{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T12:52:25Z","timestamp":1771678345722,"version":"3.50.1"},"reference-count":27,"publisher":"American Institute of Mathematical Sciences (AIMS)","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["NHM"],"published-print":{"date-parts":[[2023]]},"abstract":"<jats:p xml:lang=\"fr\">&lt;abstract&gt;\n\n&lt;p&gt;Various intelligent methods for condition monitoring and fault diagnosis of mechanical equipment have been developed over the past few years. However, most of the existing deep learning (DL)-based fault diagnosis models perform well only when applied to deal with limited types of general failures, and these models fail to accurately distinguish fine-grained faults under multiple working conditions. To address these challenges, we propose a novel multiscale hybrid model (MSHM), which takes the raw vibration signal as input and progressively learns representative features containing both spatial and temporal information to effectively classify fine-grained faults in an end-to-end way. To simulate fine-grained failure scenarios in practice, more than 100 classes of faults under different working conditions are constructed based on two benchmark datasets, and the experimental results demonstrate that our proposed MSHM has advantages over state-of-the-art methods in terms of accuracy in identifying fine-grained faults, generality in handling fault classes of different granularity, and learning ability with limited data.&lt;\/p&gt;\n\n\t      &lt;\/abstract&gt;<\/jats:p>","DOI":"10.3934\/nhm.2023018","type":"journal-article","created":{"date-parts":[[2023,1,14]],"date-time":"2023-01-14T12:33:00Z","timestamp":1673699580000},"page":"444-462","source":"Crossref","is-referenced-by-count":3,"title":["A novel multiscale hybrid neural network for intelligent fine-grained fault diagnosis"],"prefix":"10.3934","volume":"18","author":[{"given":"Chuanjiang","family":"Li","sequence":"first","affiliation":[{"name":"School of Mechanical Engineering, Guizhou University, Guiyang, Guizhou 550025, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shaobo","family":"Li","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Guizhou University, Guiyang, Guizhou 550025, China"},{"name":"State Key Laboratory of Public Big Data, Guizhou University, Guiyang, Guizhou 550025, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lei","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Guizhou University, Guiyang, Guizhou 550025, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongjing","family":"Wei","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Guizhou Institute of Technology, Guiyang, Guizhou 550003, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ansi","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Guizhou University, Guiyang, Guizhou 550025, China"},{"name":"State Key Laboratory of Public Big Data, Guizhou University, Guiyang, Guizhou 550025, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yizong","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Guizhou University, Guiyang, Guizhou 550025, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"2321","reference":[{"key":"key-10.3934\/nhm.2023018-1","doi-asserted-by":"publisher","unstructured":"C. J. Li, S. B. Li, A. S. Zhang, L. Yang, E. Zio, M. Pecht, et al., A Siamese hybrid neural network framework for few-shot fault diagnosis of fixed-wing unmanned aerial vehicles, <i>J Comput Des Eng<\/i>, <b>9<\/b> (2022), 