{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T10:37:26Z","timestamp":1783420646358,"version":"3.54.6"},"reference-count":34,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2022,6,30]],"date-time":"2022-06-30T00:00:00Z","timestamp":1656547200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Young Elite Scientist Sponsorship Program by Henan Association for Science and Technology","award":["2021HYTP014"],"award-info":[{"award-number":["2021HYTP014"]}]},{"name":"Young Elite Scientist Sponsorship Program by Henan Association for Science and Technology","award":["222102220028"],"award-info":[{"award-number":["222102220028"]}]},{"name":"Young Elite Scientist Sponsorship Program by Henan Association for Science and Technology","award":["222102210002"],"award-info":[{"award-number":["222102210002"]}]},{"name":"Young Elite Scientist Sponsorship Program by Henan Association for Science and Technology","award":["22A416004"],"award-info":[{"award-number":["22A416004"]}]},{"name":"Young Elite Scientist Sponsorship Program by Henan Association for Science and Technology","award":["21A413002"],"award-info":[{"award-number":["21A413002"]}]},{"name":"Henan Province scientific and technological research","award":["2021HYTP014"],"award-info":[{"award-number":["2021HYTP014"]}]},{"name":"Henan Province scientific and technological research","award":["222102220028"],"award-info":[{"award-number":["222102220028"]}]},{"name":"Henan Province scientific and technological research","award":["222102210002"],"award-info":[{"award-number":["222102210002"]}]},{"name":"Henan Province scientific and technological research","award":["22A416004"],"award-info":[{"award-number":["22A416004"]}]},{"name":"Henan Province scientific and technological research","award":["21A413002"],"award-info":[{"award-number":["21A413002"]}]},{"name":"Key Projects of Henan Province Colleges","award":["2021HYTP014"],"award-info":[{"award-number":["2021HYTP014"]}]},{"name":"Key Projects of Henan Province Colleges","award":["222102220028"],"award-info":[{"award-number":["222102220028"]}]},{"name":"Key Projects of Henan Province Colleges","award":["222102210002"],"award-info":[{"award-number":["222102210002"]}]},{"name":"Key Projects of Henan Province Colleges","award":["22A416004"],"award-info":[{"award-number":["22A416004"]}]},{"name":"Key Projects of Henan Province Colleges","award":["21A413002"],"award-info":[{"award-number":["21A413002"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>This paper proposes an intelligent diagnosis method for rotating machinery faults based on improved variational mode decomposition (IVMD) and CNN to process the rotating machinery non-stationary signal. Firstly, to solve the problem of time-domain feature extraction for fault diagnosis, this paper proposes an improved variational mode decomposition method with automatic optimization of the number of modes. This method overcomes the problems of the traditional VMD method, in that each parameter is set by experience and is greatly influenced by subjective experience. Secondly, the decomposed signal components are analyzed by correlation, and then high correlated components with the original signal are selected to reconstruct the original signal. The continuous wavelet transform (CWT) is employed to extract the two-dimensional time\u2013frequency domain feature map of the fault signal. Finally, the deep learning method is used to construct a convolutional neural network. After feature extraction, the two-dimensional time-frequency image is applied to the neural network to identify fault features. Experiments verify that the proposed method can adapt to rotating machinery faults in complex environments and has a high recognition rate.<\/jats:p>","DOI":"10.3390\/e24070908","type":"journal-article","created":{"date-parts":[[2022,6,30]],"date-time":"2022-06-30T20:53:02Z","timestamp":1656622382000},"page":"908","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":38,"title":["Improved Variational Mode Decomposition and CNN for Intelligent Rotating Machinery Fault Diagnosis"],"prefix":"10.3390","volume":"24","author":[{"given":"Qiyang","family":"Xiao","sequence":"first","affiliation":[{"name":"School of Artificial Intelligence, Henan University, Zhengzhou 450046, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sen","family":"Li","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Henan University, Zhengzhou 450046, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2700-834X","authenticated-orcid":false,"given":"Lin","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Henan University, Zhengzhou 450046, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wentao","family":"Shi","sequence":"additional","affiliation":[{"name":"School