{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T02:02:07Z","timestamp":1779328927087,"version":"3.51.4"},"reference-count":47,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2023,7,25]],"date-time":"2023-07-25T00:00:00Z","timestamp":1690243200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100007540","name":"Jiangsu Agriculture Science and Technology Innovation Fund (JASTIF)","doi-asserted-by":"publisher","award":["CX(21)3155"],"award-info":[{"award-number":["CX(21)3155"]}],"id":[{"id":"10.13039\/100007540","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100007540","name":"Jiangsu Agriculture Science and Technology Innovation Fund (JASTIF)","doi-asserted-by":"publisher","award":["BK20201065"],"award-info":[{"award-number":["BK20201065"]}],"id":[{"id":"10.13039\/100007540","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100007540","name":"Jiangsu Agriculture Science and Technology Innovation Fund (JASTIF)","doi-asserted-by":"publisher","award":["2022CFB935"],"award-info":[{"award-number":["2022CFB935"]}],"id":[{"id":"10.13039\/100007540","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004608","name":"Natural Science Foundation of Jiangsu Province","doi-asserted-by":"publisher","award":["CX(21)3155"],"award-info":[{"award-number":["CX(21)3155"]}],"id":[{"id":"10.13039\/501100004608","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004608","name":"Natural Science Foundation of Jiangsu Province","doi-asserted-by":"publisher","award":["BK20201065"],"award-info":[{"award-number":["BK20201065"]}],"id":[{"id":"10.13039\/501100004608","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004608","name":"Natural Science Foundation of Jiangsu Province","doi-asserted-by":"publisher","award":["2022CFB935"],"award-info":[{"award-number":["2022CFB935"]}],"id":[{"id":"10.13039\/501100004608","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Natural Science Foundation of Hubei Province of China","award":["CX(21)3155"],"award-info":[{"award-number":["CX(21)3155"]}]},{"name":"Natural Science Foundation of Hubei Province of China","award":["BK20201065"],"award-info":[{"award-number":["BK20201065"]}]},{"name":"Natural Science Foundation of Hubei Province of China","award":["2022CFB935"],"award-info":[{"award-number":["2022CFB935"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Because of the influence of harsh and variable working environments, the vibration signals of rolling bearings for combine harvesters usually show obvious characteristics of strong non-stationarity and nonlinearity. Accomplishing accurate fault diagnosis using these signals for rolling bearings is a challenging subject. In this paper, a novel fault diagnosis method based on composite-scale-variable dispersion entropy (CSvDE) and self-optimization variational mode decomposition (SoVMD) is proposed, systematically combining the nonstationary signal analysis approach and machine learning technology. Firstly, an improved SoVMD algorithm is developed to realize adaptive parameter optimization and to further extract multiscale frequency components from original signals. Subsequently, a CSvDE-based feature learning model is established to generate the multiscale fault feature space (MsFFS) of frequency components for the improvement of fault feature learning ability. Finally, the generated MsFFS can serve as the inputs of the Softmax classifier for fault category identification. Extensive experiments on the vibration datasets collected from rolling bearings of combine harvesters are conducted, and the experimental results demonstrate the more superior and robust fault diagnosis performance of the proposed method compared to other existing approaches.<\/jats:p>","DOI":"10.3390\/e25081111","type":"journal-article","created":{"date-parts":[[2023,7,26]],"date-time":"2023-07-26T00:45:02Z","timestamp":1690332302000},"page":"1111","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Fault Diagnosis for Rolling Bearing of Combine Harvester Based on Composite-Scale-Variable Dispersion Entropy and Self-Optimization Variational Mode Decomposition Algorithm"],"prefix":"10.3390","volume":"25","author":[{"given":"Wei","family":"Jiang","sequence":"first","affiliation":[{"name":"Jiangsu Key Laboratory of Advanced Manufacturing Technology, Huaiyin Institute of Technology, Huai\u2019an 223003, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yahui","family":"Shan","sequence":"additional","affiliation":[{"name":"Wuhan Second Ship Design and Research Institute, Wuhan 430064, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoming","family":"Xue","sequence":"additional","affiliation":[{"name":"Jiangsu