{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T01:59:00Z","timestamp":1776131940968,"version":"3.50.1"},"reference-count":44,"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\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["52075031"],"award-info":[{"award-number":["52075031"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012165","name":"Key Technologies Research and Development Program","doi-asserted-by":"publisher","award":["2021YFA1003503"],"award-info":[{"award-number":["2021YFA1003503"]}],"id":[{"id":"10.13039\/501100012165","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Advanced Engineering Informatics"],"published-print":{"date-parts":[[2026,9]]},"DOI":"10.1016\/j.aei.2026.104623","type":"journal-article","created":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T17:47:35Z","timestamp":1774547255000},"page":"104623","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"PA","title":["DTFN: An interpretable differentiable time-frequency network for rolling bearing fault diagnosis with few samples"],"prefix":"10.1016","volume":"74","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8333-0037","authenticated-orcid":false,"given":"Guangyi","family":"Chen","sequence":"first","affiliation":[]},{"given":"Xi","family":"Li","sequence":"additional","affiliation":[]},{"given":"Gang","family":"Tang","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.aei.2026.104623_b0005","doi-asserted-by":"crossref","DOI":"10.1109\/TIM.2023.3318735","article-title":"Domain adaptation with multi-adversarial learning for open-set cross-domain intelligent bearing fault diagnosis","volume":"72","author":"Zhu","year":"2023","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"10.1016\/j.aei.2026.104623_b0010","doi-asserted-by":"crossref","DOI":"10.1016\/j.aei.2023.102262","article-title":"Imbalanced domain generalization via Semantic-Discriminative augmentation for intelligent fault diagnosis","volume":"59","author":"Zhao","year":"2024","journal-title":"Adv. Eng. Inform."},{"key":"10.1016\/j.aei.2026.104623_b0015","doi-asserted-by":"crossref","DOI":"10.1016\/j.ymssp.2023.110544","article-title":"Physics-Informed Residual Network (PIResNet) for rolling element bearing fault diagnostics","volume":"200","author":"Ni","year":"2023","journal-title":"Mech. Syst. Sig. Process."},{"key":"10.1016\/j.aei.2026.104623_b0020","doi-asserted-by":"crossref","DOI":"10.1016\/j.ymssp.2022.109995","article-title":"Multistate fault diagnosis strategy for bearings based on an improved convolutional sparse coding with priori periodic filter group","volume":"188","author":"Han","year":"2023","journal-title":"Mech. Syst. Sig. Process."},{"key":"10.1016\/j.aei.2026.104623_b0025","article-title":"A systematic review of diagnosis methods for rolling bearing compound faults: research status, challenges, and future prospects","volume":"36","author":"Li","year":"2025","journal-title":"Meas. Sci. Technol."},{"key":"10.1016\/j.aei.2026.104623_b0030","doi-asserted-by":"crossref","DOI":"10.1088\/1361-6501\/ace98a","article-title":"Rotating machinery fault diagnosis based on optimized Hilbert curve images and a novel bi-channel CNN with attention mechanism","volume":"34","author":"Sun","year":"2023","journal-title":"Meas. Sci. Technol."},{"key":"10.1016\/j.aei.2026.104623_b0035","doi-asserted-by":"crossref","DOI":"10.1016\/j.ymssp.2019.106587","article-title":"Applications of machine learning to machine fault diagnosis: a review and roadmap","volume":"138","author":"Lei","year":"2020","journal-title":"Mech. Syst. Sig. Process."},{"key":"10.1016\/j.aei.2026.104623_b0040","doi-asserted-by":"crossref","first-page":"1271","DOI":"10.1007\/s10845-020-01608-8","article-title":"A novel normalized recurrent neural network for fault diagnosis with noisy labels","volume":"32","author":"Nie","year":"2021","journal-title":"J. Intell. Manuf."},{"key":"10.1016\/j.aei.2026.104623_b0045","doi-asserted-by":"crossref","first-page":"5768","DOI":"10.1109\/JSEN.2022.3146151","article-title":"Rolling bearing fault severity recognition via data mining integrated with convolutional neural network","volume":"22","author":"Liu","year":"2022","journal-title":"IEEE Sens. J."