{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,23]],"date-time":"2026-05-23T22:07:00Z","timestamp":1779574020370,"version":"3.53.1"},"reference-count":85,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Neural Networks"],"published-print":{"date-parts":[[2026,11]]},"DOI":"10.1016\/j.neunet.2026.109143","type":"journal-article","created":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T15:16:48Z","timestamp":1779203808000},"page":"109143","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Heterogeneous neural blind deconvolution: A signal processing-empowered foundation feature extractor for bearing fault diagnosis"],"prefix":"10.1016","volume":"203","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1880-2621","authenticated-orcid":false,"given":"Jing-Xiao","family":"Liao","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4666-377X","authenticated-orcid":false,"given":"Chao","family":"He","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jipu","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiao-Cong","family":"Zhong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinwei","family":"Sun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yiu-ming","family":"Cheung","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Feng-Lei","family":"Fan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9329-8894","authenticated-orcid":false,"given":"Shiping","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6831-3175","authenticated-orcid":false,"given":"Xiaoge","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.neunet.2026.109143_bib0001","doi-asserted-by":"crossref","first-page":"248","DOI":"10.1016\/j.ymssp.2017.01.011","article-title":"Fast computation of the spectral correlation","volume":"92","author":"Antoni","year":"2017","journal-title":"Mechanical Systems and Signal Processing"},{"key":"10.1016\/j.neunet.2026.109143_bib0002","doi-asserted-by":"crossref","first-page":"569","DOI":"10.1016\/j.jsv.2018.06.055","article-title":"Blind deconvolution based on cyclostationarity maximization and its application to fault identification","volume":"432","author":"Buzzoni","year":"2018","journal-title":"Journal of Sound and Vibration"},{"key":"10.1016\/j.neunet.2026.109143_bib0003","doi-asserted-by":"crossref","first-page":"186","DOI":"10.1016\/j.jmsy.2021.11.016","article-title":"Unsupervised domain-share CNN for machine fault transfer diagnosis from steady speeds to time-varying speeds","volume":"62","author":"Cao","year":"2022","journal-title":"The Journal of Manufacturing Systems"},{"key":"10.1016\/j.neunet.2026.109143_bib0004","doi-asserted-by":"crossref","first-page":"103348","DOI":"10.1109\/ACCESS.2024.3430010","article-title":"Enhancing reliability through interpretability: A comprehensive survey of interpretable intelligent fault diagnosis in rotating machinery","volume":"12","author":"Chen","year":"2024","journal-title":"IEEE Access"},{"key":"10.1016\/j.neunet.2026.109143_bib0005","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":"Mechanical Systems and Signal Processing"},{"issue":"5","key":"10.1016\/j.neunet.2026.109143_bib0006","doi-asserted-by":"crossref","first-page":"3064","DOI":"10.1109\/TPAMI.2023.3339211","article-title":"A hybrid neural coding approach for pattern recognition with spiking neural networks","volume":"46","author":"Chen","year":"2024","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"10.1016\/j.neunet.2026.109143_bib0007","doi-asserted-by":"crossref","DOI":"10.1016\/j.jsv.2019.114900","article-title":"A novel blind deconvolution method and its application to fault identification","volume":"460","author":"Cheng","year":"2019","journal-title":"Journal of Sound and Vibration"},{"key":"10.1016\/j.neunet.2026.109143_bib0008","first-page":"4479","article-title":"Fast fourier convolution","volume":"33","author":"Chi","year":"2020","journal-title":"Advances in Neural Information Processing