{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,26]],"date-time":"2025-10-26T14:32:50Z","timestamp":1761489170424,"version":"build-2065373602"},"reference-count":33,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2013,7,5]],"date-time":"2013-07-05T00:00:00Z","timestamp":1372982400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/3.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Timely and accurate condition monitoring and fault diagnosis of rotating machinery are very important to maintain a high degree of availability, reliability and operational safety. This paper presents a novel intelligent method based on local mean decomposition (LMD) and multi-class reproducing wavelet support vector machines (RWSVM), which is applied to diagnose rotating machinery faults. First, the sensor-based vibration signals measured from the rotating machinery are preprocessed by the LMD method and product functions (PFs) are produced. Second, statistic features are extracted to acquire more fault characteristic information from the sensitive PF. Finally, these features are fed into a multi-class RWSVM to identify the rotating machinery health conditions. The experimental results validate the effectiveness of the proposed RWSVM method in identifying rotating machinery fault patterns accurately and effectively and its superiority over that based on the general SVM.<\/jats:p>","DOI":"10.3390\/s130708679","type":"journal-article","created":{"date-parts":[[2013,7,5]],"date-time":"2013-07-05T12:28:23Z","timestamp":1373027303000},"page":"8679-8694","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["LMD Method and Multi-Class RWSVM of Fault Diagnosis for Rotating Machinery Using Condition Monitoring Information"],"prefix":"10.3390","volume":"13","author":[{"given":"Zhiwen","family":"Liu","sequence":"first","affiliation":[{"name":"State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University,  Xi'an 710049, China"}]},{"given":"Xuefeng","family":"Chen","sequence":"additional","affiliation":[{"name":"State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University,  Xi'an 710049, China"}]},{"given":"Zhengjia","family":"He","sequence":"additional","affiliation":[{"name":"State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University,  Xi'an 710049, China"}]},{"given":"Zhongjie","family":"Shen","sequence":"additional","affiliation":[{"name":"State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University,  Xi'an 710049, China"}]}],"member":"1968","published-online":{"date-parts":[[2013,7,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1593","DOI":"10.1016\/j.eswa.2007.08.072","article-title":"A new approach to intelligent fault diagnosis of rotating machinery","volume":"35","author":"Lei","year":"2008","journal-title":"Expert. Syst. Appl."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"696","DOI":"10.1016\/j.ymssp.2011.08.002","article-title":"Early fault diagnosis of rotating machinery based on wavelet packets\u2014Empirical mode decomposition feature extraction and neural network","volume":"27","author":"Bin","year":"2012","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1016\/S0888-3270(03)00075-X","article-title":"Application of the wavelet transform in machine condition monitoring and fault diagnostics: A review with bibliography","volume":"18","author":"Peng","year":"2004","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2300","DOI":"10.1016\/j.asoc.2010.08.011","article-title":"Fault diagnosis of ball bearings using continuous wavelet transform","volume":"11","author":"Kankar","year":"2011","journal-title":"Appl. Soft Comput."},{"key":"ref_5","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. Roy. Soc. London Ser. A"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.apor.2004.08.004","article-title":"Identification of the components of wave spectra by the Hilbert Huang transform method","volume":"26","author":"Veltcheva","year":"2004","journal-title":"Appl. Ocean Res."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"467","DOI":"10.1016\/j.ymssp.2008.03.007","article-title":"Diagnosis of subharmonic faults of large roating machinery based on EMD","volume":"23","author":"Wu","year":"2009","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_8","first-page":"1","article-title":"A Fault diagnosis approach for gears based on IMF AR model and SVM","volume":"21","author":"Cheng","year":"2008","journal-title":"EURASIP J. Advan. Signal Process."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"3182","DOI":"10.1016\/j.ymssp.2007.05.006","article-title":"EMD- and SVM-based on temperature drift model and compensation for a dynamically tuned gyroscope (DTG)","volume":"21","author":"Xu","year":"2007","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"443","DOI":"10.1098\/rsif.2005.0058","article-title":"The local mean decomposition and its application to EEG perception data","volume":"2","author":"Smith","year":"2005","journal-title":"J. R. Soc. Interface."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1016\/j.mechmachtheory.2012.04.008","article-title":"An order tracking technique for the gear fault diagnosis using local mean decomposition method","volume":"55","author":"Cheng","year":"2012","journal-title":"Mech. Mach. Theor."},{"key":"ref_12","first-page":"40","article-title":"Locomotive fault diagnosis based on local mean decomposition demodulating approach","volume":"45","author":"Chen","year":"2010","journal-title":"J. Xi'an Jiao Tong Univ."