of Marine Science and Technology, Northwestern Polytechnical University, Xi\u2019an 710072, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,6,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.measurement.2017.08.036","article-title":"Early fault diagnosis of bearing and stator faults of the single-phase induction motor using acoustic signals","volume":"113","author":"Glowacz","year":"2017","journal-title":"Measurement"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"115175","DOI":"10.1016\/j.jsv.2020.115175","article-title":"Cyclostationary modeling for local fault diagnosis of planetary gear vibration signals","volume":"471","author":"Sun","year":"2020","journal-title":"J. Sound Vib."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Yan, X., She, D., Xu, Y., and Jia, M. (2021). Application of Generalized Composite Multiscale Lempel\u2013Ziv Complexity in Identifying Wind Turbine Gearbox Faults. Entropy, 23.","DOI":"10.3390\/e23111372"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1541","DOI":"10.1109\/JSEN.2021.3131722","article-title":"Bearing Fault Diagnosis Based on Multiple Transformation Domain Fusion and Improved Residual Dense Networks","volume":"22","author":"Sun","year":"2022","journal-title":"IEEE Sens. J."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1016\/j.measurement.2016.04.036","article-title":"Recent progress on decoupling diagnosis of hybrid failures in gear transmission systems using vibration sensor signal: A review","volume":"90","author":"Li","year":"2016","journal-title":"Measurement"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"439","DOI":"10.1016\/j.measurement.2016.01.023","article-title":"Wheel-bearing fault diagnosis of trains using empirical wavelet transform","volume":"82","author":"Cao","year":"2016","journal-title":"Measurement"},{"key":"ref_7","first-page":"1787","article-title":"Progress and Development Trends of Composite Structure Evaluation Using Noncontact Nondestructive Testing Techniques in Aviation and Aerospace Industries","volume":"35","author":"Ma","year":"2014","journal-title":"Acta Aeronaut. Astronaut. Sin."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Fu, Q., Jing, B., He, P., Si, S., and Wang, Y. (2018). Fault Feature Selection and Diagnosis of Rolling Bearings Based on EEMD and Optimized Elman_AdaBoost Algorithm. IEEE Sens. J., 5024\u20135034.","DOI":"10.1109\/JSEN.2018.2830109"},{"key":"ref_9","first-page":"305","article-title":"Vibration Forces Produced by Waviness of the Rolling Surfaces of Thrust Loaded Ball Bearings Part 1: Theory","volume":"202","author":"Wardle","year":"1988","journal-title":"Proc. Instn. Mech. Eng."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1223","DOI":"10.1016\/S0890-6955(02)00049-4","article-title":"Vibration monitoring of high speed spindles using spectral analysis techniques","volume":"42","author":"Vafaei","year":"2002","journal-title":"Int. J. Mach. Tools Manuf."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1016\/j.measurement.2015.03.017","article-title":"A novel bearing fault diagnosis model integrated permutation entropy, ensemble empirical mode decomposition and optimized SVM","volume":"69","author":"Zhanga","year":"2015","journal-title":"Measurement"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1016\/j.ymssp.2015.11.013","article-title":"Fault detection in rotor bearing systems using time frequency techniques","volume":"72","author":"Chandra","year":"2016","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"10946","DOI":"10.1109\/JSEN.2021.3061595","article-title":"A Multi-Input and Multi-Task Convolutional Neural Network for Fault Diagnosis Based on Bearing Vibration Signal","volume":"21","author":"Wang","year":"2021","journal-title":"IEEE Sens. J."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"903","DOI":"10.1098\/rspa.1998.0193","article-title":"The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis","volume":"454","author":"Huang","year":"1998","journal-title":"Proc. R. Soc. London Ser. A Math. Phys. Eng. Sci."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1007\/s10444-004-7614-3","article-title":"A B-spline approach for empirical mode decompositions","volume":"24","author":"Chen","year":"2006","journal-title":"Adv. Comput. Math."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1142\/S1793536910000422","article-title":"Complementary Ensemble Empirical Mode Decomposition: A Novel Noise Enhanced Data Analysis Method","volume":"2","author":"Yeh","year":"2010","journal-title":"Adv. Adapt. Data Anal."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"366","DOI":"10.1016\/j.sigpro.2017.08.002","article-title":"Extreme-point weighted mode decomposition","volume":"142","author":"Zheng","year":"2018","journal-title":"Signal Process."