Systems"},{"issue":"3","key":"10.1016\/j.neunet.2026.109143_bib0009","doi-asserted-by":"crossref","first-page":"531","DOI":"10.1109\/TSP.2013.2288675","article-title":"Variational mode decomposition","volume":"62","author":"Dragomiretskiy","year":"2013","journal-title":"IIEEE Transactions on Signal Processing"},{"issue":"6","key":"10.1016\/j.neunet.2026.109143_bib0010","doi-asserted-by":"crossref","first-page":"2035","DOI":"10.1109\/TMI.2019.2963248","article-title":"Quadratic autoencoder (q-AE) for low-dose CT denoising","volume":"39","author":"Fan","year":"2019","journal-title":"IEEE Transactions on Medical Imaging"},{"key":"10.1016\/j.neunet.2026.109143_bib0011","doi-asserted-by":"crossref","first-page":"383","DOI":"10.1016\/j.neunet.2020.01.007","article-title":"Universal approximation with quadratic deep networks","volume":"124","author":"Fan","year":"2020","journal-title":"Neural Networks"},{"issue":"11","key":"10.1016\/j.neunet.2026.109143_bib0012","doi-asserted-by":"crossref","first-page":"9702","DOI":"10.1109\/TPAMI.2025.3588894","article-title":"One neuron saved is one neuron earned: On parametric efficiency of quadratic networks","volume":"47","author":"Fan","year":"2025","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"10.1016\/j.neunet.2026.109143_bib0013","article-title":"Minimum noise amplitude deconvolution and its application in repetitive impact detection","author":"Fang","year":"2022","journal-title":"Structural Health Monitoring"},{"key":"10.1016\/j.neunet.2026.109143_bib0014","first-page":"1","article-title":"Clformer: A lightweight transformer based on convolutional embedding and linear self-attention with strong robustness for bearing fault diagnosis under limited sample conditions","volume":"71","author":"Fang","year":"2021","journal-title":"IEEE Transactions on Instrumentation and Measurement"},{"key":"10.1016\/j.neunet.2026.109143_bib0015","first-page":"1","article-title":"Lefe-net: A lightweight efficient feature extraction network with strong robustness for bearing fault diagnosis","volume":"70","author":"Fang","year":"2021","journal-title":"IEEE Transactions on Instrumentation and Measurement"},{"key":"10.1016\/j.neunet.2026.109143_bib0016","unstructured":"Fu, C., Chen, P., Shen, Y., Qin, Y., Zhang, M., Lin, X., Yang, J., Zheng, X., Li, K., Sun, X. et al. (2023). Mme: A comprehensive evaluation benchmark for multimodal large language models. arXiv preprint arXiv: 2306.13394."},{"issue":"59","key":"10.1016\/j.neunet.2026.109143_bib0017","first-page":"1","article-title":"Domain-adversarial training of neural networks","volume":"17","author":"Ganin","year":"2016","journal-title":"Journal of Machine Learning Research"},{"key":"10.1016\/j.neunet.2026.109143_bib0018","series-title":"Advances in neural information processing systems","article-title":"Optimal kernel choice for large-scale two-sample tests","volume":"vol. 25","author":"Gretton","year":"2012"},{"key":"10.1016\/j.neunet.2026.109143_bib0019","doi-asserted-by":"crossref","DOI":"10.1016\/j.neunet.2025.107392","article-title":"Structure information preserving domain adaptation network for fault diagnosis of sucker rod pumping systems","volume":"188","author":"Gu","year":"2025","journal-title":"Neural Networks"},{"key":"10.1016\/j.neunet.2026.109143_bib0020","doi-asserted-by":"crossref","DOI":"10.1016\/j.aei.2024.102731","article-title":"Adaptive maximum generalized gaussian cyclostationarity blind deconvolution for the early fault diagnosis of high-speed train bearings under non-gaussian noise","volume":"62","author":"Han","year":"2024","journal-title":"Advanced Engineering Informatics"},{"key":"10.1016\/j.neunet.2026.109143_bib0021","series-title":"Adaptive filter theory","author":"Haykin","year":"1986"},{"key":"10.1016\/j.neunet.2026.109143_bib0022","doi-asserted-by":"crossref","DOI":"10.1016\/j.aei.2024.102568","article-title":"Interpretable modulated differentiable