{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T16:08:43Z","timestamp":1784736523101,"version":"3.55.0"},"reference-count":44,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2023,10,21]],"date-time":"2023-10-21T00:00:00Z","timestamp":1697846400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["42174189"],"award-info":[{"award-number":["42174189"]}]},{"name":"National Natural Science Foundation of China","award":["41974176"],"award-info":[{"award-number":["41974176"]}]},{"name":"National Natural Science Foundation of China","award":["61772086"],"award-info":[{"award-number":["61772086"]}]},{"name":"National Natural Science Foundation of China","award":["T2021028"],"award-info":[{"award-number":["T2021028"]}]},{"name":"National Natural Science Foundation of China","award":["2022ZDYF017"],"award-info":[{"award-number":["2022ZDYF017"]}]},{"name":"National Natural Science Foundation of China","award":["2023YFZD024"],"award-info":[{"award-number":["2023YFZD024"]}]},{"name":"National Natural Science Foundation of China","award":["YJS202312"],"award-info":[{"award-number":["YJS202312"]}]},{"name":"Hubei Province Higher Education Institutions Outstanding Young and Middle-aged Science and Technology Innovation Team Project","award":["42174189"],"award-info":[{"award-number":["42174189"]}]},{"name":"Hubei Province Higher Education Institutions Outstanding Young and Middle-aged Science and Technology Innovation Team Project","award":["41974176"],"award-info":[{"award-number":["41974176"]}]},{"name":"Hubei Province Higher Education Institutions Outstanding Young and Middle-aged Science and Technology Innovation Team Project","award":["61772086"],"award-info":[{"award-number":["61772086"]}]},{"name":"Hubei Province Higher Education Institutions Outstanding Young and Middle-aged Science and Technology Innovation Team Project","award":["T2021028"],"award-info":[{"award-number":["T2021028"]}]},{"name":"Hubei Province Higher Education Institutions Outstanding Young and Middle-aged Science and Technology Innovation Team Project","award":["2022ZDYF017"],"award-info":[{"award-number":["2022ZDYF017"]}]},{"name":"Hubei Province Higher Education Institutions Outstanding Young and Middle-aged Science and Technology Innovation Team Project","award":["2023YFZD024"],"award-info":[{"award-number":["2023YFZD024"]}]},{"name":"Hubei Province Higher Education Institutions Outstanding Young and Middle-aged Science and Technology Innovation Team Project","award":["YJS202312"],"award-info":[{"award-number":["YJS202312"]}]},{"name":"Major Science and Technology Plan Project of Jingmen City","award":["42174189"],"award-info":[{"award-number":["42174189"]}]},{"name":"Major Science and Technology Plan Project of Jingmen City","award":["41974176"],"award-info":[{"award-number":["41974176"]}]},{"name":"Major Science and Technology Plan Project of Jingmen City","award":["61772086"],"award-info":[{"award-number":["61772086"]}]},{"name":"Major Science and Technology Plan Project of Jingmen City","award":["T2021028"],"award-info":[{"award-number":["T2021028"]}]},{"name":"Major Science and Technology Plan Project of Jingmen City","award":["2022ZDYF017"],"award-info":[{"award-number":["2022ZDYF017"]}]},{"name":"Major Science and Technology Plan Project of Jingmen City","award":["2023YFZD024"],"award-info":[{"award-number":["2023YFZD024"]}]},{"name":"Major Science and Technology Plan Project of Jingmen City","award":["YJS202312"],"award-info":[{"award-number":["YJS202312"]}]},{"name":"Jingchu Institute of Technology Joint Training Graduate Research Special Fund Project","award":["42174189"],"award-info":[{"award-number":["42174189"]}]},{"name":"Jingchu Institute of Technology Joint Training Graduate Research Special Fund Project","award":["41974176"],"award-info":[{"award-number":["41974176"]}]},{"name":"Jingchu Institute of Technology Joint Training Graduate Research Special Fund Project","award":["61772086"],"award-info":[{"award-number":["61772086"]}]},{"name":"Jingchu Institute of Technology Joint Training Graduate Research Special Fund Project","award":["T2021028"],"award-info":[{"award-number":["T2021028"]}]},{"name":"Jingchu Institute of Technology Joint Training Graduate Research Special Fund Project","award":["2022ZDYF017"],"award-info":[{"award-number":["2022ZDYF017"]}]},{"name":"Jingchu Institute of Technology Joint Training Graduate Research Special Fund Project","award":["2023YFZD024"],"award-info":[{"award-number":["2023YFZD024"]}]},{"name":"Jingchu Institute of Technology Joint Training Graduate Research Special Fund Project","award":["YJS202312"],"award-info":[{"award-number":["YJS202312"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Since the rolling bearing fault signal captured by a vibration sensor contains a large amount of background noise, fault features cannot be accurately extracted. To address this problem, a rolling bearing fault feature extraction algorithm based on improved pelican optimization algorithm (IPOA)\u2013variable