMI.2022.3164083","article-title":"Contextual transformer networks for visual recognition","volume":"45","author":"Li","year":"2022","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"2025102300371323400_bib15","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1006\/jsvi.2000.2864","article-title":"Feature extraction based on Morlet wavelet and its application for mechanical fault diagnosis","volume":"234","author":"Lin","year":"2000","journal-title":"Journal of Sound and Vibration"},{"key":"2025102300371323400_bib16","first-page":"2","article-title":"A tutorial of the wavelet transform","volume":"21","author":"Liu","year":"2010","journal-title":"NTUEE, Taiwan"},{"key":"2025102300371323400_bib17","doi-asserted-by":"publisher","first-page":"19563","DOI":"10.1109\/JSEN.2024.3447777","article-title":"An anti-noise bearing\u2019s fault diagnosis method using adaptive deconvolution and Mobile ViT","volume":"25","author":"Liu","year":"2025","journal-title":"IEEE Sensors Journal"},{"key":"2025102300371323400_bib18","doi-asserted-by":"publisher","first-page":"11976","DOI":"10.48550\/arXiv.2201.03545","article-title":"A convnet for the 2020s","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Liu","year":"2022"},{"key":"2025102300371323400_bib19","doi-asserted-by":"publisher","first-page":"1605","DOI":"10.1109\/TII.2021.3084615","article-title":"Motor fault diagnosis based on scale invariant image features","volume":"18","author":"Long","year":"2021","journal-title":"IEEE Transactions on Industrial Informatics"},{"key":"2025102300371323400_bib20","doi-asserted-by":"publisher","first-page":"719","DOI":"10.1109\/TEC.2005.847955","article-title":"Condition monitoring and fault diagnosis of electrical motors\u2014A review","volume":"20","author":"Nandi","year":"2005","journal-title":"IEEE Transactions on Energy Conversion"},{"key":"2025102300371323400_bib21","doi-asserted-by":"publisher","first-page":"127678","DOI":"10.1016\/j.energy.2023.127678","article-title":"A transformer-based deep neural network with wavelet transform for forecasting wind speed and wind energy","volume":"278","author":"Nascimento","year":"2023","journal-title":"Energy"},{"key":"2025102300371323400_bib22","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1093\/jcde\/qwae105","article-title":"Fault frequency band segmentation and domain adaptation with fault simulated signal for fault diagnosis of rolling element bearings","volume":"12","author":"Park","year":"2025","journal-title":"Journal of Computational Design and Engineering"},{"key":"2025102300371323400_bib23","doi-asserted-by":"publisher","first-page":"628","DOI":"10.1007\/s40430-020-02711-w","article-title":"Vibration analysis in bearings for failure prevention using CNN","volume":"42","author":"Pinedo-Sanchez","year":"2020","journal-title":"Journal of the Brazilian Society of Mechanical Sciences and Engineering"},{"key":"2025102300371323400_bib24","doi-asserted-by":"publisher","first-page":"10428","DOI":"10.48550\/arXiv.2003.13678","article-title":"Designing network design spaces","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Radosavovic","year":"2020"},{"key":"2025102300371323400_bib25","doi-asserted-by":"publisher","first-page":"464","DOI":"10.48550\/arXiv.1803.02155","article-title":"Self-attention with relative position representations","volume-title":"In\u00a0Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics","author":"Shaw","year":"2018"},{"key":"2025102300371323400_bib26","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1409.1556","article-title":"Very deep convolutional networks for large-scale image recognition","author":"Simonyan","year":"2014"},{"key":"2025102300371323400_bib27","doi-asserted-by":"publisher","first-page":"4041","DOI":"10.1007\/s12652-021-03177-x","article-title":"A bearing fault diagnosis model based on CNN with wide convolution kernels","volume":"13","author":"Song","year":"2022","journal-title":"Journal of Ambient Intelligence and Humanized Computing"},{"key":"2025102300371323400_bib28","doi-asserted-by":"publisher","first-page":"2862","DOI":"10.1016\/j.renene.2010.05.012","article-title":"Wind turbine fault diagnosis based on Morlet wavelet transformation and Wigner-Ville distribution","volume":"35","author":"Tang","year":"2010","journal-title":"Renewable Energy"},{"key":"2025102300371323400_bib29","doi-asserted-by":"publisher","first-page":"5785","DOI":"10.48550\/arXiv.2303.14189","article-title":"Fastvit: A fast hybrid vision transformer using structural reparameterization","volume-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","author":"Vasu","year":"2023"},{"key":"2025102300371323400_bib30","doi-asserted-by":"publisher","first-page":"182","DOI":"10.1016\/j.compind.2018.12.013","article-title":"A novel convolutional neural network based fault recognition method via image fusion of multi-vibration-signals","volume":"105","author":"Wang","year":"2019","journal-title":"Computers in Industry"},{"key":"2025102300371323400_bib31","doi-asserted-by":"publisher","first-page":"20240015","DOI":"10.1515\/phys-2024-0015","article-title":"Transformer-based intelligent fault diagnosis methods of mechanical equipment: A survey","volume":"22","author":"Wang","year":"2024","journal-title":"Open Physics"},{"key":"2025102300371323400_bib32","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/PHM-Nanjing52125.2021.9612919","article-title":"A one-dimensional vision transformer with multiscale convolution fusion for bearing fault diagnosis","volume-title":"2021 Global Reliability and Prognostics and Health Management (PHM-Nanjing)","author":"Weng","year":"2021"},{"key":"2025102300371323400_bib33","doi-asserted-by":"publisher","first-page":"439","DOI":"10.1016\/j.jmsy.2023.02.018","article-title":"A transformer-based approach for novel fault detection and fault classification\/diagnosis in manufacturing: A rotary system application","volume":"67","author":"Wu","year":"2023","journal-title":"Journal of Manufacturing Systems"},{"key":"2025102300371323400_bib34","doi-asserted-by":"publisher","first-page":"22","DOI":"10.48550\/arXiv.2103.15808","article-title":"Cvt: Introducing convolutions to vision transformers","volume-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","author":"Wu","year":"2021"},{"key":"2025102300371323400_bib35","first-page":"4558","article-title":"A two-dimensional convolutional neural network optimization method for bearing fault diagnosis","volume":"39","author":"Xiao","year":"2019","journal-title":"Proceedings of the CSEE"},{"key":"2025102300371323400_bib36","doi-asserted-by":"publisher","first-page":"277","DOI":"10.3390\/machines11020277","article-title":"A multi-information fusion ViT model and its application to the fault diagnosis of bearing with small data samples","volume":"11","author":"Xu","year":"2023","journal-title":"Machines"},{"key":"2025102300371323400_bib37","doi-asserted-by":"publisher","first-page":"110848","DOI":"10.1016\/j.ress.2025.110848","article-title":"An Improved Cross-Machine Transfer Strategy Based on Multi-Source Domain Knowledge for Abnormal Sample Recognition","author":"Yan","year":"2025","journal-title":"Reliability Engineering & System Safety"},{"key":"2025102300371323400_bib38","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1080\/10589759.2024.2412184","article-title":"Transformer assisted by DSC and BiLSTM for bearing fault pattern recognition under strong noise interference","author":"Yang","year":"2024","journal-title":"Nondestructive Testing and Evaluation"},{"key":"2025102300371323400_bib39","doi-asserted-by":"publisher","first-page":"5334","DOI":"10.3390\/s23115334","article-title":"Research on rolling bearing fault diagnosis based on digital twin data and improved ConvNext","volume":"23","author":"Zhang","year":"2023","journal-title":"Sensors"},{"key":"2025102300371323400_bib40","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1007\/978-3-030-17989-2_3","article-title":"Wavelet transform","volume-title":"Fundamentals of image data mining: Analysis, Features, Classification and Retrieval","author":"Zhang","year":"2019"},{"key":"2025102300371323400_bib41","doi-asserted-by":"publisher","first-page":"16878132221125019","DOI":"10.1177\/16878132221125019","article-title":"Signals hierarchical feature enhancement method for CNN-based fault diagnosis","volume":"14","author":"Zhang","year":"2022","journal-title":"Advances in Mechanical Engineering"},{"key":"2025102300371323400_bib42","doi-asserted-by":"publisher","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":"2025102300371323400_bib43","doi-asserted-by":"publisher","first-page":"29857","DOI":"10.1109\/ACCESS.2020.2972859","article-title":"Deep learning algorithms for bearing fault diagnostics\u2014A comprehensive review","volume":"8","author":"Zhang","year":"2020","journal-title":"IEEE Access"},{"key":"2025102300371323400_bib44","doi-asserted-by":"publisher","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":"2025102300371323400_bib45","doi-asserted-by":"publisher","first-page":"737","DOI":"10.3390\/e25050737","article-title":"Multi-fault classification and diagnosis of rolling bearing based on improved convolution neural network","volume":"25","author":"Zhang","year":"2023","journal-title":"Entropy"},{"key":"2025102300371323400_bib46","doi-asserted-by":"publisher","first-page":"109964","DOI":"10.1016\/j.ress.2024.109964","article-title":"Domain generalization for cross-domain fault diagnosis: An application-oriented perspective and a benchmark study","volume":"245","author":"Zhao","year":"2024","journal-title":"Reliability Engineering & System Safety"},{"key":"2025102300371323400_bib47","first-page":"1","article-title":"Rolling bearing fault diagnosis based on residual connection and 1D-CNN","volume":"40","author":"Zhao","year":"2021","journal-title":"Journal of Vibration and Shock"},{"key":"2025102300371323400_bib48","doi-asserted-by":"publisher","first-page":"117297","DOI":"10.1016\/j.eswa.2022.117297","article-title":"Construction of health indicators for condition monitoring of rotating machinery: A review of the research","volume":"203","author":"Zhou","year":"2022","journal-title":"Expert Systems with Applications"}],"container-title":["Journal of Computational Design and Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/jcde\/advance-article-pdf\/doi\/10.1093\/jcde\/qwaf080\/64258279\/qwaf080.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/jcde\/article-pdf\/12\/9\/82\/64258279\/qwaf080.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/jcde\/article-pdf\/12\/9\/82\/64258279\/qwaf080.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,23]],"date-time":"2025-10-23T04:37:19Z","timestamp":1761194239000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/jcde\/article\/12\/9\/82\/8252957"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9]]},"references-count":48,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2025,9,2]]}},"URL":"https:\/\/doi.org\/10.1093\/jcde\/qwaf080","relation":{},"ISSN":["2288-5048"],"issn-type":[{"value":"2288-5048","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2025,9]]},"published":{"date-parts":[[2025,9]]}}}