{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T18:53:54Z","timestamp":1770749634933,"version":"3.50.0"},"reference-count":33,"publisher":"Tech Science Press","issue":"3","license":[{"start":{"date-parts":[[2025,3,16]],"date-time":"2025-03-16T00:00:00Z","timestamp":1742083200000},"content-version":"vor","delay-in-days":74,"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.2025.059295","type":"journal-article","created":{"date-parts":[[2025,1,8]],"date-time":"2025-01-08T02:18:31Z","timestamp":1736302711000},"page":"4863-4880","update-policy":"https:\/\/doi.org\/10.32604\/tsp-crossmarkpolicy","source":"Crossref","is-referenced-by-count":4,"title":["Rolling Bearing Fault Diagnosis Based on MTF Encoding and CBAM-LCNN Mechanism"],"prefix":"10.32604","volume":"82","author":[{"given":"Wei","family":"Liu","sequence":"first","affiliation":[]},{"given":"Sen","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Yinchao","family":"He","sequence":"additional","affiliation":[]},{"given":"Jiaojiao","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Yu","family":"Gu","sequence":"additional","affiliation":[]}],"member":"17807","published-online":{"date-parts":[[2025]]},"reference":[{"key":"ref1","doi-asserted-by":"crossref","first-page":"111507","DOI":"10.1016\/j.ymssp.2024.111507","article-title":"Counterfactual-augmented few-shot contrastive learning for machinery intelligent fault diagnosis with limited samples","volume":"216","author":"Liu","year":"2024","journal-title":"Mech Syst Sig Process"},{"key":"ref2","doi-asserted-by":"crossref","first-page":"342","DOI":"10.1016\/j.jmsy.2023.09.019","article-title":"Interpretable data-augmented adversarial variational autoencoder with sequential attention for imbalanced fault diagnosis","volume":"71","author":"Liu","year":"2023","journal-title":"J Manuf Syst"},{"key":"ref3","doi-asserted-by":"crossref","first-page":"107138","DOI":"10.1016\/j.engappai.2023.107138","article-title":"Feature extraction of multi-sensors for early bearing fault diagnosis using deep learning based on minimum unscented kalman filter","volume":"127","author":"Tang","year":"2024","journal-title":"Eng Appl Artif Intell"},{"key":"ref4","doi-asserted-by":"crossref","first-page":"109439","DOI":"10.1016\/j.knosys.2022.109439","article-title":"Data-augmented wavelet capsule generative adversarial network for rolling bearing fault diagnosis","volume":"252","author":"Liu","year":"2022","journal-title":"Knowl Based Syst"},{"key":"ref5","doi-asserted-by":"crossref","first-page":"2156","DOI":"10.3390\/s24072156","article-title":"Intelligent fault diagnosis of rolling bearing based on gramian angular difference field and improved dual attention residual network","volume":"24","author":"Tong","year":"2024","journal-title":"Sensors"},{"key":"ref6","doi-asserted-by":"crossref","first-page":"3121","DOI":"10.1007\/s42417-022-00735-1","article-title":"A fault diagnosis approach based on 2D-vibration imaging for bearing faults","volume":"11","author":"Mishra","year":"2023","journal-title":"J Vib Eng Technol"},{"key":"ref7","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":"ref8","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/j.neucom.2019.05.052","article-title":"An improved deep convolutional neural network with multi-scale information for bearing fault diagnosis","volume":"359","author":"Huang","year":"2019","journal-title":"Neurocomputing"},{"key":"ref9","doi-asserted-by":"crossref","first-page":"13462","DOI":"10.1109\/TIE.2022.3144572","article-title":"Dual-path mixed-domain residual threshold networks for bearing fault diagnosis","volume":"69","author":"Chen","year":"2022","journal-title":"IEEE Trans Ind Electron"},{"key":"ref10","doi-asserted-by":"crossref","first-page":"106507","DOI":"10.1016\/j.engappai.2023.106507","article-title":"Diagnosisformer: an efficient rolling bearing fault diagnosis method based on improved Transformer","volume":"124","author":"Hou","year":"2023","journal-title":"Eng Appl Artif Intell"},{"key":"ref11","doi-asserted-by":"crossref","first-page":"107557","DOI":"10.1016\/j.measurement.2020.107557","article-title":"Bearings fault detection using wavelet transform and generalized Gaussian density modeling","volume":"155","author":"Tao","year":"2020","journal-title":"Measurement"},{"key":"ref12","first-page":"6380486","article-title":"Bearing intelligent fault diagnosis based on wavelet transform and convolutional neural network","volume":"2020","author":"Guo","year":"2020","journal-title":"Shock Vib"},{"key":"ref13","doi-asserted-by":"crossref","first-page":"106427","DOI":"10.1016\/j.cie.2020.106427","article-title":"Domain adaptive deep belief network for rolling bearing fault diagnosis","volume":"143","author":"Che","year":"2020","journal-title":"Comput Indus Eng"},{"key":"ref14","doi-asserted-by":"crossref","first-page":"103050","DOI":"10.1016\/j.dsp.2021.103050","article-title":"An improved complementary ensemble empirical mode decomposition method and its application in rolling bearing fault diagnosis","volume":"113","author":"Gu","year":"2021","journal-title":"Digit Sig Process"},{"key":"ref15","doi-asserted-by":"crossref","first-page":"108727","DOI":"10.1016\/j.apacoust.2022.108727","article-title":"Towards a fault diagnosis method for rolling bearing with bi-directional deep belief network","volume":"192","author":"Tang","year":"2022","journal-title":"Appl Acoust"},{"key":"ref16","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1134\/S1061830922600575","article-title":"Investigation of rolling bearing weak fault diagnosis based on CNN with two-dimensional