{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T00:59:49Z","timestamp":1778547589487,"version":"3.51.4"},"reference-count":51,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2020,7,3]],"date-time":"2020-07-03T00:00:00Z","timestamp":1593734400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["51605068"],"award-info":[{"award-number":["51605068"]}],"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":["61771087"],"award-info":[{"award-number":["61771087"]}],"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":["51475065"],"award-info":[{"award-number":["51475065"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>It is difficult to extract the fault signal features of locomotive rolling bearings and the accuracy of fault diagnosis is low. In this paper, a novel fault diagnosis method based on the optimized variational mode decomposition (VMD) and resonance demodulation technology, namely GNVRFD, is proposed to realize the fault diagnosis of locomotive rolling bearings. In the proposed GNVRFD method, the genetic algorithm and nonlinear programming are combined to design a novel parameter optimization algorithm to adaptively optimize the two parameters of the VMD. Then the optimized VMD is employed to decompose the collected vibration signal into a series of intrinsic mode functions (IMFs), and the kurtosis value of each IMF is calculated, respectively. According to the principle of maximum value, two most sensitive IMF components are selected to reconstruct the vibration signal. The resonance demodulation technology is used to decompose the reconstructed vibration signal in order to obtain the envelope spectrum, and the fault frequency of locomotive rolling bearings is effectively obtained. Finally, the actual data of rolling bearings is selected to testify the effectiveness of the proposed GNVRFD method. The experiment results demonstrate that the proposed GNVRFD method can more accurately and effectively diagnose the fault of locomotive rolling bearings by comparing with other fault diagnosis methods.<\/jats:p>","DOI":"10.3390\/e22070739","type":"journal-article","created":{"date-parts":[[2020,7,6]],"date-time":"2020-07-06T03:19:27Z","timestamp":1594005567000},"page":"739","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["Fault Diagnosis for Rolling Bearings Using Optimized Variational Mode Decomposition and Resonance Demodulation"],"prefix":"10.3390","volume":"22","author":[{"given":"Chunguang","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Electronics and Information Engineering, Dalian Jiaotong University, Dalian 116028, China"},{"name":"Traction Power State Key Laboratory of Southwest Jiaotong University, Chengdu 610031, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yao","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Electronics and Information Engineering, Dalian Jiaotong University, Dalian 116028, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wu","family":"Deng","sequence":"additional","affiliation":[{"name":"School of Electronics and Information Engineering, Dalian Jiaotong University, Dalian 116028, China"},{"name":"Traction Power State Key Laboratory of Southwest Jiaotong University, Chengdu 610031, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,7,3]]},"reference":[{"key":"ref_1","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_2","doi-asserted-by":"crossref","first-page":"115368","DOI":"10.1109\/ACCESS.2019.2936243","article-title":"Improved deep transfer auto-encoder for fault diagnosis of gearbox under variable working conditions with small training samples","volume":"7","author":"He","year":"2019","journal-title":"IEEE Access"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1016\/j.eswa.2019.03.043","article-title":"An efficient chaotic mutative moth-flame-inspired optimizer for global optimization tasks","volume":"129","author":"Xu","year":"2019","journal-title":"Expert Syst. 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