{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T17:59:28Z","timestamp":1777658368598,"version":"3.51.4"},"reference-count":29,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2021,8,5]],"date-time":"2021-08-05T00:00:00Z","timestamp":1628121600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Wenlei Sun","award":["2020B02014"],"award-info":[{"award-number":["2020B02014"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>We propose a novel fault-diagnosis approach for rolling bearings by integrating variational mode decomposition (VMD), refined composite multiscale dispersion entropy (RCMDE), and support vector machine (SVM) optimized by a sparrow search algorithm (SSA). Firstly, VMD was selected from various signal decomposition methods to decompose the original signal. Then, the signal features were extracted by RCMDE as the input of the diagnosis model. Compared with multiscale sample entropy (MSE) and multiscale dispersion entropy (MDE), RCMDE proved to be superior. Afterwards, SSA was used to search the optimal parameters of SVM to identify different faults. Finally, the proposed coordinated VMD\u2013RCMDE\u2013SSA\u2013SVM approach was verified and evaluated by the experimental data collected by the wind turbine drivetrain diagnostics simulator (WTDS). The results of the experiments demonstrate that the proposed approach not only identifies bearing fault types quickly and effectively but also achieves better performance than other comparative methods.<\/jats:p>","DOI":"10.3390\/s21165297","type":"journal-article","created":{"date-parts":[[2021,8,5]],"date-time":"2021-08-05T09:35:32Z","timestamp":1628156132000},"page":"5297","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":37,"title":["Coordinated Approach Fusing RCMDE and Sparrow Search Algorithm-Based SVM for Fault Diagnosis of Rolling Bearings"],"prefix":"10.3390","volume":"21","author":[{"given":"Jie","family":"Lv","sequence":"first","affiliation":[{"name":"School of Mechanical Engineering, Xinjiang University, Urumqi 830047, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenlei","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Xinjiang University, Urumqi 830047, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongwei","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Xinjiang University, Urumqi 830047, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fan","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Xinjiang University, Urumqi 830047, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,8,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"13086","DOI":"10.1109\/ACCESS.2020.2966582","article-title":"Fault Diagnosis for Rolling Bearings Based on Composite Multiscale Fine-Sorted Dispersion Entropy and SVM With Hybrid Mutation SCA-HHO Algorithm Optimization","volume":"8","author":"Fu","year":"2020","journal-title":"IEEE Access"},{"key":"ref_2","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. Frankl. Inst."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"3264969","DOI":"10.1155\/2019\/3264969","article-title":"Blind Parameter Identification of MAR Model and Mutation Hybrid GWO-SCA Optimized SVM for Fault Diagnosis of Rotating Machinery","volume":"2019","author":"Fu","year":"2019","journal-title":"Complexity"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"107582","DOI":"10.1016\/j.ymssp.2020.107582","article-title":"A novel Fast Entrogram and its applications in rolling bearing fault diagnosis","volume":"154","author":"Zhang","year":"2021","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"238","DOI":"10.1049\/sil2.12026","article-title":"Improved VMD-KFCM algorithm for the fault diagnosis of rolling bearing vibration signals","volume":"15","author":"Chang","year":"2021","journal-title":"IET Signal Process."},{"key":"ref_6","first-page":"123","article-title":"Order bispectrum analysis based on fault characteristic frequency and its application to the fault diagno-sis of rolling bearings","volume":"33","author":"Liu","year":"2013","journal-title":"Proc. CSEE"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Li, Z., Jiang, W., Zhang, S., Sun, Y., and Zhang, S. (2021). A Hydraulic Pump Fault Diagnosis Method Based on the Modified Ensemble Empirical Mode Decomposition and Wavelet Kernel Extreme Learning Machine Methods. Sensors, 21.","DOI":"10.3390\/s21082599"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"443","DOI":"10.1098\/rsif.2005.0058","article-title":"The local mean decomposition and its application to EEG perception data","volume":"2","author":"Smith","year":"2005","journal-title":"J. R. Soc. Interface"},{"key":"ref_9","first-page":"215","article-title":"A new method of nonstationary signal analysis local characteristic scale decomposition","volume":"25","author":"Cheng","year":"2012","journal-title":"J. Vib. Eng."},{"key":"ref_10","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":"IEEE Trans. Signal Process."