{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,23]],"date-time":"2026-01-23T22:04:11Z","timestamp":1769205851572,"version":"3.49.0"},"reference-count":33,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2019,1,10]],"date-time":"2019-01-10T00:00:00Z","timestamp":1547078400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002855","name":"Ministry of Science and Technology of the People's Republic of China","doi-asserted-by":"publisher","award":["2011ZX069"],"award-info":[{"award-number":["2011ZX069"]}],"id":[{"id":"10.13039\/501100002855","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Natural Science Foundation","award":["61305065"],"award-info":[{"award-number":["61305065"]}]},{"DOI":"10.13039\/501100002367","name":"Chinese Academy of Sciences","doi-asserted-by":"publisher","award":["XDA17030100"],"award-info":[{"award-number":["XDA17030100"]}],"id":[{"id":"10.13039\/501100002367","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>As important sources in fault diagnosis of rotary machinery, vibration signals are usually processed in the time or frequency domain as features to distinguish different classes of faults. However, these kinds of processing methods always ignore the corresponding relations among multiple signals, resulting in information loss. In this paper, a new fault description strategy named vibration image is proposed, based on which three new kinds of features are extracted, containing coupling information between different channels of vibration signals. Additionally, a new feature fusion method called two-layer AdaBoost is designed to train the fault recognition model, which avoids overfitting when the dataset is not large enough. Features based on vibration images combined with two-layer AdaBoost are adopted to diagnose faults of rotary machinery. Taking an active magnetic bearing-rotor system as the experimental platform, a dataset with four classes of faults is collected and our algorithm achieves good performance. Meanwhile, features based on vibration images and two-layer AdaBoost are both proved to be efficient separately.<\/jats:p>","DOI":"10.3390\/s19020244","type":"journal-article","created":{"date-parts":[[2019,1,11]],"date-time":"2019-01-11T04:10:16Z","timestamp":1547179816000},"page":"244","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["Fault Diagnosis of Active Magnetic Bearing\u2013Rotor System via Vibration Images"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0372-7120","authenticated-orcid":false,"given":"Xunshi","family":"Yan","sequence":"first","affiliation":[{"name":"Institute of Nuclear and New Energy Technology, Tsinghua University, Beijing 100084, China"},{"name":"Key Laboratory of Advanced Reactor Engineering and Safety, Ministry of Education, Beijing 100084, China"},{"name":"Collaborative Innovation Center of Advanced Nuclear Energy Technology, Beijing 100084, China"}]},{"given":"Zhe","family":"Sun","sequence":"additional","affiliation":[{"name":"Institute of Nuclear and New Energy Technology, Tsinghua University, Beijing 100084, China"},{"name":"Key Laboratory of Advanced Reactor Engineering and Safety, Ministry of Education, Beijing 100084, China"},{"name":"Collaborative Innovation Center of Advanced Nuclear Energy Technology, Beijing 100084, China"}]},{"given":"Jingjing","family":"Zhao","sequence":"additional","affiliation":[{"name":"Institute of Nuclear and New Energy Technology, Tsinghua University, Beijing 100084, China"},{"name":"Key Laboratory of Advanced Reactor Engineering and Safety, Ministry of Education, Beijing 100084, China"},{"name":"Collaborative Innovation Center of Advanced Nuclear Energy Technology, Beijing 100084, China"}]},{"given":"Zhengang","family":"Shi","sequence":"additional","affiliation":[{"name":"Institute of Nuclear and New Energy Technology, Tsinghua University, Beijing 100084, China"},{"name":"Key Laboratory of Advanced Reactor Engineering and Safety, Ministry of Education, Beijing 100084, China"},{"name":"Collaborative Innovation Center of Advanced Nuclear Energy Technology, Beijing 100084, China"}]},{"given":"Chen-An","family":"Zhang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of High Temperature Gas Dynamics, Institute of Mechanics, Chinese Academy of Sciences, Beijing 100190, China"}]}],"member":"1968","published-online":{"date-parts":[[2019,1,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1016\/j.ymssp.2017.06.012","article-title":"A review on data-driven fault severity assessment in rolling bearings","volume":"99","author":"Cerrada","year":"2018","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_2","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. Acoust."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.measurement.2017.08.036","article-title":"Early fault diagnosis of bearing and stator faults of the single-phase induction motor using acoustic signals","volume":"113","author":"Glowacz","year":"2018","journal-title":"Measurement"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1016\/j.ymssp.2018.02.016","article-title":"Artificial intelligence for fault diagnosis of rotating machinery: A review","volume":"108","author":"Liu","year":"2018","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Caesarendra, W., and Tjahjowidodo, T. (2017). A Review of Feature Extraction Methods in Vibration-Based Condition Monitoring and Its Application for Degradation Trend Estimation of Low-Speed Slew Bearing. Machines, 5.","DOI":"10.3390\/machines5040021"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1016\/S0003-682X(97)00018-2","article-title":"Statistical analysis of sound and vibration signals for monitoring rolling element bearing condition","volume":"53","author":"Heng","year":"1998","journal-title":"Appl. Acoust."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"2525","DOI":"10.1016\/j.measurement.2013.04.061","article-title":"Rotating machine fault diagnosis using dimension reduction with linear local tangent space alignment","volume":"46","author":"Li","year":"2013","journal-title":"Measurement"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1313","DOI":"10.1016\/j.ymssp.2008.10.004","article-title":"Fault diagnosis based on walsh transform and rough sets","volume":"23","author":"Xiang","year":"2009","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"276","DOI":"10.1016\/S0925-8388(00)00964-6","article-title":"Effectiveness of new spectral tools in the anomaly detection of rolling element bearings","volume":"310","author":"Pineyro","year":"2000","journal-title":"J. Alloys Compd."