STFT and physics-informed balanced spectrum metric for freight train wheelset bearing cross-machine transfer fault diagnosis under speed fluctuations","volume":"62","author":"He","year":"2024","journal-title":"Advanced Engineering Informatics"},{"key":"10.1016\/j.neunet.2026.109143_bib0023","doi-asserted-by":"crossref","DOI":"10.1016\/j.jii.2026.101068","article-title":"Prior knowledge-embedded first-layer interpretable paradigm for rail transit vehicle human\u2013computer collaboration fault monitoring","author":"He","year":"2026","journal-title":"Journal of Industrial Information Integration"},{"key":"10.1016\/j.neunet.2026.109143_bib0024","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2024.111499","article-title":"Interpretable physics-informed domain adaptation paradigm for cross-machine transfer diagnosis","author":"He","year":"2024","journal-title":"Knowledge-Based Systems"},{"key":"10.1016\/j.neunet.2026.109143_bib0025","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":"The Journal of Manufacturing Systems"},{"key":"10.1016\/j.neunet.2026.109143_bib0026","unstructured":"Hinton, G., Vinyals, O., & Dean, J. (2015). Distilling the knowledge in a neural network. arXiv preprint arXiv: 1503.02531."},{"key":"10.1016\/j.neunet.2026.109143_bib0027","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2022.105794","article-title":"Gtfe-net: A gramian time frequency enhancement cnn for bearing fault diagnosis","volume":"119","author":"Jia","year":"2023","journal-title":"Engineering Applications of Artificial Intelligence"},{"key":"10.1016\/j.neunet.2026.109143_bib0028","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":"Mechanical Systems and Signal Processing"},{"key":"10.1016\/j.neunet.2026.109143_bib0029","series-title":"Phm society european conference","article-title":"Condition monitoring of bearing damage in electromechanical drive systems by using motor current signals of electric motors: A benchmark data set for data-driven classification","volume":"vol. 3","author":"Lessmeier","year":"2016"},{"issue":"6","key":"10.1016\/j.neunet.2026.109143_bib0030","doi-asserted-by":"crossref","first-page":"8013","DOI":"10.3390\/s130608013","article-title":"Sequential fuzzy diagnosis method for motor roller bearing in variable operating conditions based on vibration analysis","volume":"13","author":"Li","year":"2013","journal-title":"Sensors"},{"issue":"4","key":"10.1016\/j.neunet.2026.109143_bib0031","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 Transactions on Systems, Man, and Cybernetics"},{"key":"10.1016\/j.neunet.2026.109143_bib0032","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1016\/j.sigpro.2018.12.005","article-title":"Multi-layer domain adaptation method for rolling bearing fault diagnosis","volume":"157","author":"Li","year":"2019","journal-title":"Signal Processing"},{"key":"10.1016\/j.neunet.2026.109143_bib0033","doi-asserted-by":"crossref","DOI":"10.1016\/j.aei.2024.103003","article-title":"Residual attention guided vision transformer with acoustic-vibration signal feature fusion for cross-domain fault diagnosis","volume":"64","author":"Lian","year":"2025","journal-title":"Advanced Engineering Informatics"},{"key":"10.1016\/j.neunet.2026.109143_bib0034","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TIM.2023.3326161","article-title":"Attention-embedded quadratic network (qttention) for effective and interpretable bearing fault diagnosis","volume":"72","author":"Liao","year":"2023","journal-title":"IEEE Transactions on Instrumentation and Measurement"},{"key":"10.1016\/j.neunet.2026.109143_bib0035","doi-asserted-by":"crossref","DOI":"10.1016\/j.ymssp.2024.111750","article-title":"Classifier-guided neural blind deconvolution: A physics-informed denoising module for bearing fault diagnosis under noisy conditions","volume":"222","author":"Liao","year":"2025","journal-title":"Mechanical Systems and Signal