},{"key":"10.1016\/j.aei.2026.104623_b0050","doi-asserted-by":"crossref","first-page":"6339","DOI":"10.1109\/TNNLS.2021.3135877","article-title":"Intelligent fault diagnosis of gearbox under variable working conditions with adaptive intraclass and interclass convolutional neural network","volume":"34","author":"Zhao","year":"2023","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"10.1016\/j.aei.2026.104623_b0055","doi-asserted-by":"crossref","DOI":"10.1109\/TIM.2022.3196742","article-title":"Multiscale residual attention convolutional neural network for bearing fault diagnosis","volume":"71","author":"Jia","year":"2022","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"10.1016\/j.aei.2026.104623_b0060","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2022.104713","article-title":"Intelligent fault diagnosis of train axle box bearing based on parameter optimization VMD and improved DBN","volume":"110","author":"Jin","year":"2022","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.aei.2026.104623_b0065","doi-asserted-by":"crossref","first-page":"439","DOI":"10.1016\/j.ymssp.2017.06.022","article-title":"A deep convolutional neural network with new training methods for bearing fault diagnosis under noisy environment and different working load","volume":"100","author":"Zhang","year":"2018","journal-title":"Mech. Syst. Sig. Process."},{"key":"10.1016\/j.aei.2026.104623_b0070","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2023.120696","article-title":"Generalized MAML for few-shot cross-domain fault diagnosis of bearing driven by heterogeneous signals","volume":"230","author":"Lin","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.aei.2026.104623_b0075","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1016\/j.isatra.2021.03.042","article-title":"A new dynamic model and transfer learning based intelligent fault diagnosis framework for rolling element bearings race faults: solving the small sample problem","volume":"121","author":"Dong","year":"2022","journal-title":"ISA Trans."},{"key":"10.1016\/j.aei.2026.104623_b0080","doi-asserted-by":"crossref","first-page":"10130","DOI":"10.1109\/TIE.2020.3028821","article-title":"A small sample focused intelligent fault diagnosis scheme of machines via multimodules learning with gradient penalized generative adversarial networks","volume":"68","author":"Zhang","year":"2021","journal-title":"IEEE Trans. Ind. Electron."},{"key":"10.1016\/j.aei.2026.104623_b0085","doi-asserted-by":"crossref","DOI":"10.1109\/TIM.2021.3119135","article-title":"Conditional GAN and 2-D CNN for Bearing Fault Diagnosis with Small Samples","volume":"70","author":"Yang","year":"2021","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"10.1016\/j.aei.2026.104623_b0090","doi-asserted-by":"crossref","first-page":"433","DOI":"10.1007\/s10845-018-1456-1","article-title":"Intelligent rotating machinery fault diagnosis based on deep learning using data augmentation","volume":"31","author":"Li","year":"2020","journal-title":"J. Intell. Manuf."},{"key":"10.1016\/j.aei.2026.104623_b0095","doi-asserted-by":"crossref","DOI":"10.1016\/j.ymssp.2021.108765","article-title":"A novel method based on meta-learning for bearing fault diagnosis with small sample learning under different working conditions","volume":"169","author":"Su","year":"2022","journal-title":"Mech. Syst. Sig. Process."},{"key":"10.1016\/j.aei.2026.104623_b0100","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1016\/j.neucom.2021.01.099","article-title":"Meta-learning for few-shot bearing fault diagnosis under complex working conditions","volume":"439","author":"Li","year":"2021","journal-title":"Neurocomputing"},{"key":"10.1016\/j.aei.2026.104623_b0105","doi-asserted-by":"crossref","first-page":"383","DOI":"10.1016\/j.isatra.2021.03.013","article-title":"Semi-supervised meta-learning networks with squeeze-and-excitation attention for few-shot fault diagnosis","volume":"120","author":"Feng","year":"2022","journal-title":"ISA Trans."