{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,23]],"date-time":"2026-01-23T07:26:53Z","timestamp":1769153213519,"version":"3.49.0"},"reference-count":29,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2021,3,23]],"date-time":"2021-03-23T00:00:00Z","timestamp":1616457600000},"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":["61633005"],"award-info":[{"award-number":["61633005"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This study proposes a novel resonance demodulation frequency band selection method named the initial center frequency-guided filter (ICFGF) to diagnose the bearing fault. The proposed technology has a better performance on resisting the interference from the random impulses. More explicitly, the ICFGF can be summarized as two steps. In the first step, a variance statistic index is applied to evaluate the energy spectrum distribution, which can adaptively determine the center frequency of the fault impulse and suppress the interference from random impulse effectively. In the second step, a modified mayfly optimization algorithm (MMA) is applied to search the optimal resonance demodulation frequency band based on the center frequency from the first step, which has faster convergence. Finally, the filtered signal is processed by the squared envelope spectrum technology. Results of the proposed method for signals from an outer fault bearing and a ball fault bearing indicate that the ICFGF works well to extract bearing fault feature. Furthermore, compared with some other methods, including fast kurtogram, ensemble empirical mode decomposition, and conditional variance-based selector technology, the ICFGF can extract the fault characteristic more accurately.<\/jats:p>","DOI":"10.3390\/s21062245","type":"journal-article","created":{"date-parts":[[2021,3,23]],"date-time":"2021-03-23T23:59:41Z","timestamp":1616543981000},"page":"2245","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["Bearing Fault Diagnosis Based on Energy Spectrum Statistics and Modified Mayfly Optimization Algorithm"],"prefix":"10.3390","volume":"21","author":[{"given":"Yuhu","family":"Liu","sequence":"first","affiliation":[{"name":"College of Automation, Chongqing University, Chongqing 400044, China"},{"name":"State Key Laboratory of Power Transmission Equipment and System Security and New Technology, Chongqing University, Chongqing 400044, China"}]},{"given":"Yi","family":"Chai","sequence":"additional","affiliation":[{"name":"College of Automation, Chongqing University, Chongqing 400044, China"},{"name":"State Key Laboratory of Power Transmission Equipment and System Security and New Technology, Chongqing University, Chongqing 400044, China"}]},{"given":"Bowen","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Automation, Chongqing University, Chongqing 400044, China"},{"name":"State Key Laboratory of Power Transmission Equipment and System Security and New Technology, Chongqing University, Chongqing 400044, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7330-2235","authenticated-orcid":false,"given":"Yiming","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Automation, Chongqing University, Chongqing 400044, China"},{"name":"State Key Laboratory of Power Transmission Equipment and System Security and New Technology, Chongqing University, Chongqing 400044, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,3,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"658","DOI":"10.1016\/j.measurement.2019.01.017","article-title":"A parameter-adaptive stochastic resonance based on whale optimization algorithm for weak signal detection for rotating machinery","volume":"136","author":"He","year":"2019","journal-title":"Measurement"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1016\/j.isatra.2018.11.033","article-title":"Time\u2013frequency analysis for bearing fault diagnosis using multiple Q-factor Gabor wavelets","volume":"87","author":"Zhang","year":"2019","journal-title":"ISA Trans."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"10773","DOI":"10.1007\/s00521-019-04612-z","article-title":"Intelligent bearing fault diagnosis using PCA\u2013DBN framework","volume":"32","author":"Zhu","year":"2020","journal-title":"Neural Comput. 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