1511\u20131524. https:\/\/doi.org\/10.1093\/jcde\/qwac070","DOI":"10.1093\/jcde\/qwac070"},{"key":"key-10.3934\/nhm.2023018-2","doi-asserted-by":"publisher","unstructured":"J. L. Chen, J. Pan, Z. P. Li, Y. Y. Zi, X. F. Chen, Generator bearing fault diagnosis for wind turbine via empirical wavelet transform using measured vibration signals, <i>Renewable Energy<\/i>, <b>89<\/b> (2016), 80\u201392. https:\/\/doi.org\/10.1016\/j.renene.2015.12.010","DOI":"10.1016\/j.renene.2015.12.010"},{"key":"key-10.3934\/nhm.2023018-3","doi-asserted-by":"publisher","unstructured":"D. D. Peng, Z. L. Liu, H. Wang, Y. Qin, L. M. Jia, A novel deeper one-dimensional CNN with residual learning for fault diagnosis of wheelset bearings in high-speed trains,  <i>IEEE Access<\/i>, <b>7<\/b> (2018), 10278\u201310293. https:\/\/doi.org\/10.1109\/ACCESS.2018.2888842","DOI":"10.1109\/ACCESS.2018.2888842"},{"key":"key-10.3934\/nhm.2023018-4","doi-asserted-by":"publisher","unstructured":"R. X. Chen, L. L. Tang, X. L. Hu, H. N. Wu, Fault diagnosis method of low-speed rolling bearing based on acoustic emission signal and subspace embedded feature distribution alignment, <i>IEEE Trans Ind Inf<\/i>, <b>17<\/b> (2020), 5402\u20135410. https:\/\/doi.org\/10.1109\/T\u2161.2020.3028103","DOI":"10.1109\/T\u2161.2020.3028103"},{"key":"key-10.3934\/nhm.2023018-5","doi-asserted-by":"publisher","unstructured":"A. Rai, S. H. Upadhyay, A review on signal processing techniques utilized in the fault diagnosis of rolling element bearings, <i>Tribol Int<\/i>, <b>96<\/b> (2016), 289\u2013306. https:\/\/doi.org\/10.1016\/j.triboint.2015.12.037","DOI":"10.1016\/j.triboint.2015.12.037"},{"key":"key-10.3934\/nhm.2023018-6","doi-asserted-by":"publisher","unstructured":"Z. J. Wang, W. H. Du, J. Y. Wang, J. Zhou, X. F. Han, Z. Y. Zhang, et al., Research and application of improved adaptive MOMEDA fault diagnosis method, <i>Measurement<\/i>, <b>140<\/b> (2019), 63\u201375. https:\/\/doi.org\/10.1016\/j.measurement.2019.03.033","DOI":"10.1016\/j.measurement.2019.03.033"},{"key":"key-10.3934\/nhm.2023018-7","doi-asserted-by":"publisher","unstructured":"A. B. Nassif, I. Shahin, I. Attili, M. Azzeh, K. Shaalan, Speech recognition using deep neural networks: A systematic review, <i>IEEE Access<\/i>, <b>7<\/b> (2019), 19143\u201319165. https:\/\/doi.org\/10.1109\/ACCESS.2019.2896880","DOI":"10.1109\/ACCESS.2019.2896880"},{"key":"key-10.3934\/nhm.2023018-8","doi-asserted-by":"publisher","unstructured":"Y. Li, H. K. Zhang, X. Z. Xue, Y. N. Jiang, Q. Shen, Deep learning for remote sensing image classification: A survey, <i>WIREs Data Min Knowl Discovery<\/i>, <b>8<\/b> (2018), e1264. https:\/\/doi.org\/10.1002\/widm.1264","DOI":"10.1002\/widm.1264"},{"key":"key-10.3934\/nhm.2023018-9","doi-asserted-by":"publisher","unstructured":"PM. Lavanya, E. Sasikala, Deep learning techniques on text classification using Natural language processing (NLP) in social healthcare network: A comprehensive survey, <i>2021 3rd International Conference on Signal Processing and Communication (ICPSC) (IEEE)<\/i>, (2021), 603\u2013609. https:\/\/doi.org\/10.1109\/ICSPC51351.2021.9451752","DOI":"10.1109\/ICSPC51351.2021.9451752"},{"key":"key-10.3934\/nhm.2023018-10","doi-asserted-by":"publisher","unstructured":"C. Zhong, J. S. Wang, W. Z. Sun, Fault diagnosis method of rotating bearing based on improved ensemble empirical mode decomposition and deep belief network, <i>Meas Sci Technol<\/i>, <b>33<\/b> (2022), 