},{"key":"10.1016\/j.aei.2026.104623_b0110","doi-asserted-by":"crossref","DOI":"10.1016\/j.ymssp.2022.109628","article-title":"Bayesian transfer learning with active querying for intelligent cross-machine fault prognosis under limited data","volume":"183","author":"Zhu","year":"2023","journal-title":"Mech. Syst. Sig. Process."},{"key":"10.1016\/j.aei.2026.104623_b0115","doi-asserted-by":"crossref","first-page":"5760","DOI":"10.1109\/TII.2021.3103412","article-title":"Simulation-Driven Domain Adaptation for Rolling Element Bearing Fault Diagnosis","volume":"18","author":"Liu","year":"2022","journal-title":"IEEE Trans. Ind. Inform."},{"key":"10.1016\/j.aei.2026.104623_b0120","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2023.107031","article-title":"C-ECAFormer: a new lightweight fault diagnosis framework towards heavy noise and small samples","volume":"126","author":"Wang","year":"2023","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.aei.2026.104623_b0125","doi-asserted-by":"crossref","first-page":"208","DOI":"10.1016\/j.neucom.2020.04.143","article-title":"Fault detection and identification of rolling element bearings with Attentive Dense CNN","volume":"405","author":"Plakias","year":"2020","journal-title":"Neurocomputing"},{"key":"10.1016\/j.aei.2026.104623_b0130","doi-asserted-by":"crossref","first-page":"422","DOI":"10.1038\/s42254-021-00314-5","article-title":"Physics-informed machine learning","volume":"3","author":"Karniadakis","year":"2021","journal-title":"Nat. Rev. Phys."},{"key":"10.1016\/j.aei.2026.104623_b0135","doi-asserted-by":"crossref","first-page":"31425","DOI":"10.1109\/JSEN.2023.3328007","article-title":"Multiscale residual antinoise network via interpretable dynamic recalibration mechanism for rolling bearing fault diagnosis with few samples","volume":"23","author":"Liu","year":"2023","journal-title":"IEEE Sens. J."},{"key":"10.1016\/j.aei.2026.104623_b0140","doi-asserted-by":"crossref","first-page":"2302","DOI":"10.1109\/TSMC.2020.3048950","article-title":"WaveletKernelNet: an interpretable deep neural network for industrial intelligent diagnosis","volume":"52","author":"Li","year":"2022","journal-title":"IEEE Trans. Syst. Man Cybern. -Syst."},{"key":"10.1016\/j.aei.2026.104623_b0145","doi-asserted-by":"crossref","first-page":"579","DOI":"10.1016\/j.jmsy.2023.08.014","article-title":"Physics-informed interpretable wavelet weight initialization and balanced dynamic adaptive threshold for intelligent fault diagnosis of rolling bearings","volume":"70","author":"He","year":"2023","journal-title":"J. Manuf. Syst."},{"key":"10.1016\/j.aei.2026.104623_b0150","doi-asserted-by":"crossref","first-page":"14974","DOI":"10.1109\/TNNLS.2023.3282599","article-title":"WPConvNet: an interpretable wavelet packet kernel-constrained convolutional network for noise-robust fault diagnosis","volume":"35","author":"Li","year":"2023","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"10.1016\/j.aei.2026.104623_b0155","doi-asserted-by":"crossref","DOI":"10.1016\/j.aei.2024.102705","article-title":"VKCNN: an interpretable variational kernel convolutional neural network for rolling bearing fault diagnosis","volume":"62","author":"Chen","year":"2024","journal-title":"Adv. Eng. Inform."},{"key":"10.1016\/j.aei.2026.104623_b0160","doi-asserted-by":"crossref","DOI":"10.1016\/j.neucom.2023.126257","article-title":"An explainable intelligence fault diagnosis framework for rotating machinery","volume":"541","author":"Yang","year":"2023","journal-title":"Neurocomputing"},{"key":"10.1016\/j.aei.2026.104623_b0165","doi-asserted-by":"crossref","DOI":"10.1016\/j.aei.2024.102421","article-title":"An interpretable multiplication-convolution residual network for equipment fault diagnosis via time-frequency filtering","volume":"60","author":"Liu","year":"2024","journal-title":"Adv. Eng. Inform."},{"key":"10.1016\/j.aei.2026.104623_b0170","doi-asserted-by":"crossref","first-page":"625","DOI":"10.2991\/ijcis.d.210113.001","article-title":"An efficient CNN with tunable input-size for bearing fault diagnosis","volume":"14","author":"Chen","year":"2021","journal-title":"Int. J. Comput. Intell. Syst."