Processing"},{"key":"10.1016\/j.neunet.2026.109143_bib0036","first-page":"1","article-title":"Quadratic neuron-empowered heterogeneous autoencoder for unsupervised anomaly detection","author":"Liao","year":"2024","journal-title":"IEEE Transactions on Artificial Intelligence"},{"key":"10.1016\/j.neunet.2026.109143_bib0037","first-page":"1","article-title":"BearingPGA-net: A lightweight and deployable bearing fault diagnosis network via decoupled knowledge distillation and FPGA acceleration","volume":"73","author":"Liao","year":"2024","journal-title":"IEEE Transactions on Instrumentation and Measurement"},{"key":"10.1016\/j.neunet.2026.109143_bib0038","doi-asserted-by":"crossref","DOI":"10.1016\/j.neunet.2024.106400","article-title":"A grid fault diagnosis framework based on adaptive integrated decomposition and cross-modal attention fusion","volume":"178","author":"Liu","year":"2024","journal-title":"Neural Networks"},{"key":"10.1016\/j.neunet.2026.109143_bib0039","series-title":"International conference on machine learning","first-page":"97","article-title":"Learning transferable features with deep adaptation networks","author":"Long","year":"2015"},{"key":"10.1016\/j.neunet.2026.109143_bib0040","series-title":"International conference on machine learning","first-page":"2208","article-title":"Deep transfer learning with joint adaptation networks","author":"Long","year":"2017"},{"key":"10.1016\/j.neunet.2026.109143_bib0041","unstructured":"Loshchilov, I., & Hutter, F. (2016). SGDR: Stochastic gradient descent with warm restarts. arXiv preprint arXiv: 1608.03983."},{"key":"10.1016\/j.neunet.2026.109143_bib0042","doi-asserted-by":"crossref","DOI":"10.1016\/j.aei.2024.102787","article-title":"Envelope spectrum neural network with adaptive domain weight harmonization for intelligent bearing fault diagnosis under cross-machine scenarios","volume":"62","author":"Lu","year":"2024","journal-title":"Advanced Engineering Informatics"},{"key":"10.1016\/j.neunet.2026.109143_bib0043","doi-asserted-by":"crossref","DOI":"10.1016\/j.aei.2024.102909","article-title":"Explainable and interpretable bearing fault classification and diagnosis under limited data","volume":"62","author":"Magad\u00e1n","year":"2024","journal-title":"Advanced Engineering Informatics"},{"key":"10.1016\/j.neunet.2026.109143_bib0044","doi-asserted-by":"crossref","first-page":"461","DOI":"10.1016\/j.ymssp.2016.05.036","article-title":"Multipoint optimal minimum entropy deconvolution and convolution fix: Application to vibration fault detection","volume":"82","author":"McDonald","year":"2017","journal-title":"Mechanical Systems and Signal Processing"},{"key":"10.1016\/j.neunet.2026.109143_bib0045","doi-asserted-by":"crossref","DOI":"10.1016\/j.ymssp.2021.108202","article-title":"A review on the application of blind deconvolution in machinery fault diagnosis","volume":"163","author":"Miao","year":"2022","journal-title":"Mechanical Systems and Signal Processing"},{"key":"10.1016\/j.neunet.2026.109143_bib0046","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1016\/j.ymssp.2017.01.033","article-title":"Application of an improved maximum correlated kurtosis deconvolution method for fault diagnosis of rolling element bearings","volume":"92","author":"Miao","year":"2017","journal-title":"Mechanical Systems and Signal Processing"},{"key":"10.1016\/j.neunet.2026.109143_bib0047","unstructured":"Michelucci, U. (2022). An introduction to autoencoders. arXiv::2201.03898."