085109. https:\/\/doi.org\/10.1088\/1361-6501\/ac6cc9","DOI":"10.1088\/1361-6501\/ac6cc9"},{"key":"key-10.3934\/nhm.2023018-11","doi-asserted-by":"publisher","unstructured":"C. J. Li, S. B. Li, A. S. Zhang, Q. He, Z. Liao, J. J. Hu, Meta-learning for few-shot bearing fault diagnosis under complex working conditions, <i>Neurocomputing<\/i>, <b>439<\/b> (2021), 197\u2013211. https:\/\/doi.org\/10.1016\/j.neucom.2021.01.099","DOI":"10.1016\/j.neucom.2021.01.099"},{"key":"key-10.3934\/nhm.2023018-12","doi-asserted-by":"publisher","unstructured":"J. L. Li, R. X. Chen, X. Z. Huang, A sequence-to-sequence remaining useful life prediction method combining unsupervised LSTM encoding-decoding and temporal convolutional network, <i>Meas Sci Technol<\/i>, <b>33<\/b> (2022), 085013. https:\/\/doi.org\/10.1088\/1361-6501\/ac632d","DOI":"10.1088\/1361-6501\/ac632d"},{"key":"key-10.3934\/nhm.2023018-13","doi-asserted-by":"publisher","unstructured":"H. D. Shao, H. K. Jiang, X. Q. Li, S. P. Wu, Intelligent fault diagnosis of rolling bearing using deep wavelet auto-encoder with extreme learning machine, <i>Knowledge-Based Systems<\/i>, <b>140<\/b> (2018), 1\u201314. https:\/\/doi.org\/10.1016\/j.knosys.2017.10.024","DOI":"10.1016\/j.knosys.2017.10.024"},{"key":"key-10.3934\/nhm.2023018-14","doi-asserted-by":"publisher","unstructured":"W. Zhang, G. L. Peng, C. H. Li, Y. H. Chen, Z. J. Zhang, A new deep learning model for fault diagnosis with good anti-noise and domain adaptation ability on raw vibration signals, <i>Sensors<\/i>, <b>17<\/b> (2017), 425. https:\/\/doi.org\/10.3390\/s17020425","DOI":"10.3390\/s17020425"},{"key":"key-10.3934\/nhm.2023018-15","doi-asserted-by":"publisher","unstructured":"D. T. Hoang, H. J. Kang, Rolling element bearing fault diagnosis using convolutional neural network and vibration image, <i>Cognit Syst Res<\/i>, <b>53<\/b> (2019), 42\u201350. https:\/\/doi.org\/10.1016\/j.cogsys.2018.03.002","DOI":"10.1016\/j.cogsys.2018.03.002"},{"key":"key-10.3934\/nhm.2023018-16","doi-asserted-by":"publisher","unstructured":"Z. Y. Chen, W. H. Li, Multisensor feature fusion for bearing fault diagnosis using sparse autoencoder and deep belief network, <i>IEEE Trans Instrum Meas<\/i>, <b>66<\/b> (2017), 1693\u20131702. https:\/\/doi.org\/10.1109\/TIM.2017.2669947","DOI":"10.1109\/TIM.2017.2669947"},{"key":"key-10.3934\/nhm.2023018-17","doi-asserted-by":"publisher","unstructured":"H. K. Jiang, X. Li, H. D. Shao, K. Zhao, Intelligent fault diagnosis of rolling bearings using an improved deep recurrent neural network, <i>Meas Sci Technol<\/i>, <b>29<\/b> (2018), 065107. https:\/\/doi.org\/10.1088\/1361-6501\/aab945","DOI":"10.1088\/1361-6501\/aab945"},{"key":"key-10.3934\/nhm.2023018-18","doi-asserted-by":"publisher","unstructured":"H. R. Fang, J. Deng, B. Zhao, Y. Shi, J. Y. Zhou, S. Y Shao, LEFE-Net: A lightweight efficient feature extraction network with strong robustness for bearing fault diagnosis, <i>IEEE Trans Instrum Meas<\/i>, <b>70<\/b> (2021), 1\u201311. https:\/\/doi.org\/10.1109\/TIM.2021.3067187","DOI":"10.1109\/TIM.2021.3067187"},{"key":"key-10.3934\/nhm.2023018-19","doi-asserted-by":"publisher","unstructured":"Z. L. Liu, H. Wang, J. J. Liu, Y. Qin, D. D. Peng, Multitask learning based on lightweight 1DCNN for fault diagnosis of wheelset bearings, <i>IEEE Trans Instrum Meas<\/i>, <b>70<\/b> (2020), 1\u201311. https:\/\/doi.org\/10.1109\/TIM.2020.3017900","DOI":"10.1109\/TIM.2020.3017900"},{"key":"key-10.3934\/nhm.2023018-20","unstructured":"X. Chu, Y. Lin, Y. S. Wang, X. T. Wang, H. L. Yu, X. Gao, et al., Distance metric learning with joint representation diversification, <i>International Conference on Machine Learning (PMLR)<\/i>, (2020), 1962\u20131973."