Key Laboratory of Advanced Manufacturing Technology, Huaiyin Institute of Technology, Huai\u2019an 223003, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3623-6360","authenticated-orcid":false,"given":"Jianpeng","family":"Ma","sequence":"additional","affiliation":[{"name":"Aero Engine Corporation of China, Harbin Bearing Co., Ltd., Harbin 150500, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhong","family":"Chen","sequence":"additional","affiliation":[{"name":"Jiangsu Key Laboratory of Advanced Manufacturing Technology, Huaiyin Institute of Technology, Huai\u2019an 223003, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nan","family":"Zhang","sequence":"additional","affiliation":[{"name":"Jiangsu Key Laboratory of Advanced Manufacturing Technology, Huaiyin Institute of Technology, Huai\u2019an 223003, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"106771","DOI":"10.1016\/j.compag.2022.106771","article-title":"Combine harvester remote monitoring system based on multi-source information fusion","volume":"194","author":"Qiu","year":"2022","journal-title":"Comput. Electron. Agr."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Yang, G., Cheng, Y., Xi, C., Liu, L., and Gan, X. (2022). Combine Harvester Bearing Fault-Diagnosis Method Based on SDAE-RCmvMSE. Entropy, 24.","DOI":"10.3390\/e24081139"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"108734","DOI":"10.1016\/j.ymssp.2021.108734","article-title":"Dual-impulse behavior analysis and quantitative diagnosis of the raceway fault of rolling bearing","volume":"169","author":"Ma","year":"2022","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Yan, G., Chen, J., Bai, Y., Yu, C., and Yu, C. (2022). A Survey on Fault Diagnosis Approaches for Rolling Bearings of Railway Vehicles. Processes, 10.","DOI":"10.3390\/pr10040724"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.isatra.2022.07.017","article-title":"Decentralized plant-wide monitoring based on mutual information-Louvain decomposition and support vector data description diagnosis","volume":"133","author":"Wang","year":"2023","journal-title":"ISA Trans."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"5583","DOI":"10.1002\/rnc.6660","article-title":"An integrated design method for active fault diagnosis and control","volume":"33","author":"Wang","year":"2023","journal-title":"Int. J. Robust Nonlinear Control."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Ahmed, H., and Nandi, A. (2022). Intrinsic Dimension Estimation-Based Feature Selection and Multinomial Logistic Regression for Classification of Bearing Faults Using Compressively Sampled Vibration Signals. Entropy, 24.","DOI":"10.3390\/e24040511"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"125017","DOI":"10.1088\/1361-6501\/ac29d3","article-title":"Bearing fault diagnosis based on the active energy conversion of generalized stochastic resonance in fluctuating-frequency linear oscillator","volume":"32","author":"Wang","year":"2021","journal-title":"Meas. Sci. Technol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"108569","DOI":"10.1016\/j.measurement.2020.108569","article-title":"Synchrosqueezing extracting transform and its application in bearing fault diagnosis under non-stationary conditions","volume":"173","author":"Liu","year":"2021","journal-title":"Measurement"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"105269","DOI":"10.1016\/j.engappai.2022.105269","article-title":"Intelligent fault diagnosis of rolling bearing based on wavelet transform and improved ResNet under noisy labels and environment","volume":"115","author":"Liang","year":"2022","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"110348","DOI":"10.1016\/j.measurement.2021.110348","article-title":"An improved empirical wavelet transform and sensitive components selecting method for bearing fault","volume":"187","author":"Liu","year":"2022","journal-title":"Measurement"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"3507311","DOI":"10.1109\/TIM.2020.3044517","article-title":"An Adaptive Optimized TVF-EMD Based on a Sparsity-Impact Measure Index for Bearing Incipient Fault Diagnosis","volume":"70","author":"Ye","year":"2021","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1016\/j.neucom.2021.01.001","article-title":"The intermittent fault diagnosis of analog circuits based on EEMD-DBN","volume":"436","author":"Zhong","year":"2021","journal-title":"Neurocomputing"},{"key":"ref_14","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_15","doi-asserted-by":"crossref","unstructured":"Zhou, J., Xiao, M., Niu, Y., and