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"3325","DOI":"10.1109\/TSP.2021.3085113","article-title":"A Novel Ridge Detector for Nonstationary Multicomponent Signals: Development and Application to Robust Mode Retrieval","volume":"69","author":"Laurent","year":"2021","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_19","unstructured":"Fourer, D., Harmouche, J., Schmitt, J., Oberlin, T., and Flandrin, P. (September, January 28). The ASTRES Toolbox for Mode Extraction of Non-Stationary Multicomponent Signals. Proceedings of the 25th European Signal Processing Conference (EUSIPCO), Kos, Greece."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"531","DOI":"10.1109\/TSP.2013.2288675","article-title":"Variational Mode Decomposition","volume":"62","author":"Dragomiretskiy","year":"2014","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2658","DOI":"10.1049\/iet-gtd.2015.0906","article-title":"Detection of current transformer saturation based on variational mode decomposition analysis","volume":"10","author":"Abdoos","year":"2016","journal-title":"IET Gener. Transm. Distrib."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1109\/LGRS.2016.2644723","article-title":"Mitigation of Ionospheric Scintillation Effects on GNSS Signals Using Variational Mode Decomposition","volume":"14","author":"Sivavaraprasad","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"108216","DOI":"10.1016\/j.ymssp.2021.108216","article-title":"A fault information-guided variational mode decomposition (FIVMD) method for rolling element bearings diagnosis","volume":"164","author":"Ni","year":"2022","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"108185","DOI":"10.1016\/j.measurement.2020.108185","article-title":"An optimized VMD method and its applications in bearing fault diagnosis","volume":"166","author":"Li","year":"2020","journal-title":"Measurement"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Liang, T., Lu, H., and Sun, H. (2021). Application of Parameter Optimized Variational Mode Decomposition Method in Fault Feature Extraction of Rolling Bearing. Entropy, 23.","DOI":"10.3390\/e23050520"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Ye, M., Yan, X., and Jia, M. (2021). Rolling Bearing Fault Diagnosis Based on VMD-MPE and PSO-SVM. Entropy, 23.","DOI":"10.3390\/e23060762"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"3137","DOI":"10.1109\/TIE.2016.2519325","article-title":"An Intelligent Fault Diagnosis Method Using Unsupervised Feature Learning towards Mechanical Big Data","volume":"63","author":"Lei","year":"2016","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"107398","DOI":"10.1016\/j.ymssp.2020.107398","article-title":"1D convolutional neural networks and applications: A survey","volume":"151","author":"Kiranyaz","year":"2021","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"7067","DOI":"10.1109\/TIE.2016.2582729","article-title":"Real-Time Motor Fault Detection by 1-D Convolutional Neural Networks","volume":"63","author":"Ince","year":"2016","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"5990","DOI":"10.1109\/TIE.2017.2774777","article-title":"A New Convolutional Neural Network-Based Data-Driven Fault Diagnosis Method","volume":"65","author":"Wen","year":"2018","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.cogsys.2018.03.002","article-title":"Rolling element bearing fault diagnosis using convolutional neural network and vibration image","volume":"53","author":"Hoang","year":"2019","journal-title":"Cogn. Syst. Res."},{"key":"ref_32","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","author":"Smith","year":"2015","journal-title":"Mech. Syst. Signal Processing"},{"key":"ref_33","unstructured":"(2022, April 20). Case Western Reserve University Bearing Data Center Website. Available online: http:\/\/csegroups.case.edu\/bearingdatacenter\/home."},{"key":"ref_34","unstructured":"(2022, April 20). The Math Works Variational Mode Decomposition Website. Available online: https:\/\/au.mathworks.com\/help\/signal\/ref\/vmd.html?searchHighlight=vmd&s_tid=srchtitle."}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/24\/7\/908\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:41:15Z","timestamp":1760139675000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/24\/7\/908"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,30]]},"references-count":34,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2022,7]]}},"alternative-id":["e24070908"],"URL":"https:\/\/doi.org\/10.3390\/e24070908","relation":{},"ISSN":["1099-4300"],"issn-type":[{"value":"1099-4300","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,6,30]]}}}