{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,27]],"date-time":"2026-01-27T07:57:04Z","timestamp":1769500624763,"version":"3.49.0"},"reference-count":44,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2016,6,28]],"date-time":"2016-06-28T00:00:00Z","timestamp":1467072000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Key Technologies R&amp;D Program of China","award":["I16B300011"],"award-info":[{"award-number":["I16B300011"]}]},{"name":"State Key Laboratory independent project of Beijing Jiaotong University","award":["I16K00100"],"award-info":[{"award-number":["I16K00100"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Roller bearing plays a significant role in industrial sectors. To improve the ability of roller bearing fault diagnosis under multi-rotating situation, this paper proposes a novel roller bearing fault characteristic: the Amplitude Modulation (AM) based correntropy extracted from the Intrinsic Mode Functions (IMFs), which are decomposed by Fast Ensemble Empirical mode decomposition (FEEMD) and employ Least Square Support Vector Machine (LSSVM) to implement intelligent fault identification. Firstly, the roller bearing vibration acceleration signal is decomposed by FEEMD to extract IMFs. Secondly, IMF correntropy matrix (IMFCM) as the fault feature matrix is calculated from the AM-correntropy model of the primary vibration signal and IMFs. Furthermore, depending on LSSVM, the fault identification results of the roller bearing are obtained. Through the bearing identification experiments in stationary rotating conditions, it was verified that IMFCM generates more stable and higher diagnosis accuracy than conventional fault features such as energy moment, fuzzy entropy, and spectral kurtosis. Additionally, it proves that IMFCM has more diagnosis robustness than conventional fault features under cross-mixed roller bearing operating conditions. The diagnosis accuracy was more than 84% for the cross-mixed operating condition, which is much higher than the traditional features. In conclusion, it was proven that FEEMD-IMFCM-LSSVM is a reliable technology for roller bearing fault diagnosis under the constant or multi-positioned operating conditions, and as such, it possesses potential prospects for a broad application of uses.<\/jats:p>","DOI":"10.3390\/e18070242","type":"journal-article","created":{"date-parts":[[2016,6,29]],"date-time":"2016-06-29T06:20:33Z","timestamp":1467181233000},"page":"242","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Fast EEMD Based AM-Correntropy Matrix and Its Application on Roller Bearing Fault Diagnosis"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5125-2265","authenticated-orcid":false,"given":"Yunxiao","family":"Fu","sequence":"first","affiliation":[{"name":"State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, China"},{"name":"School of Electric Engineering, Beijing Jiaotong University, Beijing 100044, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Limin","family":"Jia","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, China"},{"name":"Beijing Research Center of Urban Traffic Information Sensing and Service Technologies, Beijing Jiaotong University, Beijing 100044, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yong","family":"Qin","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, China"},{"name":"Beijing Research Center of Urban Traffic Information Sensing and Service Technologies, Beijing Jiaotong University, Beijing 100044, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jie","family":"Yang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, China"},{"name":"School of Electrical Engineering and Automation, Jiangxi University of Science and Technology, Ganzhou 341000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ding","family":"Fu","sequence":"additional","affiliation":[{"name":"School of Business, University of Wollongong Australia, Wollongong 2519, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2016,6,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"865","DOI":"10.1007\/s11012-014-9968-z","article-title":"Early damage detection of roller bearings using wavelet packet decomposition, ensemble empirical mode decomposition and support vector machine","volume":"50","author":"Tabrizi","year":"2015","journal-title":"Meccanica"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1846","DOI":"10.1109\/TIE.2014.2361115","article-title":"Induction machine bearing faults detection by means of statistical processing of the stray flux measurement","volume":"62","author":"Frosini","year":"2015","journal-title":"IEEE Trans. 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