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1016\/j.mechmachtheory.2011.08.007","article-title":"Application of local mean decomposition to the surveillance and diagnostics of low-speed helical gearbox","volume":"47","author":"Wang","year":"2012","journal-title":"Mech. Mach. Theor."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Wang, Y., He, Z., and Zi, Y. (2010). A comparative study on the local mean decomposition and empirical mode decomposition and their applications to rotating machinery health diagnosis. J. Vib. Acoust.","DOI":"10.1115\/1.4000770"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"4168","DOI":"10.1016\/j.eswa.2009.11.006","article-title":"Incipient gear box fault diagnosis using discrete wavelet transform (DWT) for feature extraction and classification using artificial neural network (ANN)","volume":"37","author":"Saravanan","year":"2010","journal-title":"Expert Syst. Appl."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"7334","DOI":"10.1016\/j.eswa.2010.12.095","article-title":"EEMD method and WNN for fault diagnosis of locomotive roller bearings","volume":"38","author":"Lei","year":"2011","journal-title":"Expert Syst. Appl."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Vapnik, V. (1999). The Nature of Statistical Learning Theory, Springer-Verlag.","DOI":"10.1007\/978-1-4757-3264-1"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Burges, C.J.C. (1999). Advance in Kernel Methods\u2014Support Vector Learning, MIT Press.","DOI":"10.7551\/mitpress\/1130.001.0001"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1109\/TSMCB.2003.811113","article-title":"Wavelet support vector machine","volume":"34","author":"Zhang","year":"2004","journal-title":"IEEE Trans. Syst. Man Cybern. B."},{"key":"ref_20","first-page":"644","article-title":"Reproducing kernel space based on GABOR wavelet transform","volume":"2","author":"Deng","year":"2008","journal-title":"ICWAPR\u203208, Hong Kong:IEEE SMCS"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"318","DOI":"10.1016\/j.matcom.2007.12.004","article-title":"Bearing condition monitoring based on shock pulse method and improved redundant lifting scheme","volume":"79","author":"Li","year":"2008","journal-title":"Math. Comput. Simulat."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"419","DOI":"10.1038\/nature02341","article-title":"General conditions for predictivity in learning theory","volume":"428","author":"Poggio","year":"2004","journal-title":"Nature"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1023\/A:1018946025316","article-title":"Regularization networks and support vector machines","volume":"13","author":"Evgeniou","year":"2000","journal-title":"Adv. Comput. Math."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1098\/rsta.1909.0016","article-title":"Functions of positive and negative type and their connection with the theory of integral equation","volume":"209","author":"Mercer","year":"1909","journal-title":"Phil. Trans. Roy. Soc. London A"},{"key":"ref_25","first-page":"1485","article-title":"Frames, Reproducing kernels, Regularization and learning","volume":"6","author":"Rakotomamonjy","year":"2005","journal-title":"J. Mach. Learn. Res."},{"key":"ref_26","unstructured":"Suykens, J., and Horvath, G. (2002). Advances in Learning Theory: Methods, Models and Applications, IOS."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1090\/S0002-9947-1950-0051437-7","article-title":"Theory of reproducing kernels","volume":"68","author":"Aronszajn","year":"1950","journal-title":"Trans. Amer. Math. Soc."},{"key":"ref_28","unstructured":"Hsu, C.W., Chang, C,C., and Lin, C.J. (2003). A Practical Guide to Support Vector Classification, National Taiwan University."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"3782","DOI":"10.1016\/j.ins.2007.03.028","article-title":"Rough set based 1-v-1 and 1-v-r approaches to support vector machine multi-classification","volume":"177","author":"Lingras","year":"2007","journal-title":"Inform. Sci."},{"key":"ref_30","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_31","doi-asserted-by":"crossref","first-page":"968","DOI":"10.1007\/s00170-006-0780-3","article-title":"Fault diagnosis of rotating machinery based on a new hybrid clustering algorithm","volume":"35","author":"Lei","year":"2008","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"625","DOI":"10.1016\/S0888-3270(03)00020-7","article-title":"Gear fault detection using artificial neural networks and support vector machines with genetic algorithms","volume":"18","author":"Samanta","year":"2004","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"12131","DOI":"10.1016\/j.eswa.2009.03.063","article-title":"An intelligent fault diagnosis method based on wavelet packer analysis and hybrid support vector machines","volume":"36","author":"Xian","year":"2009","journal-title":"Expert Syst. Appl."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/13\/7\/8679\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T21:47:45Z","timestamp":1760219265000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/13\/7\/8679"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2013,7,5]]},"references-count":33,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2013,7]]}},"alternative-id":["s130708679"],"URL":"https:\/\/doi.org\/10.3390\/s130708679","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2013,7,5]]}}}