modal decomposition (VMD) and multipoint optimal minimum entropy deconvolution adjustment (MOMEDA) methods is proposed. Firstly, the pelican optimization algorithm (POA) was improved using a reverse learning strategy for dimensional-by-dimensional lens imaging and circle mapping, and the optimization performance of IPOA was verified. Secondly, the kurtosis-square envelope Gini coefficient criterion was used to select the optimal modal components from the decomposed components of the signal, and MOMEDA was used to process the optimal modal components in order to obtain the optimal deconvolution signal. Finally, the Teager energy operator (TEO) was employed to demodulate and analyze the optimally deconvoluted signal in order to enhance the transient shock component of the original fault signal. The effectiveness of the proposed method was verified using simulated and actual signals. The results showed that the proposed method can accurately extract failure characteristics in the presence of strong background noise interference.<\/jats:p>","DOI":"10.3390\/s23208620","type":"journal-article","created":{"date-parts":[[2023,10,21]],"date-time":"2023-10-21T12:59:48Z","timestamp":1697893188000},"page":"8620","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["A Rolling Bearing Fault Feature Extraction Algorithm Based on IPOA-VMD and MOMEDA"],"prefix":"10.3390","volume":"23","author":[{"given":"Kang","family":"Yi","sequence":"first","affiliation":[{"name":"School of Electronic Information, Yangtze University, Jingzhou 434023, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Changxin","family":"Cai","sequence":"additional","affiliation":[{"name":"School of Electronic Information, Yangtze University, Jingzhou 434023, China"},{"name":"Hubei Key Laboratory of Drilling and Production Engineering for Oil and Gas, Wuhan 430100, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7831-9447","authenticated-orcid":false,"given":"Wentao","family":"Tang","sequence":"additional","affiliation":[{"name":"School of Electronics and Information Engineering, Jingchu University of Technology, Jingmen 448000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin","family":"Dai","sequence":"additional","affiliation":[{"name":"School of Electronics and Information Engineering, Jingchu University of Technology, Jingmen 448000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fulin","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Electronics and Information Engineering, Jingchu University of Technology, Jingmen 448000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fangqing","family":"Wen","sequence":"additional","affiliation":[{"name":"Electronic and Communication Institute, China Three Gorges University, Yichang 443002, China"},{"name":"Institute of Vehicle Information Control and Network Technology, Hubei University of Automotive Technology, Shiyan 442002, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,10,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Brusa, E., Delprete, C., Gargiuli, S., and Giorio, L. (2023). Screening of Discrete Wavelet Transform Parameters for the Denoising of Rolling Bearing Signals in Presence of Localised Defects. Sensors, 23.","DOI":"10.3390\/s23010008"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"128442","DOI":"10.1016\/j.energy.2023.128442","article-title":"A Deep Feature Learning Method for Remaining Useful Life Prediction of Drilling Pumps","volume":"282","author":"Guo","year":"2023","journal-title":"Energy"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Di Maggio, L.G. (2023). Intelligent Fault Diagnosis of Industrial Bearings Using Transfer Learning and CNNs Pre-Trained for Audio Classification. Sensors, 23.","DOI":"10.3390\/s23010211"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Mohiuddin, M., Islam, M.S., Islam, S., Miah, M.S., and Niu, M.-B. (2023). Intelligent Fault Diagnosis of Rolling Element Bearings Based on Modified AlexNet. Sensors, 23.","DOI":"10.3390\/s23187764"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"3848","DOI":"10.1109\/JSEN.2022.3232707","article-title":"A Feature Extraction Method Using VMD and Improved Envelope Spectrum Entropy for Rolling Bearing Fault Diagnosis","volume":"23","author":"Yang","year":"2023","journal-title":"IEEE Sens. J."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"107943","DOI":"10.1016\/j.ymssp.2021.107943","article-title":"Adaptive Periodic Mode Decomposition and Its Application in Rolling Bearing Fault Diagnosis","volume":"161","author":"Cheng","year":"2021","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Wang, X., Guo, Y., Wen, F., He, J., and Truong, T.