Ji, G. (2022). Rolling Bearing Fault Diagnosis Based on WGWOA-VMD-SVM. Sensors, 22.","DOI":"10.3390\/s22166281"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"111360","DOI":"10.1016\/j.measurement.2022.111360","article-title":"A VME method based on the convergent tendency of VMD and its application in multi-fault diagnosis of rolling bearings","volume":"198","author":"Li","year":"2022","journal-title":"Measurement"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Jin, Z., Chen, G., and Yang, Z. (2022). Rolling Bearing Fault Diagnosis Based on WOA-VMD-MPE and MPSO-LSSVM. Entropy, 24.","DOI":"10.3390\/e24070927"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"104007","DOI":"10.1088\/1361-6501\/ac0034","article-title":"Bearing fault diagnosis based on optimized variational mode decomposition and 1D convolutional neural networks","volume":"32","author":"Wang","year":"2022","journal-title":"Meas. Sci. Technol."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"518","DOI":"10.1016\/j.isatra.2022.06.047","article-title":"Bearing multi-fault diagnosis with iterative generalized demodulation guided by enhanced rotational frequency matching under time-varying speed conditions","volume":"133","author":"Zhao","year":"2023","journal-title":"ISA Trans."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1814","DOI":"10.1002\/cjce.24281","article-title":"Improved bilayer convolution transfer learning neural network for industrial fault detection","volume":"100","author":"Wang","year":"2021","journal-title":"Can. J. Chem. Eng."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1627","DOI":"10.1109\/TMECH.2022.3215545","article-title":"Bearing Weak Fault Feature Extraction Under Time-Varying Speed Conditions Based on Frequency Matching Demodulation Transform","volume":"28","author":"Zhao","year":"2022","journal-title":"IEEE\/ASME Trans. Mechatron."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/j.compind.2019.02.001","article-title":"Deep convolutional neural network based planet bearing fault classification","volume":"107","author":"Zhao","year":"2019","journal-title":"Comput. Ind."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"3109","DOI":"10.1109\/TIE.2021.3063979","article-title":"Variational Embedding Multiscale Diversity Entropy for Fault Diagnosis of Large-Scale Machinery","volume":"69","author":"Wang","year":"2022","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Li, Y., Gao, P., Tang, B., Yi, Y., and Zhang, J. (2022). Double Feature Extraction Method of Ship-Radiated Noise Signal Based on Slope Entropy and Permutation Entropy. Entropy, 24.","DOI":"10.3390\/e24091265"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"2607","DOI":"10.1109\/TIM.2020.2981220","article-title":"Entropy Measures in Machine Fault Diagnosis: Insights and Applications","volume":"69","author":"Huo","year":"2020","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1447","DOI":"10.1007\/s11071-021-06728-1","article-title":"Hierarchical diversity entropy for the early fault diagnosis of rolling bearing","volume":"108","author":"Wang","year":"2022","journal-title":"Nonlinear Dynam."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2297","DOI":"10.1073\/pnas.88.6.2297","article-title":"Approximate entropy as a measure of system complexity","volume":"6","author":"Pincus","year":"1991","journal-title":"Proc. Nat. Acad. Sci. USA"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1016\/j.ymssp.2013.04.006","article-title":"Quantitative diagnosis of a spall-like fault of a rolling element bearing by empirical mode decomposition and the approximate entropy method","volume":"40","author":"Zhao","year":"2013","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Liu, W., Jiang, Y., and Xu, Y. (2022). A Super Fast Algorithm for Estimating Sample Entropy. Entropy, 24.","DOI":"10.3390\/e24040524"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1576817","DOI":"10.1155\/2019\/1576817","article-title":"Multistep Degradation Tendency Prediction for Aircraft Engines Based on CEEMDAN Permutation Entropy and Improved Grey-Markov Model","volume":"2019","author":"Jiang","year":"2019","journal-title":"Complexity"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1016\/j.isatra.2017.12.021","article-title":"Spare optimistic based on improved ADMM and the minimum entropy de-convolution for the early weak fault diagnosis of bearings in marine systems","volume":"78","author":"Gao","year":"2018","journal-title":"ISA Trans."