{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T18:28:31Z","timestamp":1772735311820,"version":"3.50.1"},"reference-count":23,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2015,5,11]],"date-time":"2015-05-11T00:00:00Z","timestamp":1431302400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>As train loads and travel speeds have increased over time, railway axle bearings have become critical elements which require more efficient non-destructive inspection and fault diagnostics methods. This paper presents a novel and adaptive procedure based on ensemble empirical mode decomposition (EEMD) and Hilbert marginal spectrum for multi-fault diagnostics of axle bearings. EEMD overcomes the limitations that often hypothesize about data and computational efforts that restrict the application of signal processing techniques. The outputs of this adaptive approach are the intrinsic mode functions that are treated with the Hilbert transform in order to obtain the Hilbert instantaneous frequency spectrum and marginal spectrum. Anyhow, not all the IMFs obtained by the decomposition should be considered into Hilbert marginal spectrum. The IMFs\u2019 confidence index arithmetic proposed in this paper is fully autonomous, overcoming the major limit of selection by user with experience, and allows the development of on-line tools. The effectiveness of the improvement is proven by the successful diagnosis of an axle bearing with a single fault or multiple composite faults, e.g., outer ring fault, cage fault and pin roller fault.<\/jats:p>","DOI":"10.3390\/s150510991","type":"journal-article","created":{"date-parts":[[2015,5,11]],"date-time":"2015-05-11T10:09:28Z","timestamp":1431338968000},"page":"10991-11011","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":65,"title":["Faults Diagnostics of Railway Axle Bearings Based on IMF\u2019s Confidence Index Algorithm for Ensemble EMD"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5641-6791","authenticated-orcid":false,"given":"Cai","family":"Yi","sequence":"first","affiliation":[{"name":"State Key Laboratory of Traction Power, Southwest Jiaotong University, Chengdu 610031,  China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianhui","family":"Lin","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Traction Power, Southwest Jiaotong University, Chengdu 610031,  China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weihua","family":"Zhang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Traction Power, Southwest Jiaotong University, Chengdu 610031,  China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianming","family":"Ding","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Traction Power, Southwest Jiaotong University, Chengdu 610031,  China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2015,5,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1016\/j.ymssp.2014.04.006","article-title":"Feature extraction of rolling bearing\u2019s early weak fault based on EEMD and tunable Q-factor wavelet transform","volume":"48","author":"Wang","year":"2014","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1051\/meca\/2011150","article-title":"Gearbox fault diagnosis using ensemble empirical mode decomposition (EEMD) and residual signal","volume":"13","author":"Mahgoun","year":"2012","journal-title":"Mech. Ind."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"821","DOI":"10.1016\/j.ymssp.2010.10.002","article-title":"Dignostics of gear fault based on EMD and automatic selection of intrinsic mode functions","volume":"25","author":"Ricci","year":"2011","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Symonds, N., Corni, I., Wood, R.J.K., Wasenczuk, A., and Vincent, D. (2015). Observing early stage rail axle bearing damage. Eng. Fail. Anal.","DOI":"10.1016\/j.engfailanal.2015.02.008"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"349","DOI":"10.1016\/j.wear.2010.10.003","article-title":"Axle box acceleration: Measurement and simulation for detection of short track defects","volume":"271","author":"Molodova","year":"2011","journal-title":"Wear"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1016\/j.engfailanal.2012.11.008","article-title":"Axle fracture of an ICE3 high speed train","volume":"35","author":"Klinger","year":"2013","journal-title":"Eng. Fail. Anal."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1016\/j.engfailanal.2012.11.007","article-title":"Structural assessment of railway axles\u2014A critical review","volume":"35","author":"Zerbst","year":"2013","journal-title":"Eng. Fail. Anal."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"214","DOI":"10.1016\/j.engfracmech.2012.09.029","article-title":"Safe life and damage tolerance aspects of railway axles\u2014A review","volume":"98","author":"Zerbst","year":"2013","journal-title":"Eng. Fract. Mech."},{"key":"ref_9","first-page":"47","article-title":"Sound Investment","volume":"99","author":"Marsh","year":"2013","journal-title":"Rail Eng."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2684","DOI":"10.1109\/TVT.2008.915505","article-title":"A Stationary System of Noncontact Temperature Measurement and Hotbox Detecting","volume":"57","author":"Milic","year":"2008","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_11","unstructured":"Owen, R. (2014). Institute of Acoustics."},{"key":"ref_12","first-page":"42","article-title":"Suggestions on Hot Box Prediction Way for Passenger Cars after Improvement of the Infrared System","volume":"6","author":"Zhang","year":"2009","journal-title":"Railw. Veh."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"903","DOI":"10.1098\/rspa.1998.0193","article-title":"The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis","volume":"454","author":"Huang","year":"1998","journal-title":"Proc. R. Soc. Lond. Ser."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Yang, Y., Deng, J., and Wu, C. (2009, January 26\u201328). Analysis of mode mixing phenomenon in the empical mode decomposition method. Proceedings of the Second International Symposium on Information Science Engineering, Shanghai, China.","DOI":"10.1109\/ISISE.2009.19"},{"key":"ref_15","unstructured":"Ricci, R., Pennacchi, P., Lombardi, M., and Mirabile, C. (2010, January 20\u201322). Failure diagnostics of a spiral bevel gearbox using EMD and HHT. Proceedings of the ISMA2010 Including USD, Leuven, Belgium."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1142\/S1793536909000047","article-title":"Ensemble empirical mode decomposition: A noise assisted data analysis method","volume":"1","author":"Wu","year":"2009","journal-title":"Adv. Adapt. Data Anal."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1327","DOI":"10.1016\/j.ymssp.2008.11.005","article-title":"Application of the EEMD method to rotor fault diagnosis of rotating machinery","volume":"23","author":"Lei","year":"2009","journal-title":"Mech. Syst. Signal Process"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"125701","DOI":"10.1088\/0957-0233\/20\/12\/125701","article-title":"Fault diagnosis of rotating machinery using an improved HHT based on EEMD and sensitive IMFs","volume":"20","author":"Lei","year":"2009","journal-title":"Measur. Sci. Technol."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2104","DOI":"10.1016\/j.ymssp.2010.03.003","article-title":"Performance enhancement of ensemble empirical mode decomposition","volume":"24","author":"Zhang","year":"2010","journal-title":"Mech. Syst. Signal Process"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/j.ymssp.2013.07.006","article-title":"Multi-fault diagnosis for rolling element bearings based on ensemble empirical mode decomposition and optimized support vector machines","volume":"41","author":"Zhang","year":"2013","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1016\/j.sigpro.2013.11.012","article-title":"Improved Hilbert-Huang transform based weak signal detection methodology and its application on incipient fault diagnosis and ECG signal analysis","volume":"98","author":"Yan","year":"2014","journal-title":"Signal Process."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1007\/s11265-012-0726-y","article-title":"Empirical mode decomposition: Real-time implementation and applications","volume":"73","author":"Eftekhar","year":"2013","journal-title":"J. Signal Process Syst."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2530","DOI":"10.3390\/s130202530","article-title":"Heart Sound Biometric System Based on Marginal Spectrum Analysis","volume":"13","author":"Zhao","year":"2013","journal-title":"Sensors"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/15\/5\/10991\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T20:46:04Z","timestamp":1760215564000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/15\/5\/10991"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2015,5,11]]},"references-count":23,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2015,5]]}},"alternative-id":["s150510991"],"URL":"https:\/\/doi.org\/10.3390\/s150510991","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2015,5,11]]}}}