-K. (2023). EMVS-MIMO Radar with Sparse Rx Geometry: Tensor Modeling and 2D Direction Finding. IEEE Trans. Aerosp. Electron. Syst., 1\u201314.","DOI":"10.1109\/TAES.2023.3297570"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"893","DOI":"10.1109\/LSP.2023.3296038","article-title":"2D-DOA Estimation for Coherent Signals via a Polarized Uniform Rectangular Array","volume":"30","author":"Zhang","year":"2023","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"113306","DOI":"10.1016\/j.measurement.2023.113306","article-title":"A Novel Denoising Method of the Hydro-Turbine Runner for Fault Signal Based on WT-EEMD","volume":"219","author":"Dao","year":"2023","journal-title":"Measurement"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2891","DOI":"10.1109\/TIM.2019.2928534","article-title":"A Novel Rolling Bearing Fault Diagnosis Method Based on Empirical Wavelet Transform and Spectral Trend","volume":"69","author":"Xu","year":"2020","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"123813","DOI":"10.1109\/ACCESS.2020.3006030","article-title":"An Improved Empirical Mode Decomposition Based on Adaptive Weighted Rational Quartic Spline for Rolling Bearing Fault Diagnosis","volume":"8","author":"Ye","year":"2020","journal-title":"IEEE Access"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1016\/j.ymssp.2013.04.005","article-title":"Generalized Empirical Mode Decomposition and Its Applications to Rolling Element Bearing Fault Diagnosis","volume":"40","author":"Zheng","year":"2013","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"109986","DOI":"10.1016\/j.measurement.2021.109986","article-title":"Research on Test Bench Bearing Fault Diagnosis of Improved EEMD Based on Improved Adaptive Resonance Technology","volume":"185","author":"Li","year":"2021","journal-title":"Measurement"},{"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","first-page":"122108","DOI":"10.1016\/j.energy.2021.122108","article-title":"Fault Diagnosis of Flywheel Bearing Based on Parameter Optimization Variational Mode Decomposition Energy Entropy and Deep Learning","volume":"239","author":"He","year":"2022","journal-title":"Energy"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"512","DOI":"10.1016\/j.ymssp.2016.08.042","article-title":"Independence-Oriented VMD to Identify Fault Feature for Wheel Set Bearing Fault Diagnosis of High Speed Locomotive","volume":"85","author":"Li","year":"2017","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"064001","DOI":"10.1088\/1361-6501\/acb83d","article-title":"Weak Signal Enhancement for Rolling Bearing Fault Diagnosis Based on Adaptive Optimized VMD and SR under Strong Noise Background","volume":"34","author":"Luo","year":"2023","journal-title":"Meas. Sci. Technol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1016\/j.ymssp.2017.11.029","article-title":"A Parameter-Adaptive VMD Method Based on Grasshopper Optimization Algorithm to Analyze Vibration Signals from Rotating Machinery","volume":"108","author":"Zhang","year":"2018","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Yang, J., Zhou, C., Li, X., Pan, A., and Yang, T. (2023). A Fault Feature Extraction Method Based on Improved VMD Multi-Scale Dispersion Entropy and TVD-CYCBD. Entropy, 25.","DOI":"10.3390\/e25020277"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Ding, J., Huang, L., Xiao, D., and Li, X. (2020). GMPSO-VMD Algorithm and Its Application to Rolling Bearing Fault Feature Extraction. Sensors, 20.","DOI":"10.3390\/s20071946"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"110798","DOI":"10.1016\/j.measurement.2022.110798","article-title":"Adaptive Variational Mode Decomposition Based on Archimedes Optimization Algorithm and Its Application to Bearing Fault Diagnosis","volume":"191","author":"Wang","year":"2022","journal-title":"Measurement"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Mei, X., Cui, Z., Sheng, Q., Zhou, J., and Li, C. (2023). Application of the Improved POA-RF Model in Predicting the Strength and Energy Absorption Property of a Novel Aseismic Rubber-Concrete Material. Materials, 16.","DOI":"10.3390\/ma16031286"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"906","DOI":"10.1016\/j.ymssp.2006.02.005","article-title":"Enhancement of Autoregressive Model Based Gear Tooth Fault Detection Technique by the Use of Minimum Entropy Deconvolution Filter","volume":"21","author":"Endo","year":"2007","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1016\/j.ymssp.2012.06.010","article-title":"Maximum Correlated Kurtosis Deconvolution and Application on Gear Tooth Chip Fault Detection","volume":"33","author":"McDonald","year":"2012","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"461","DOI":"10.1016\/j.ymssp.2016.05.036","article-title":"Multipoint Optimal Minimum Entropy Deconvolution and Convolution Fix: Application to Vibration Fault Detection","volume":"82","author":"McDonald","year":"2017","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"15732","DOI":"10.1109\/JSEN.2023.3277516","article-title":"Weak Fault Feature Extraction Method of Rolling Bearings Based on MVO-MOMEDA under Strong Noise Interference","volume":"23","author":"Lv","year":"2023","journal-title":"IEEE Sens. J."