{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T05:11:20Z","timestamp":1771650680819,"version":"3.50.1"},"reference-count":36,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2018,12,5]],"date-time":"2018-12-05T00:00:00Z","timestamp":1543968000000},"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":["Grant No. 51777074, 51475164"],"award-info":[{"award-number":["Grant No. 51777074, 51475164"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["2017XS134"],"award-info":[{"award-number":["2017XS134"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Rotor is a widely used and easily defected mechanical component. Thus, it is significant to develop effective techniques for rotor fault diagnosis. Fault signature extraction and state classification of the extracted signatures are two key steps for diagnosing rotor faults. To complete the accurate recognition of rotor states, a novel evaluation index named characteristic frequency band energy entropy (CFBEE) was proposed to extract the defective features of rotors, and support vector machine (SVM) was employed to automatically identify the rotor fault types. Specifically, the raw vibration signal of rotor was first analyzed by a joint time\u2013frequency method based on improved singular spectrum decomposition (ISSD) and Hilbert transform (HT) to derive its time\u2013frequency spectrum (TFS), which is named ISSD-HT TFS in this paper. Then, the CFBEE of the ISSD-HT TFS was calculated as the fault feature vector. Finally, SVM was used to complete the automatic identification of rotor faults. Simulated processing results indicate that ISSD improves the end effects of singular spectrum decomposition (SSD) and is superior to empirical mode decomposition (EMD) in extracting the sub-components of rotor vibration signal. The ISSD-HT TFS can more accurately reflect the time\u2013frequency information compared to the EMD-HT TFS. Experimental verification demonstrates that the proposed method can accurately identify rotor defect types and outperform some other methods.<\/jats:p>","DOI":"10.3390\/e20120932","type":"journal-article","created":{"date-parts":[[2018,12,5]],"date-time":"2018-12-05T12:22:00Z","timestamp":1544012520000},"page":"932","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":36,"title":["Rotor Fault Diagnosis Based on Characteristic Frequency Band Energy Entropy and Support Vector Machine"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0362-4700","authenticated-orcid":false,"given":"Bin","family":"Pang","sequence":"first","affiliation":[{"name":"Department of Mechanical Engineering, North China Electric Power University, Baoding 071000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guiji","family":"Tang","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, North China Electric Power University, Baoding 071000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chong","family":"Zhou","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, North China Electric Power University, Baoding 071000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tian","family":"Tian","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, North China Electric Power University, Baoding 071000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,12,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1016\/j.apacoust.2018.03.010","article-title":"Acoustic based fault diagnosis of three-phase induction motor","volume":"137","author":"Glowacz","year":"2018","journal-title":"Appl. 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