image","volume":"59","author":"Zheng","year":"2023","journal-title":"Russ J Nondestruct Test"},{"key":"ref17","doi-asserted-by":"crossref","first-page":"1659","DOI":"10.1007\/s40435-023-01324-0","article-title":"Diagnosis and classification of gear composite faults based on S-transform and improved 2D convolutional neural network","volume":"12","author":"Zheng","year":"2024","journal-title":"Int J Dyn Control"},{"key":"ref18","doi-asserted-by":"crossref","first-page":"42","DOI":"10.21595\/jve.2022.22796","article-title":"Bearing fault diagnosis method based on Gramian angular field and ensemble deep learning","volume":"25","author":"Han","year":"2023","journal-title":"J Vibroeng"},{"key":"ref19","doi-asserted-by":"crossref","first-page":"822","DOI":"10.3390\/met13040822","article-title":"A lightweight neural network based on GAF and ECA for bearing fault diagnosis","volume":"13","author":"Gu","year":"2023","journal-title":"Metals"},{"key":"ref20","doi-asserted-by":"crossref","first-page":"35007","DOI":"10.1088\/1361-6501\/ad11c7","article-title":"Rolling bearing fault diagnosis method based on MTF-MFACNN","volume":"35","author":"Lei","year":"2023","journal-title":"Meas Sci Technol"},{"key":"ref21","doi-asserted-by":"crossref","first-page":"125150","DOI":"10.1088\/1361-6501\/acf8e7","article-title":"A rolling bearing fault diagnosis method based on Markov transition field and multi-scale Runge-Kutta residual network","volume":"34","author":"Ding","year":"2023","journal-title":"Meas Sci Technol"},{"key":"ref22","doi-asserted-by":"crossref","first-page":"105111","DOI":"10.1088\/1361-6501\/ace19c","article-title":"A novel fault diagnosis approach of rolling bearing using intrinsic feature extraction and CBAM-enhanced InceptionNet","volume":"34","author":"Xu","year":"2023","journal-title":"Meas Sci Technol"},{"key":"ref23","doi-asserted-by":"crossref","first-page":"3408","DOI":"10.3390\/rs12203408","article-title":"Depth-wise separable convolution neural network with residual connection for hyperspectral image classification","volume":"12","author":"Dang","year":"2020","journal-title":"Remote Sens"},{"key":"ref24","doi-asserted-by":"crossref","first-page":"35110","DOI":"10.1088\/1361-6501\/aca5a9","article-title":"A fault diagnosis method for rotating machinery with variable speed based on multi-feature fusion and improved ShuffleNet V2","volume":"34","author":"Luo","year":"2022","journal-title":"Meas Sci Technol"},{"key":"ref25","doi-asserted-by":"crossref","first-page":"76108","DOI":"10.1088\/1361-6501\/ad3295","article-title":"A new fault diagnosis of rolling bearing on FFT image coding and L-CNN","volume":"35","author":"Cui","year":"2024","journal-title":"Meas Sci Technol"},{"key":"ref26","doi-asserted-by":"crossref","first-page":"109720","DOI":"10.1016\/j.ress.2023.109720","article-title":"A novel bearing fault diagnosis approach using the Gaussian mixture model and the weighted principal component analysis","volume":"242","author":"Chaleshtori","year":"2024","journal-title":"Reliab Eng Syst Saf"},{"key":"ref27","doi-asserted-by":"crossref","first-page":"751","DOI":"10.3390\/e24060751","article-title":"A new fault diagnosis of rolling bearing based on Markov transition field and CNN","volume":"24","author":"Wang","year":"2022","journal-title":"Entropy"},{"key":"ref28","doi-asserted-by":"crossref","first-page":"112408","DOI":"10.1016\/j.measurement.2022.112408","article-title":"A motor bearing fault voiceprint recognition method based on Mel-CNN model","volume":"207","author":"Shan","year":"2023","journal-title":"Measurement"},{"key":"ref29","doi-asserted-by":"crossref","first-page":"608","DOI":"10.3390\/machines12090608","article-title":"Lightweight network bearing intelligent fault diagnosis based on VMD-FK-ShuffleNetV2","volume":"12","author":"Jiang","year":"2024","journal-title":"Machines"},{"key":"ref30","doi-asserted-by":"crossref","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 Syst Appl"},{"key":"ref31","doi-asserted-by":"crossref","first-page":"35141","DOI":"10.1109\/JSEN.2024.3458409","article-title":"A Gramian angular field for constructing graph-based GNNs and its applications in rolling bearing defect detection","volume":"24","author":"Li","year":"2024","journal-title":"IEEE Sens J"},{"key":"ref32","first-page":"1","article-title":"LAFICNN: a novel convolutional adaptive fusion framework for fault diagnosis of rotating machinery","volume":"73","author":"Yu","year":"2024","journal-title":"IEEE Trans Instrum Meas"},{"key":"ref33","doi-asserted-by":"crossref","first-page":"46122","DOI":"10.1088\/1361-6501\/ad1d4a","article-title":"A new model for bearing fault diagnosis based on mutual mapping of signals and images and sparse representation","volume":"35","author":"Yang","year":"2024","journal-title":"Meas Sci Technol"}],"container-title":["Computers, Materials & Continua"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/cdn.techscience.cn\/files\/cmc\/2025\/TSP_CMC-82-3\/TSP_CMC_59295\/TSP_CMC_59295.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,14]],"date-time":"2025-11-14T06:30:04Z","timestamp":1763101804000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.techscience.com\/cmc\/v82n3\/59893"}},"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.2025.059295","relation":{},"ISSN":["1546-2226"],"issn-type":[{"value":"1546-2226","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"2024-10-03","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-12-16","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-03-06","order":2,"name":"published","label":"Published Online","group":{"name":"publication_history","label":"Publication History"}}]}}