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"503","DOI":"10.1016\/j.isatra.2021.10.033","article-title":"Adaptive MOMEDA Based on Improved Advance-Retreat Algorithm for Fault Features Extraction of Axial Piston Pump","volume":"128","author":"Xiao","year":"2022","journal-title":"ISA Trans."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1016\/j.measurement.2019.03.033","article-title":"Research and Application of Improved Adaptive MOMEDA Fault Diagnosis Method","volume":"140","author":"Wang","year":"2019","journal-title":"Measurement"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"509","DOI":"10.1016\/j.sigpro.2015.09.041","article-title":"Filter Bank Property of Variational Mode Decomposition and Its Applications","volume":"120","author":"Wang","year":"2016","journal-title":"Signal Process."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Trojovsk\u00fd, P., and Dehghani, M. (2022). Pelican Optimization Algorithm: A Novel Nature-Inspired Algorithm for Engineering Applications. Sensors, 22.","DOI":"10.3390\/s22030855"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"308","DOI":"10.1016\/j.engappai.2008.07.006","article-title":"Use of Particle Swarm Optimization for Machinery Fault Detection","volume":"22","author":"Samanta","year":"2009","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.advengsoft.2013.12.007","article-title":"Grey Wolf Optimizer","volume":"69","author":"Mirjalili","year":"2014","journal-title":"Adv. Eng. Softw."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1016\/j.advengsoft.2016.01.008","article-title":"The Whale Optimization Algorithm","volume":"95","author":"Mirjalili","year":"2016","journal-title":"Adv. Eng. Softw."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"113636","DOI":"10.1016\/j.chaos.2023.113636","article-title":"A Hyperchaotic Map with Distance-Increasing Pairs of Coexisting Attractors and Its Application in the Pelican Optimization Algorithm","volume":"173","author":"Ge","year":"2023","journal-title":"Chaos Solitons Fractals"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"4385","DOI":"10.1007\/s00521-018-3343-2","article-title":"Chaotic Grasshopper Optimization Algorithm for Global Optimization","volume":"31","author":"Arora","year":"2019","journal-title":"Neural Comput. Appl."},{"key":"ref_36","first-page":"2475460","article-title":"A Multistrategy-Integrated Learning Sparrow Search Algorithm and Optimization of Engineering Problems","volume":"2022","author":"Wang","year":"2022","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"66723","DOI":"10.1109\/ACCESS.2018.2873782","article-title":"The Entropy Algorithm and Its Variants in the Fault Diagnosis of Rotating Machinery: A Review","volume":"6","author":"Li","year":"2018","journal-title":"IEEE Access"},{"key":"ref_38","first-page":"3518912","article-title":"Feature Extraction Based on Hierarchical Improved Envelope Spectrum Entropy for Rolling Bearing Fault Diagnosis","volume":"72","author":"Chen","year":"2023","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"125001","DOI":"10.1088\/1361-6501\/aa8a57","article-title":"Improvement of Kurtosis-Guided-Grams via Gini Index for Bearing Fault Feature Identification","volume":"28","author":"Miao","year":"2017","journal-title":"Meas. Sci. Technol."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"108333","DOI":"10.1016\/j.ymssp.2021.108333","article-title":"Practical Framework of Gini Index in the Application of Machinery Fault Feature Extraction","volume":"165","author":"Miao","year":"2022","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"611","DOI":"10.1016\/j.isatra.2023.02.017","article-title":"Periodicity Measure of Cyclo-Stationary Impulses Based on Low Sparsity of Gini Index and Its Application to Bearing Diagnosis","volume":"138","author":"Liang","year":"2023","journal-title":"ISA Trans."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"108514","DOI":"10.1016\/j.measurement.2020.108514","article-title":"An Adaptive Variational Mode Decomposition Based on Sailfish Optimization Algorithm and Gini Index for Fault Identification in Rolling Bearings","volume":"173","author":"Nassef","year":"2021","journal-title":"Measurement"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1016\/j.isatra.2018.05.017","article-title":"Incipient Fault Feature Extraction of Rolling Bearings Based on the MVMD and Teager Energy Operator","volume":"80","author":"Ma","year":"2018","journal-title":"ISA Trans."},{"key":"ref_44","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\u201365","author":"Smith","year":"2015","journal-title":"Mech. Syst. Signal Process."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/20\/8620\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:09:30Z","timestamp":1760130570000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/20\/8620"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,21]]},"references-count":44,"journal-issue":{"issue":"20","published-online":{"date-parts":[[2023,10]]}},"alternative-id":["s23208620"],"URL":"https:\/\/doi.org\/10.3390\/s23208620","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,10,21]]}}}