},{"key":"key-10.3934\/nhm.2023018-21","doi-asserted-by":"crossref","unstructured":"C. P. Pang, Management optimization of equipment maintenance and spare parts for automobile intelligent manufacturing enterprises, <i>Int J Front Eng Technol<\/i>, (2022), 4. https:\/\/dx.doi.org\/10.25236\/IJFET.2022.040507","DOI":"10.25236\/IJFET.2022.040507"},{"key":"key-10.3934\/nhm.2023018-22","doi-asserted-by":"publisher","unstructured":"G. D. Sun, Y. Gao, K. Lin, Y. Hu, Fine-grained fault diagnosis method of rolling bearing combining multisynchrosqueezing transform and sparse feature coding based on dictionary learning, <i>Shock Vib<\/i>, <b>2019<\/b> (2019), 1\u201313. https:\/\/doi.org\/10.1155\/2019\/1531079","DOI":"10.1155\/2019\/1531079"},{"key":"key-10.3934\/nhm.2023018-23","doi-asserted-by":"publisher","unstructured":"Y. Wang, R. N. Liu, D. Lin, D. Y. Chen, P Li, Q. H. Hu, et al., Coarse-to-Fine: progressive knowledge transfer-based multitask convolutional neural network for intelligent large-scale fault diagnosis, <i>IEEE Trans Neural Networks Learn Syst,<\/i>  (2021), 1\u201314. https:\/\/doi.org\/10.1109\/TNNLS.2021.3100928","DOI":"10.1109\/TNNLS.2021.3100928"},{"key":"key-10.3934\/nhm.2023018-24","doi-asserted-by":"publisher","unstructured":"K. M. He, X. Y. Zhang, S. Q. Ren, J. Sun, Deep residual learning for image recognition, <i>Comput Vision Pattern Recognit<\/i>, (2021), 770\u2013781. https:\/\/doi.org\/10.48550\/arXiv.1512.03385","DOI":"10.48550\/arXiv.1512.03385"},{"key":"key-10.3934\/nhm.2023018-25","doi-asserted-by":"publisher","unstructured":"X. H. Chen, B. K. Zhang, D. Gao, Bearing fault diagnosis base on multi-scale CNN and LSTM model, <i>J Intell Manuf<\/i>, <b>32<\/b> (2021), 971\u2013987. https:\/\/doi.org\/10.1007\/s10845-020-01600-2","DOI":"10.1007\/s10845-020-01600-2"},{"key":"key-10.3934\/nhm.2023018-26","doi-asserted-by":"publisher","unstructured":"Z. B. Zhao, T. F. Li, J. Y. Wu, C. Sun, S. B. Wang, R. Q. Yan, et al., Deep learning algorithms for rotating machinery intelligent diagnosis: An open source benchmark study, <i>ISA Trans<\/i>, <b>107<\/b> (2020), 224\u2013255. https:\/\/doi.org\/10.1016\/j.isatra.2020.08.010","DOI":"10.1016\/j.isatra.2020.08.010"},{"key":"key-10.3934\/nhm.2023018-27","doi-asserted-by":"publisher","unstructured":"R. N. Liu, F. Wang, B. Y. Yang, S. J Qin, Multiscale kernel based residual convolutional neural network for motor fault diagnosis under nonstationary conditions, <i>IEEE Trans Ind Inf<\/i>, <b>16<\/b> (2019), 3797\u20133806. https:\/\/doi.org\/10.1109\/T\u2161.2019.2941868","DOI":"10.1109\/T\u2161.2019.2941868"}],"container-title":["Networks and Heterogeneous Media"],"original-title":[],"link":[{"URL":"http:\/\/www.aimspress.com\/article\/doi\/10.3934\/nhm.2023018?viewType=html","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,14]],"date-time":"2023-01-14T12:33:07Z","timestamp":1673699587000},"score":1,"resource":{"primary":{"URL":"http:\/\/www.aimspress.com\/article\/doi\/10.3934\/nhm.2023018"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"references-count":27,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2023]]}},"URL":"https:\/\/doi.org\/10.3934\/nhm.2023018","relation":{},"ISSN":["1556-1801"],"issn-type":[{"value":"1556-1801","type":"print"}],"subject":[],"published":{"date-parts":[[2023]]}}}