},{"key":"10.1016\/j.aei.2026.104623_b0175","series-title":"International Conference on Equipment Intelligent Operation and Maintenance (ICEIOM)","first-page":"640","article-title":"Differentiable Time-Frequency Network for Bearing Fault Diagnosis with Small Samples","author":"Chen","year":"2025"},{"key":"10.1016\/j.aei.2026.104623_b0180","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. Franklin Inst."},{"key":"10.1016\/j.aei.2026.104623_b0185","doi-asserted-by":"crossref","first-page":"1392","DOI":"10.23919\/EUSIPCO55093.2022.9909963","article-title":"A differentiable short-time Fourier transform with respect to the window length","author":"Leiber","year":"2022","journal-title":"30th European Signal Processing Conference (EUSIPCO)"},{"key":"10.1016\/j.aei.2026.104623_b0190","doi-asserted-by":"crossref","DOI":"10.1016\/j.ymssp.2023.110314","article-title":"Interpretable convolutional neural network with multilayer wavelet for Noise-Robust Machinery fault diagnosis","volume":"195","author":"Wang","year":"2023","journal-title":"Mech. Syst. Sig. Process."},{"key":"10.1016\/j.aei.2026.104623_b0195","doi-asserted-by":"crossref","DOI":"10.1016\/j.ymssp.2023.110584","article-title":"Explaining deep neural networks processing raw diagnostic signals","volume":"200","author":"Herwig","year":"2023","journal-title":"Mech. Syst. Sig. Process."},{"key":"10.1016\/j.aei.2026.104623_b0200","doi-asserted-by":"crossref","first-page":"224","DOI":"10.1016\/j.isatra.2020.08.010","article-title":"Deep learning algorithms for rotating machinery intelligent diagnosis: an open source benchmark study","volume":"107","author":"Zhao","year":"2020","journal-title":"ISA Trans."},{"key":"10.1016\/j.aei.2026.104623_b0205","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1016\/j.ymssp.2015.04.021","article-title":"Rolling element bearing diagnostics using the Case Western Reserve University data: a benchmark study","volume":"64\u201365","author":"Smith","year":"2015","journal-title":"Mech. Syst. Sig. Process."},{"key":"10.1016\/j.aei.2026.104623_b0210","doi-asserted-by":"crossref","first-page":"7733","DOI":"10.1109\/TII.2022.3230669","article-title":"Time-varying online transfer learning for intelligent bearing fault diagnosis with incomplete unlabeled target data","volume":"19","author":"Zhou","year":"2023","journal-title":"IEEE Trans. Ind. Inform."},{"key":"10.1016\/j.aei.2026.104623_b0215","doi-asserted-by":"crossref","first-page":"336","DOI":"10.1007\/s11263-019-01228-7","article-title":"Grad-CAM: visual explanations from deep networks via gradient-based localization","volume":"128","author":"Selvaraju","year":"2020","journal-title":"Int. J. Comput. Vis."},{"key":"10.1016\/j.aei.2026.104623_b0220","doi-asserted-by":"crossref","DOI":"10.1016\/j.ymssp.2023.110952","article-title":"TFN: an interpretable neural network with time-frequency transform embedded for intelligent fault diagnosis","volume":"207","author":"Chen","year":"2024","journal-title":"Mech. Syst. Sig. Process."}],"container-title":["Advanced Engineering Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1474034626003150?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1474034626003150?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T01:18:57Z","timestamp":1776129537000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1474034626003150"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9]]},"references-count":44,"alternative-id":["S1474034626003150"],"URL":"https:\/\/doi.org\/10.1016\/j.aei.2026.104623","relation":{},"ISSN":["1474-0346"],"issn-type":[{"value":"1474-0346","type":"print"}],"subject":[],"published":{"date-parts":[[2026,9]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"DTFN: An interpretable differentiable time-frequency network for rolling bearing fault diagnosis with few samples","name":"articletitle","label":"Article Title"},{"value":"Advanced Engineering Informatics","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.aei.2026.104623","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":"104623"}}