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"658","DOI":"10.1016\/j.ymssp.2016.04.028","article-title":"Self-adaptive bearing fault diagnosis based on permutation entropy and manifold-based dynamic time warping","volume":"114","author":"Tian","year":"2019","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"H2039","DOI":"10.1152\/ajpheart.2000.278.6.H2039","article-title":"Physiological time-series analysis using approximate entropy and sample entropy","volume":"278","author":"Richman","year":"2000","journal-title":"Am. J. Physiol.-Heart Circ. Physiol."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"87529","DOI":"10.1109\/ACCESS.2020.2992935","article-title":"Adaptive Multiscale Weighted Permutation Entropy for Rolling Bearing Fault Diagnosis","volume":"8","author":"Huo","year":"2020","journal-title":"IEEE Access"},{"key":"ref_35","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_36","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1016\/j.jsv.2018.08.025","article-title":"Application of dispersion entropy to status characterization of rotary machines","volume":"438","author":"Rostaghi","year":"2018","journal-title":"J. Sound Vib."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"3354","DOI":"10.1177\/1475921720986945","article-title":"A new fault diagnosis method based on adaptive spectrum mode extraction","volume":"20","author":"Wang","year":"2021","journal-title":"Struct. Health"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"3520809","DOI":"10.1109\/TIM.2022.3198479","article-title":"Fault Detection for Wind Turbine System Using Fractional Extended Dispersion Entropy and Cumulative Sum Control Chart","volume":"71","author":"Shao","year":"2022","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"108333","DOI":"10.1016\/j.measurement.2020.108333","article-title":"Parallel multi-scale entropy and it\u2019s application in rolling bearing fault diagnosis","volume":"168","author":"Zhao","year":"2021","journal-title":"Measurement"},{"key":"ref_40","first-page":"67","article-title":"Graph Co-Attentive Session-based Recommendation","volume":"40","author":"Pan","year":"2022","journal-title":"ACM Trans. Inf. Syst."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/j.neucom.2020.05.040","article-title":"Wasserstein distance based deep adversarial transfer learning for intelligent fault diagnosis with unlabeled or insufficient labeled data","volume":"409","author":"Cheng","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"116822","DOI":"10.1016\/j.eswa.2022.116822","article-title":"A review of recent approaches on wrapper feature selection for intrusion detection","volume":"198","author":"Maldonado","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"23659","DOI":"10.1109\/ACCESS.2022.3154777","article-title":"A New Feature Extraction Technique for Early Degeneration Detection of Rolling Bearings","volume":"10","author":"Lv","year":"2022","journal-title":"IEEE Access"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"8444","DOI":"10.1109\/ACCESS.2021.3049436","article-title":"Multi-Scale Sample Entropy-Based Energy Moment Features Applied to Fault Classification","volume":"9","author":"Jiao","year":"2021","journal-title":"IEEE Access"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"107361","DOI":"10.1016\/j.measurement.2019.107361","article-title":"Improved multi-scale entropy and it\u2019s application in rolling bearing fault feature extraction","volume":"152","author":"Zhao","year":"2020","journal-title":"Measurement"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"035101","DOI":"10.1088\/1361-6501\/aca217","article-title":"Support vector machine fault diagnosis based on sparse scaling convex hull","volume":"34","author":"Song","year":"2023","journal-title":"Meas. Sci. Technol."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"107070","DOI":"10.1016\/j.compeleceng.2021.107070","article-title":"Fault diagnosis of mine asynchronous motor based on MEEMD energy entropy and ANN","volume":"92","author":"Yang","year":"2021","journal-title":"Comput. Electr. Eng."}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/25\/8\/1111\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:18:29Z","timestamp":1760127509000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/25\/8\/1111"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,25]]},"references-count":47,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2023,8]]}},"alternative-id":["e25081111"],"URL":"https:\/\/doi.org\/10.3390\/e25081111","relation":{},"ISSN":["1099-4300"],"issn-type":[{"value":"1099-4300","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,7,25]]}}}