},{"key":"ref_11","first-page":"136","article-title":"Sample entropy-based roller bearing fault diagnosis method","volume":"31","author":"Zhao","year":"2012","journal-title":"J. Vib. Shock"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"108891","DOI":"10.1016\/j.measurement.2020.108891","article-title":"Oscillation based permutation entropy calculation as a dynamic nonlinear feature for health monitoring of rolling element bearing","volume":"172","author":"Noman","year":"2021","journal-title":"Measurement"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1016\/j.jsv.2015.09.016","article-title":"A fault diagnosis scheme for rolling bearing based on local mean decomposition and improved multiscale fuzzy entropy","volume":"360","author":"Li","year":"2016","journal-title":"J. Sound Vib."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1016\/j.bspc.2015.08.004","article-title":"Improved multiscale permutation entropy for biomedical signal analysis: Interpretation and application to electroencephalogram recordings","volume":"23","author":"Azami","year":"2016","journal-title":"Biomed. Signal Process. Control."},{"key":"ref_15","first-page":"294","article-title":"Rotor fault diagnosis based on multiscale entropy","volume":"33","author":"Zheng","year":"2013","journal-title":"J. Vib. Meas. Diagn."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"610","DOI":"10.1109\/LSP.2016.2542881","article-title":"Dispersion Entropy: A Measure for Time-Series Analysis","volume":"23","author":"Rostaghi","year":"2016","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2872","DOI":"10.1109\/TBME.2017.2679136","article-title":"Refined Composite Multiscale Dispersion Entropy and its Application to Biomedical Signals","volume":"64","author":"Azami","year":"2017","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_18","first-page":"106","article-title":"Fault diagnosis of rotor based on VMD_IWT approximate entropy and PSO_SVM","volume":"6","author":"Zhang","year":"2019","journal-title":"Modul. Ma Chine Tool Autom. Manuf. Tech."},{"key":"ref_19","first-page":"147","article-title":"Rolling bearing fault diagnosis based on GCMWPE and parameter optimization SVM","volume":"32","author":"Ding","year":"2021","journal-title":"China Mech. Eng."},{"key":"ref_20","first-page":"107","article-title":"Fault diagnosis method based on the entropy-manifold feature and SSO-SVM","volume":"40","author":"Wang","year":"2021","journal-title":"J. Vib. Shock"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1080\/21642583.2019.1708830","article-title":"A novel swarm intelligence optimization approach: Sparrow search algorithm","volume":"8","author":"Xue","year":"2020","journal-title":"Syst. Sci. Control. Eng."},{"key":"ref_22","first-page":"190","article-title":"Semi-supervised fault diagnosis of bearings based on the VMD dispersion entropy and improved SVDD with modified grey wolf optimizer","volume":"38","author":"Fu","year":"2019","journal-title":"J. Vib. Shock."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Luo, S., Yang, W., and Luo, Y. (2020). Fault Diagnosis of a Rolling Bearing Based on Adaptive Sparest Narrow-Band Decomposition and RefinedComposite Multiscale Dispersion Entropy. Entropy, 22.","DOI":"10.3390\/e22040375"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/1961189.1961199","article-title":"LIBSVM: A library for support vector machines","volume":"2","author":"Chang","year":"2011","journal-title":"ACM Trans. Intell. Syst. Tech."},{"key":"ref_25","unstructured":"(2021, March 01). Bearing Data Center of the Case Western Reserve University. Available online: https:\/\/csegroups.case.edu\/bearingdatacenter\/home."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"108580","DOI":"10.1016\/j.measurement.2020.108580","article-title":"Coordinated approach fusing time-shift multiscale dispersion entropy and vibrational Harris hawks optimization-based SVM for fault diagnosis of rolling bearing","volume":"173","author":"Shao","year":"2021","journal-title":"Measurement"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Azami, H., and Escudero, J. (2018). Coarse-Graining Approaches in Univariate Multiscale Sample and Dispersion Entropy. Entropy, 20.","DOI":"10.3390\/e20020138"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"401","DOI":"10.1109\/TR.2018.2882682","article-title":"A Hybrid Prognostics Approach for Estimating Remaining Useful Life of Rolling Element Bearings","volume":"69","author":"Wang","year":"2018","journal-title":"IEEE Trans. Reliab."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"3520011","DOI":"10.1109\/TIM.2021.3088489","article-title":"A Hybrid Generalization Network for Intelligent Fault Diagnosis of Rotating Machinery Under Unseen Working Conditions","volume":"70","author":"Han","year":"2021","journal-title":"IEEE Trans. Instrum. Meas."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/16\/5297\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:41:09Z","timestamp":1760164869000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/16\/5297"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,8,5]]},"references-count":29,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2021,8]]}},"alternative-id":["s21165297"],"URL":"https:\/\/doi.org\/10.3390\/s21165297","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,8,5]]}}}