},{"key":"10.1016\/j.neunet.2026.109143_bib0048","doi-asserted-by":"crossref","DOI":"10.1016\/j.ymssp.2023.110796","article-title":"Uncertainty quantification in machine learning for engineering design and health prognostics: A tutorial","volume":"205","author":"Nemani","year":"2023","journal-title":"Mechanical Systems and Signal Processing"},{"key":"10.1016\/j.neunet.2026.109143_bib0049","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1016\/j.ins.2022.11.085","article-title":"A hierarchical fused fuzzy deep neural network with heterogeneous network embedding for recommendation","volume":"620","author":"Pham","year":"2023","journal-title":"Information Science"},{"key":"10.1016\/j.neunet.2026.109143_bib0050","doi-asserted-by":"crossref","first-page":"336","DOI":"10.1016\/j.neucom.2021.01.133","article-title":"A new heterogeneous neural network model and its application in image enhancement","volume":"440","author":"Qi","year":"2021","journal-title":"Neurocomputing"},{"key":"10.1016\/j.neunet.2026.109143_bib0051","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2023.110748","article-title":"Maximum mean square discrepancy: A new discrepancy representation metric for mechanical fault transfer diagnosis","volume":"276","author":"Qian","year":"2023","journal-title":"Knowledge-Based Systems"},{"key":"10.1016\/j.neunet.2026.109143_bib0052","doi-asserted-by":"crossref","unstructured":"Qin, D., Leichner, C., Delakis, M., Fornoni, M., Luo, S., Yang, F., Wang, W., Banbury, C., Ye, C., Akin, B., Aggarwal, V., Zhu, T., Moro, D., & Howard, A. (2024a). MobileNetv4 \u2013 universal models for the mobile ecosystem. arXiv: 2404.10518.","DOI":"10.1007\/978-3-031-73661-2_5"},{"key":"10.1016\/j.neunet.2026.109143_bib0053","doi-asserted-by":"crossref","DOI":"10.1016\/j.neunet.2026.108561","article-title":"A novel cross-domain fault diagnosis method for multi-condition industrial processes based on meta-domain adaptation with progressive meta-learning","author":"Qin","year":"2026","journal-title":"Neural Networks"},{"key":"10.1016\/j.neunet.2026.109143_bib0054","first-page":"1","article-title":"Large model for rotating machine fault diagnosis based on a dense connection network with depthwise separable convolution","volume":"73","author":"Qin","year":"2024","journal-title":"IEEE Transactions on Instrumentation and Measurement"},{"issue":"2","key":"10.1016\/j.neunet.2026.109143_bib0055","doi-asserted-by":"crossref","first-page":"485","DOI":"10.1016\/j.ymssp.2010.07.017","article-title":"Rolling element bearing diagnostics-a tutorial","volume":"25","author":"Randall","year":"2011","journal-title":"Mechanical Systems and Signal Processing"},{"key":"10.1016\/j.neunet.2026.109143_bib0056","series-title":"Industrial, aerospace and automotive applications","first-page":"13","author":"Randall","year":"2011"},{"key":"10.1016\/j.neunet.2026.109143_bib0057","unstructured":"Ruder, S. (2016). An overview of gradient descent optimization algorithms. arXiv preprint arXiv: 1609.04747."},{"issue":"1","key":"10.1016\/j.neunet.2026.109143_bib0058","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1090\/S0273-0979-1981-14858-8","article-title":"The fundamental theorem of algebra and complexity theory","volume":"4","author":"Smale","year":"1981","journal-title":"Bulletin (new series) of the American Mathematical Society\/Bulletin, new series, of the American Mathematical Society"},{"key":"10.1016\/j.neunet.2026.109143_bib0059","series-title":"European conference on computer vision workshops","first-page":"443","article-title":"Deep coral: Correlation alignment for deep domain adaptation","author":"Sun","year":"2016"},{"key":"10.1016\/j.neunet.2026.109143_bib0060","first-page":"1","article-title":"Deep learning-based bearing fault diagnosis using a trusted multiscale quadratic attention-embedded convolutional neural network","volume":"73","author":"Tang","year":"2024","journal-title":"IEEE Transactions on Instrumentation and Measurement"},{"key":"10.1016\/j.neunet.2026.109143_bib0061","article-title":"A continual test-time domain adaptation method for online machinery fault diagnosis under dynamic operating conditions","author":"Tian","year":"2025","journal-title":"Neural Networks"},{"issue":"8","key":"10.1016\/j.neunet.2026.109143_bib0062","doi-asserted-by":"crossref","first-page":"4863","DOI":"10.1109\/TSMC.2024.3389068","article-title":"Physically