{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T02:33:41Z","timestamp":1777430021096,"version":"3.51.4"},"reference-count":20,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2015,11,20]],"date-time":"2015-11-20T00:00:00Z","timestamp":1447977600000},"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":["51222503"],"award-info":[{"award-number":["51222503"]}],"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":["51475355"],"award-info":[{"award-number":["51475355"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Provincial Natural Science Foundation Research Project of Shaanxi","award":["2013JQ7011"],"award-info":[{"award-number":["2013JQ7011"]}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["2012jdgz01"],"award-info":[{"award-number":["2012jdgz01"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["CXTD2014001"],"award-info":[{"award-number":["CXTD2014001"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The early fault characteristics of rolling element bearings carried by vibration signals are quite weak because the signals are generally masked by heavy background noise. To extract the weak fault characteristics of bearings from the signals, an improved spectral kurtosis (SK) method is proposed based on maximum correlated kurtosis deconvolution (MCKD). The proposed method combines the ability of MCKD in indicating the periodic fault transients and the ability of SK in locating these transients in the frequency domain. A simulation signal overwhelmed by heavy noise is used to demonstrate the effectiveness of the proposed method. The results show that MCKD is beneficial to clarify the periodic impulse components of the bearing signals, and the method is able to detect the resonant frequency band of the signal and extract its fault characteristic frequency. Through analyzing actual vibration signals collected from wind turbines and hot strip rolling mills, we confirm that by using the proposed method, it is possible to extract fault characteristics and diagnose early faults of rolling element bearings. Based on the comparisons with the SK method, it is verified that the proposed method is more suitable to diagnose early faults of rolling element bearings.<\/jats:p>","DOI":"10.3390\/s151129363","type":"journal-article","created":{"date-parts":[[2015,11,24]],"date-time":"2015-11-24T01:57:02Z","timestamp":1448330222000},"page":"29363-29377","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":100,"title":["Early Fault Diagnosis of Bearings Using an Improved Spectral Kurtosis by Maximum Correlated Kurtosis Deconvolution"],"prefix":"10.3390","volume":"15","author":[{"given":"Feng","family":"Jia","sequence":"first","affiliation":[{"name":"State Key Laboratory for Manufacturing Systems Engineering, Xi\u2019an Jiaotong University, No. 28 Xianning West Road, Xi\u2019an 710049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yaguo","family":"Lei","sequence":"additional","affiliation":[{"name":"State Key Laboratory for Manufacturing Systems Engineering, Xi\u2019an Jiaotong University, No. 28 Xianning West Road, Xi\u2019an 710049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongkai","family":"Shan","sequence":"additional","affiliation":[{"name":"State Key Laboratory for Manufacturing Systems Engineering, Xi\u2019an Jiaotong University, No. 28 Xianning West Road, Xi\u2019an 710049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jing","family":"Lin","sequence":"additional","affiliation":[{"name":"State Key Laboratory for Manufacturing Systems Engineering, Xi\u2019an Jiaotong University, No. 28 Xianning West Road, Xi\u2019an 710049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2015,11,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1551","DOI":"10.1016\/j.measurement.2012.12.011","article-title":"Fault diagnosis of rotating machinery based on the statistical parameters of wavelet packet paving and a generic support vector regressive classifier","volume":"46","author":"Shen","year":"2013","journal-title":"Measurement"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"16950","DOI":"10.3390\/s131216950","article-title":"Fault diagnosis of rotating machinery based on an adaptive ensemble empirical mode decomposition","volume":"13","author":"Lei","year":"2013","journal-title":"Sensors"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"485","DOI":"10.1016\/j.ymssp.2010.07.017","article-title":"Rolling element bearing diagnostics\u2014A tutorial","volume":"25","author":"Randall","year":"2011","journal-title":"Mech. 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