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1016\/j.ymssp.2016.11.019","article-title":"Fusion information entropy method of rolling bearing fault diagnosis based on n-dimensional characteristic parameter distance","volume":"88","author":"Ai","year":"2017","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1419","DOI":"10.1016\/j.eswa.2009.06.060","article-title":"A multidimensional hybrid intelligent method for gear fault diagnosis","volume":"37","author":"Lei","year":"2010","journal-title":"Expert Syst. Appl."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2607","DOI":"10.1016\/j.ymssp.2006.12.004","article-title":"Bearing fault diagnosis using FFT of intrinsic mode functions in hilbertchuang transform","volume":"21","author":"Rai","year":"2007","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"162","DOI":"10.1016\/j.ymssp.2016.03.009","article-title":"A new rolling bearing fault diagnosis method based on gft impulse component extraction","volume":"81","author":"Ou","year":"2016","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1638","DOI":"10.1016\/j.neucom.2011.01.021","article-title":"Rolling element bearing fault diagnosis using wavelet transform","volume":"74","author":"Kankar","year":"2011","journal-title":"Neurocomputing"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"3","DOI":"10.4316\/AECE.2018.02001","article-title":"Improved wind speed prediction using empirical mode decomposition","volume":"18","author":"Zhang","year":"2018","journal-title":"Adv. Electr. Comput. Eng."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1016\/j.neucom.2017.07.048","article-title":"Robust locally linear embedding algorithm for machinery fault diagnosis","volume":"273","author":"Zhang","year":"2018","journal-title":"Neurocomputing"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"330","DOI":"10.1016\/j.jsv.2017.03.029","article-title":"Sparse discriminant manifold projections for bearing fault diagnosis","volume":"399","author":"Chen","year":"2017","journal-title":"J. Sound Vib."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"278","DOI":"10.1016\/j.ymssp.2017.09.026","article-title":"A novel method for intelligent fault diagnosis of rolling bearings using ensemble deep auto-encoders","volume":"102","author":"Shao","year":"2018","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"349","DOI":"10.1016\/j.ymssp.2018.03.025","article-title":"Deep normalized convolutional neural network for imbalanced fault classification of machinery and its understanding via visualization","volume":"110","author":"Jia","year":"2018","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1016\/j.isatra.2018.04.005","article-title":"Fault diagnosis of rolling bearings with recurrent neural network-based autoencoders","volume":"77","author":"Liu","year":"2018","journal-title":"ISA Trans."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1685","DOI":"10.1016\/j.ymssp.2008.01.005","article-title":"Fault diagnosis based on walsh transform and support vector machine","volume":"22","author":"Xiang","year":"2008","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_22","unstructured":"He, D., Shi, Z., Yan, X., Zhao, J., Liu, X., Sun, Z., and Yang, G. (2016, January 6\u201310). Study on fault diagnosis system of active magnetic bearing. Proceedings of the International Topical Meeting on High Temperature Reactor Technology, Las Vegas, NV, USA."},{"key":"ref_23","unstructured":"Schweitzer, G., and Maslen, E.H. (2009). Magnetic Bearings: Theory, Design, and Application to Rotating Machinery, Springer."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1016\/j.ymssp.2018.07.030","article-title":"Nonlinear dynamic characteristics analysis of active magnetic bearing system based on cell mapping method with a case study","volume":"117","author":"Sun","year":"2019","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1016\/j.anucene.2018.04.017","article-title":"Helium blower test based on aerodynamic force simulation","volume":"118","author":"Zhao","year":"2018","journal-title":"Ann. Nucl. Energy"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1016\/j.ymssp.2017.01.043","article-title":"Gnar-garch model and its application in feature extraction for rolling bearing fault diagnosis","volume":"93","author":"Ma","year":"2017","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Zhang, N., Wu, L., Yang, J., and Guan, Y. (2018). Naive bayes bearing fault diagnosis based on enhanced independence of data. Sensors, 18.","DOI":"10.3390\/s18020463"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.measurement.2017.07.017","article-title":"A convolutional neural network based feature learning and fault diagnosis method for the condition monitoring of gearbox","volume":"111","author":"Jing","year":"2017","journal-title":"Measurement"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Guo, S., Yang, T., Gao, W., and Zhang, C. (2018). A novel fault diagnosis method for rotating machinery based on a convolutional neural network. Sensors, 18.","DOI":"10.3390\/s18051429"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1023\/A:1007614523901","article-title":"Improved boosting algorithms using confidence-rated predictions","volume":"37","author":"Schapire","year":"1999","journal-title":"Mach. Learn."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1214\/aos\/1016218223","article-title":"Additive logistic regression: A statistical view of boosting","volume":"28","author":"Friedman","year":"2000","journal-title":"Ann. Stat."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1117\/1.3530070","article-title":"Action recognition via cumulative histogram of multiple features","volume":"50","author":"Yan","year":"2011","journal-title":"Opt. Eng."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1016\/j.neucom.2012.02.002","article-title":"Recognizing human actions using a new descriptor based on spatialctemporal interest points and weighted-output classifier","volume":"87","author":"Yan","year":"2012","journal-title":"Neurocomputing"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/2\/244\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:24:55Z","timestamp":1760185495000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/2\/244"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,1,10]]},"references-count":33,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2019,1]]}},"alternative-id":["s19020244"],"URL":"https:\/\/doi.org\/10.3390\/s19020244","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,1,10]]}}}