interpretable wavelet-guided networks with dynamic frequency decomposition for machine intelligence fault prediction","volume":"54","author":"Wang","year":"2024","journal-title":"IEEE Transactions on Systems, Man, and Cybernetics: Systems"},{"issue":"9","key":"10.1016\/j.neunet.2026.109143_bib0063","doi-asserted-by":"crossref","first-page":"4757","DOI":"10.1109\/TNNLS.2021.3060494","article-title":"Feature-level attention-guided multitask CNN for fault diagnosis and working conditions identification of rolling bearing","volume":"33","author":"Wang","year":"2022","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"10.1016\/j.neunet.2026.109143_bib0064","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":"Mechanical Systems and Signal Processing"},{"key":"10.1016\/j.neunet.2026.109143_bib0065","doi-asserted-by":"crossref","DOI":"10.1016\/j.ymssp.2021.108018","article-title":"Bearing fault diagnosis method based on adaptive maximum cyclostationarity blind deconvolution","volume":"162","author":"Wang","year":"2022","journal-title":"Mechanical Systems and Signal Processing"},{"issue":"1-2","key":"10.1016\/j.neunet.2026.109143_bib0066","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1016\/0016-7142(78)90005-4","article-title":"Minimum entropy deconvolution","volume":"16","author":"Wiggins","year":"1978","journal-title":"Geoexploration"},{"key":"10.1016\/j.neunet.2026.109143_bib0067","doi-asserted-by":"crossref","DOI":"10.1016\/j.neunet.2025.107699","article-title":"Transfer learning-motivated intelligent fault diagnosis framework for cross-domain knowledge distillation","volume":"190","author":"Wu","year":"2025","journal-title":"Neural Networks"},{"key":"10.1016\/j.neunet.2026.109143_bib0068","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2021.107936","article-title":"A survey on heterogeneous network representation learning","volume":"116","author":"Xie","year":"2021","journal-title":"Pattern Recognition"},{"key":"10.1016\/j.neunet.2026.109143_bib0069","first-page":"503","article-title":"Quadralib: A performant quadratic neural network library for architecture optimization and design exploration","volume":"4","author":"Xu","year":"2022","journal-title":"Proceedings of Machine Learning and Systems"},{"key":"10.1016\/j.neunet.2026.109143_bib0070","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2023.121338","article-title":"Liconvformer: A lightweight fault diagnosis framework using separable multiscale convolution and broadcast self-attention","volume":"237","author":"Yan","year":"2024","journal-title":"Expert Systems with Applications"},{"key":"10.1016\/j.neunet.2026.109143_bib0071","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1016\/j.neunet.2021.04.003","article-title":"Residual wide-kernel deep convolutional auto-encoder for intelligent rotating machinery fault diagnosis with limited samples","volume":"141","author":"Yang","year":"2021","journal-title":"Neural Networks"},{"key":"10.1016\/j.neunet.2026.109143_bib0072","doi-asserted-by":"crossref","DOI":"10.1016\/j.jii.2026.101076","article-title":"Semi-supervised cross-domain fault diagnosis via contrastive pre-training and annotation-efficient alignment strategy","volume":"50","author":"Yang","year":"2026","journal-title":"Journal of Industrial Information Integration"},{"key":"10.1016\/j.neunet.2026.109143_bib0073","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1016\/j.neunet.2017.07.002","article-title":"Error bounds for approximations with deep reLU networks","volume":"94","author":"Yarotsky","year":"2017","journal-title":"Neural Networks"},{"key":"10.1016\/j.neunet.2026.109143_bib0074","series-title":"Advances in neural information processing systems","first-page":"72761","article-title":"Deep fractional fourier transform","volume":"vol. 36","author":"Yu","year":"2023"},{"key":"10.1016\/j.neunet.2026.109143_bib0075","first-page":"1","article-title":"A