{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T18:58:07Z","timestamp":1773773887950,"version":"3.50.1"},"reference-count":38,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2023,6,17]],"date-time":"2023-06-17T00:00:00Z","timestamp":1686960000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Innovation Group Project of Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) of China","award":["311021013"],"award-info":[{"award-number":["311021013"]}]},{"name":"Innovation Group Project of Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) of China","award":["51775037"],"award-info":[{"award-number":["51775037"]}]},{"name":"Innovation Group Project of Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) of China","award":["FRF-BD-18-001A"],"award-info":[{"award-number":["FRF-BD-18-001A"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["311021013"],"award-info":[{"award-number":["311021013"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["51775037"],"award-info":[{"award-number":["51775037"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["FRF-BD-18-001A"],"award-info":[{"award-number":["FRF-BD-18-001A"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100018594","name":"Fundamental Research Funds for Central Universities of China","doi-asserted-by":"publisher","award":["311021013"],"award-info":[{"award-number":["311021013"]}],"id":[{"id":"10.13039\/501100018594","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100018594","name":"Fundamental Research Funds for Central Universities of China","doi-asserted-by":"publisher","award":["51775037"],"award-info":[{"award-number":["51775037"]}],"id":[{"id":"10.13039\/501100018594","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100018594","name":"Fundamental Research Funds for Central Universities of China","doi-asserted-by":"publisher","award":["FRF-BD-18-001A"],"award-info":[{"award-number":["FRF-BD-18-001A"]}],"id":[{"id":"10.13039\/501100018594","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Rolling bearings and gears are important components of rotating machinery. Their operating condition affects the operation of the equipment. Fault in the accessory directly leads to equipment downtime or a series of adverse reactions in the system, which brings enormous pecuniary loss to the institution. Hence, it is of great significance to detect the operating status of rolling bearings and gears for fault diagnosis. At present, the vibration method is considered to be the most common method for fault diagnosis, a method that analyzes the equipment by collecting vibration signals. However, rotating-machinery fault diagnosis is challenging due to the need to select effective fault feature vectors, use appropriate machine-learning classification methods, and achieve accurate fault diagnosis. To solve this problem, this paper illustrates a new fault-diagnosis method combining successive variational-mode decomposition (SVMD) entropy values and machine learning. First, the simulation signal and the real fault signal are used to analyze and compare the variational-mode decomposition (VMD) and SVMD methods. The comparison results prove that SVMD can be a useful method for fault diagnosis. Then, these two methods are utilized to extract the energy entropy and fuzzy entropy of the gearbox dataset of Southeast University (SEU), respectively. The feature vector and multiple machine-learning classification models are constructed for failure-mode identification. The experimental-analysis results successfully verify the effectiveness of the combined SVMD entropy and machine-learning approach for rotating-machinery fault diagnosis.<\/jats:p>","DOI":"10.3390\/a16060304","type":"journal-article","created":{"date-parts":[[2023,6,19]],"date-time":"2023-06-19T01:59:51Z","timestamp":1687139991000},"page":"304","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["Fault-Diagnosis Method for Rotating Machinery Based on SVMD Entropy and Machine Learning"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5273-6867","authenticated-orcid":false,"given":"Lijun","family":"Zhang","sequence":"first","affiliation":[{"name":"National Center for Materials Service Safety, University of Science and Technology Beijing, Beijing 100083, China"},{"name":"Innovation Group of Marine Engineering Materials and Corrosion Control, Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519080, China"},{"name":"Research Institute of Macro-Safety Science, University of Science and Technology Beijing, Beijing 100083, China"}]},{"given":"Yuejian","family":"Zhang","sequence":"additional","affiliation":[{"name":"National Center for Materials Service Safety, University of Science and Technology Beijing, Beijing 100083, China"}]},{"given":"Guangfeng","family":"Li","sequence":"additional","affiliation":[{"name":"National Center for Materials Service Safety, University of Science and Technology Beijing, Beijing 100083, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,6,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"945","DOI":"10.1016\/j.renene.2021.07.085","article-title":"Fault detection by an ensemble framework of Extreme Gradient Boosting (XGBoost) in the operation of offshore wind turbines","volume":"179","author":"Trizoglou","year":"2021","journal-title":"Renew. Energy"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"035902","DOI":"10.1088\/1361-6501\/aca496","article-title":"A