class-aware supervised contrastive quadratic neural network for imbalanced bearing fault diagnosis","author":"Yu","year":"2026","journal-title":"IEEE Transactions on Reliability"},{"key":"10.1016\/j.neunet.2026.109143_bib0076","doi-asserted-by":"crossref","DOI":"10.1016\/j.neunet.2024.106518","article-title":"A two-stage importance-aware subgraph convolutional network based on multi-source sensors for cross-domain fault diagnosis","volume":"179","author":"Yu","year":"2024","journal-title":"Neural Networks"},{"issue":"2","key":"10.1016\/j.neunet.2026.109143_bib0077","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":"10.1016\/j.neunet.2026.109143_bib0078","series-title":"IEEE Conference on computer vision and pattern recognition","first-page":"11953","article-title":"Decoupled knowledge distillation","author":"Zhao","year":"2022"},{"key":"10.1016\/j.neunet.2026.109143_bib0079","article-title":"Domain generalization for cross-domain fault diagnosis: An application-oriented perspective and a benchmark study","author":"Zhao","year":"2024","journal-title":"Reliability Engineering & System Safety"},{"issue":"7","key":"10.1016\/j.neunet.2026.109143_bib0080","doi-asserted-by":"crossref","first-page":"4681","DOI":"10.1109\/TII.2019.2943898","article-title":"Deep residual shrinkage networks for fault diagnosis","volume":"16","author":"Zhao","year":"2020","journal-title":"IEEE Transactions on Industrial Informatics"},{"key":"10.1016\/j.neunet.2026.109143_bib0081","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1016\/j.ymssp.2018.05.050","article-title":"Deep learning and its applications to machine health monitoring","volume":"115","author":"Zhao","year":"2019","journal-title":"Mechanical Systems and Signal Processing"},{"key":"10.1016\/j.neunet.2026.109143_bib0082","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 Transactions"},{"key":"10.1016\/j.neunet.2026.109143_bib0083","first-page":"1","article-title":"Applications of unsupervised deep transfer learning to intelligent fault diagnosis: A survey and comparative study","volume":"70","author":"Zhao","year":"2021","journal-title":"IEEE Transactions on Instrumentation and Measurement"},{"key":"10.1016\/j.neunet.2026.109143_bib0084","doi-asserted-by":"crossref","DOI":"10.1016\/j.comnet.2019.107049","article-title":"Helad: A novel network anomaly detection model based on heterogeneous ensemble learning","volume":"169","author":"Zhong","year":"2020","journal-title":"Computer Networks"},{"issue":"11","key":"10.1016\/j.neunet.2026.109143_bib0085","doi-asserted-by":"crossref","first-page":"10842","DOI":"10.1109\/TII.2023.3241587","article-title":"Trustworthy fault diagnosis with uncertainty estimation through evidential convolutional neural networks","volume":"19","author":"Zhou","year":"2023","journal-title":"IEEE Transactions on Industrial Informatics"}],"container-title":["Neural Networks"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0893608026006040?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0893608026006040?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,5,23]],"date-time":"2026-05-23T21:57:57Z","timestamp":1779573477000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0893608026006040"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,11]]},"references-count":85,"alternative-id":["S0893608026006040"],"URL":"https:\/\/doi.org\/10.1016\/j.neunet.2026.109143","relation":{},"ISSN":["0893-6080"],"issn-type":[{"value":"0893-6080","type":"print"}],"subject":[],"published":{"date-parts":[[2026,11]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Heterogeneous neural blind deconvolution: A signal processing-empowered foundation feature extractor for bearing fault diagnosis","name":"articletitle","label":"Article Title"},{"value":"Neural Networks","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.neunet.2026.109143","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":"109143"}}