deep generative model based on CNN-CVAE for wind turbine condition monitoring","volume":"34","author":"Liu","year":"2023","journal-title":"Meas. Sci. Technol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"065009","DOI":"10.1088\/1361-6501\/ac543a","article-title":"Bearing fault diagnosis using transfer learning and optimized deep belief network","volume":"33","author":"Zhao","year":"2022","journal-title":"Meas. Sci. Technol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1016\/j.renene.2022.11.064","article-title":"The fault frequency priors fusion deep learning framework with application to fault diagnosis of offshore wind turbines","volume":"202","author":"Xie","year":"2023","journal-title":"Renew. Energy"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"104613","DOI":"10.1016\/j.engfailanal.2020.104613","article-title":"Failure investigation of a crane gear damage","volume":"115","author":"Vukelic","year":"2020","journal-title":"Eng. Fail. Anal."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"025024","DOI":"10.1088\/1361-6501\/ac991f","article-title":"A novel convolutional network with a self-adaptation high-pass filter for fault diagnosis of wind turbine gearboxes","volume":"34","author":"Yang","year":"2023","journal-title":"Meas. Sci. Technol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"062703","DOI":"10.1063\/5.0070140","article-title":"Review of adaptive decomposition-based data preprocessing for renewable generation rich power system applications","volume":"13","author":"Das","year":"2021","journal-title":"J. Renew. Sustain. Energy"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"375","DOI":"10.1016\/j.measurement.2018.10.086","article-title":"An adaptive sensitive frequency band selection method for empirical wavelet transform and its application in bearing fault diagnosis","volume":"134","author":"Yu","year":"2019","journal-title":"Measurement"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Liu, X., Zhu, A., Song, Y., Ma, G., Bai, X., and Guo, Y. (2022). Application of Improved Robust Local Mean Decomposition and Multiple Disturbance Multi-Verse Optimizer-Based MCKD in the Diagnosis of Multiple Rolling Element Bearing Faults. Machines, 10.","DOI":"10.3390\/machines10100883"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"12993","DOI":"10.1016\/j.matpr.2018.02.284","article-title":"Fault Diagnosis on Journal Bearing Using Empirical Mode Decomposition","volume":"5","author":"Babu","year":"2018","journal-title":"Mater. Today-Proc."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"937","DOI":"10.1016\/j.prostr.2019.07.074","article-title":"Integrated Vibro-Acoustic Analysis and Empirical Mode Decomposition for Fault Diagnosis of Gears in a Wind Turbine","volume":"14","author":"Vamsi","year":"2019","journal-title":"Procedia Struct. Integr."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"115018","DOI":"10.1088\/1361-6501\/ac86e5","article-title":"Application of multi-kernel relevance vector machine and data pre-processing by complementary ensemble empirical mode decomposition and mutual dimensionless in fault diagnosis","volume":"33","author":"Xiong","year":"2022","journal-title":"Meas. Sci. Technol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1016\/j.isatra.2018.09.008","article-title":"Quantitative diagnosis for bearing faults by improving ensemble empirical mode decomposition","volume":"83","author":"Hoseinzadeh","year":"2018","journal-title":"ISA Trans."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"513","DOI":"10.1007\/s42417-022-00591-z","article-title":"Fault Diagnosis of Rolling Bearing Based on an Improved Denoising Technique Using Complete Ensemble Empirical Mode Decomposition and Adaptive Thresholding Method","volume":"11","author":"Sahu","year":"2023","journal-title":"J. Vib. Eng. Technol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"218","DOI":"10.1016\/j.isatra.2019.01.038","article-title":"An improved complementary ensemble empirical mode decomposition with adaptive noise and its application to rolling element bearing fault diagnosis","volume":"91","author":"Cheng","year":"2019","journal-title":"ISA Trans."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"106545","DOI":"10.1016\/j.ymssp.2019.106545","article-title":"Fault feature extraction of rotating machinery using a reweighted complete ensemble empirical mode decomposition with adaptive noise and demodulation analysis","volume":"138","author":"Wang","year":"2020","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Ma, J., Zhuo, S., Li, C., Zhan, L., and Zhang, G. (2021). An Enhanced Intrinsic Time-Scale Decomposition Method Based on Adaptive Levy Noise and Its Application in Bearing Fault Diagnosis. Symmetry, 13.","DOI":"10.3390\/sym13040617"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"109077","DOI":"10.1016\/j.epsr.2022.109077","article-title":"A new wave-based fault detection scheme during power swing","volume":"246","author":"Pazoki","year":"2023","journal-title":"Electr. Power Syst. Res."},{"key":"ref_19","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_20","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.measurement.2018.08.002","article-title":"Study on planetary gear fault diagnosis based on variational mode decomposition and deep neural networks","volume":"130","author":"Li","year":"2018","journal-title":"Measurement"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1886","DOI":"10.1177\/1077546320911484","article-title":"Intelligent fault diagnosis of rolling bearings using variational mode decomposition and self-organizing feature map","volume":"26","author":"Zhang","year":"2020","journal-title":"J. Vib. Control"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1007\/s10712-022-09742-z","article-title":"A Review of Variational Mode Decomposition in Seismic Data Analysis","volume":"44","author":"Liu","year":"2022","journal-title":"Surv. Geophys."},{"key":"ref_23","first-page":"2969488","article-title":"An Improved Parameter-Adaptive Variational Mode Decomposition Method and Its Application in Fault Diagnosis of Rolling Bearings","volume":"2021","author":"Li","year":"2021","journal-title":"Shock Vib."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"107901","DOI":"10.1016\/j.measurement.2020.107901","article-title":"An integrated method based on hybrid grey wolf optimizer improved variational mode decomposition and deep neural network for fault diagnosis of rolling bearing","volume":"162","author":"Gai","year":"2020","journal-title":"Measurement"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"112016","DOI":"10.1016\/j.measurement.2022.112016","article-title":"Bearing fault diagnosis via a parameter-optimized feature mode decomposition","volume":"203","author":"Yan","year":"2022","journal-title":"Measurement"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Nguyen, C.D., Ahmad, Z., and Kim, J.-M. (2021). Gearbox Fault Identification Framework Based on Novel Localized Adaptive Denoising Technique, Wavelet-Based Vibration Imaging, and Deep Convolutional Neural Network. Appl. Sci., 11.","DOI":"10.3390\/app11167575"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Ahmad, Z., Nguyen, T.-K., Ahmad, S., Nguyen, C.D., and Kim, J.-M. (2022). Multistage Centrifugal Pump Fault Diagnosis Using Informative Ratio Principal Component Analysis. Sensors, 22.","DOI":"10.3390\/s22010179"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"107610","DOI":"10.1016\/j.sigpro.2020.107610","article-title":"Successive variational mode decomposition","volume":"174","author":"Nazari","year":"2020","journal-title":"Signal Process."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1059","DOI":"10.1109\/JBHI.2017.2734074","article-title":"Variational Mode Extraction: A New Efficient Method to Derive Respiratory Signals from ECG","volume":"22","author":"Nazari","year":"2018","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1100","DOI":"10.1016\/j.energy.2019.03.057","article-title":"Vibration fault diagnosis of wind turbines based on variational mode decomposition and energy entropy","volume":"174","author":"Chen","year":"2019","journal-title":"Energy"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Jiao, J., Yue, J., and Pei, D. (2022). Feature Enhancement Method of Rolling Bearing Based on K-Adaptive VMD and RBF-Fuzzy Entropy. Entropy, 24.","DOI":"10.3390\/e24020197"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"2446","DOI":"10.1109\/TII.2018.2864759","article-title":"Highly Accurate Machine Fault Diagnosis Using Deep Transfer Learning","volume":"15","author":"Shao","year":"2019","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"106587","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. Signal Process."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"4927","DOI":"10.1109\/JSEN.2020.3030910","article-title":"Fault Diagnosis of Rolling Bearings Based on an Improved Stack Autoencoder and Support Vector Machine","volume":"21","author":"Cui","year":"2021","journal-title":"IEEE Sens. J."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"109810","DOI":"10.1016\/j.ymssp.2022.109810","article-title":"Leak detection in water distribution systems by classifying vibration signals","volume":"185","author":"Yu","year":"2023","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"102957","DOI":"10.1016\/j.bspc.2021.102957","article-title":"Automatic detection of abnormal EEG signals using wavelet feature extraction and gradient boosting decision tree","volume":"70","author":"Albaqami","year":"2021","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_37","first-page":"60","article-title":"Fault diagnosis of tractor auxiliary gearbox using vibration analysis and random forest classifier","volume":"9","author":"Omid","year":"2022","journal-title":"Inf. Process. Agric."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1539","DOI":"10.1109\/TIE.2017.2733438","article-title":"Machine Health Monitoring Using Local Feature-Based Gated Recurrent Unit Networks","volume":"65","author":"Zhao","year":"2018","journal-title":"IEEE Trans. Ind. Electron."}],"container-title":["Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4893\/16\/6\/304\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:55:35Z","timestamp":1760126135000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4893\/16\/6\/304"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,17]]},"references-count":38,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2023,6]]}},"alternative-id":["a16060304"],"URL":"https:\/\/doi.org\/10.3390\/a16060304","relation":{},"ISSN":["1999-4893"],"issn-type":[{"value":"1999-4893","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,6,17]]}}}