Application of an improved maximum correlated kurtosis deconvolution method for fault diagnosis of rolling element bearings
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Yaguo Lei | Yonghao Miao | Ming Zhao | Jing Lin | Y. Lei | Jing Lin | Ming Zhao | Yonghao Miao
[1] Paolo Pennacchi,et al. The combination of empirical mode decomposition and minimum entropy deconvolution for roller bearing diagnostics in non-stationary operation , 2012 .
[2] Minqiang Xu,et al. A fault diagnosis scheme for rolling bearing based on local mean decomposition and improved multiscale fuzzy entropy , 2016 .
[3] Yaguo Lei,et al. Envelope harmonic-to-noise ratio for periodic impulses detection and its application to bearing diagnosis , 2016 .
[4] R. Wiggins. Minimum entropy deconvolution , 1978 .
[5] Yaguo Lei,et al. Periodicity-based kurtogram for random impulse resistance , 2015 .
[6] Robert B. Randall,et al. Vibration-based Condition Monitoring: Industrial, Aerospace and Automotive Applications , 2011 .
[7] Feng Jia,et al. Early Fault Diagnosis of Bearings Using an Improved Spectral Kurtosis by Maximum Correlated Kurtosis Deconvolution , 2015, Sensors.
[8] Robert B. Randall,et al. Application of spectral kurtosis for detection of a tooth crack in the planetary gear of a wind turbine , 2009 .
[9] Robert B. Randall,et al. Rolling element bearing diagnostics—A tutorial , 2011 .
[10] Yanyang Zi,et al. Enhancement of signal denoising and multiple fault signatures detecting in rotating machinery using dual-tree complex wavelet transform , 2010 .
[11] Robert B. Randall,et al. Spectral kurtosis optimization for rolling element bearings , 2005, Proceedings of the Eighth International Symposium on Signal Processing and Its Applications, 2005..
[12] Tomasz Barszcz,et al. Fault Detection Enhancement in Rolling Element Bearings Using the Minimum Entropy Deconvolution , 2012 .
[13] Qing Zhao,et al. Maximum correlated Kurtosis deconvolution and application on gear tooth chip fault detection , 2012 .
[14] Ruqiang Yan,et al. Kurtosis based weighted sparse model with convex optimization technique for bearing fault diagnosis , 2016 .
[15] Jin Chen,et al. Application of Maximum Correlated Kurtosis Deconvolution on Rolling Element Bearing Fault Diagnosis , 2015 .
[16] Xiaolong Wang,et al. Diagnosis of compound faults of rolling bearings through adaptive maximum correlated kurtosis deconvolution , 2016 .
[17] Tomasz Barszcz,et al. A novel method for the optimal band selection for vibration signal demodulation and comparison with the Kurtogram , 2011 .
[18] Robert B. Randall,et al. Rolling element bearing fault diagnosis based on the combination of genetic algorithms and fast kurtogram , 2009 .
[19] Yonghao Miao,et al. Detection and recovery of fault impulses via improved harmonic product spectrum and its application in defect size estimation of train bearings , 2016 .
[20] Yanyang Zi,et al. Compound faults detection of rotating machinery using improved adaptive redundant lifting multiwavelet , 2013 .
[21] H. Saunders,et al. Mechanical Signature Analysis—Theory and Applications , 1988 .
[22] Robert B. Randall,et al. Enhancement of autoregressive model based gear tooth fault detection technique by the use of minimum entropy deconvolution filter , 2007 .
[23] Feng Wu,et al. Automatic fault feature extraction of mechanical anomaly on induction motor bearing using ensemble super-wavelet transform , 2015 .
[24] Liu Hong,et al. A time domain approach to diagnose gearbox fault based on measured vibration signals , 2014 .
[25] Qing Zhao,et al. Multipoint Optimal Minimum Entropy Deconvolution and Convolution Fix: Application to vibration fault detection , 2017 .
[26] Hongchao Wang,et al. Fault Diagnosis Method for Rolling Bearing’s Weak Fault Based on Minimum Entropy Deconvolution and Sparse Decomposition , 2013 .
[27] Peter W. Tse,et al. An enhanced Kurtogram method for fault diagnosis of rolling element bearings , 2013 .
[28] R. M. Stewart,et al. Detection of Rolling Element Bearing Damage by Statistical Vibration Analysis , 1978 .
[29] Robert B. Randall,et al. The enhancement of fault detection and diagnosis in rolling element bearings using minimum entropy deconvolution combined with spectral kurtosis , 2007 .
[30] Yaguo Lei,et al. Application of an improved kurtogram method for fault diagnosis of rolling element bearings , 2011 .
[31] J. Antoni. Fast computation of the kurtogram for the detection of transient faults , 2007 .
[32] Robert B. Randall,et al. The spectral kurtosis: application to the vibratory surveillance and diagnostics of rotating machines , 2006 .
[33] Kenneth A. Loparo,et al. Bearing condition diagnosis and prognosis using applied nonlinear dynamical analysis of machine vibration signal , 2008 .
[34] Ming Liang,et al. Identification of multiple transient faults based on the adaptive spectral kurtosis method , 2012 .
[35] Tao Liu,et al. The weak fault diagnosis and condition monitoring of rolling element bearing using minimum entropy deconvolution and envelop spectrum , 2013 .
[36] Ming Zhao,et al. Identification of multiple faults in rotating machinery based on minimum entropy deconvolution combined with spectral kurtosis , 2016 .
[37] F. Combet,et al. Optimal filtering of gear signals for early damage detection based on the spectral kurtosis , 2009 .
[38] Ming Liang,et al. An adaptive SK technique and its application for fault detection of rolling element bearings , 2011 .
[39] Shuilong He,et al. Multifractal entropy based adaptive multiwavelet construction and its application for mechanical compound-fault diagnosis , 2016 .
[40] Yonghao Miao,et al. Sparse maximum harmonics-to-noise-ratio deconvolution for weak fault signature detection in bearings , 2016 .
[41] J. Antoni. The spectral kurtosis: a useful tool for characterising non-stationary signals , 2006 .
[42] Dejie Yu,et al. A new rolling bearing fault diagnosis method based on GFT impulse component extraction , 2016 .
[43] Jianshe Kang,et al. A New Improved Kurtogram and Its Application to Bearing Fault Diagnosis , 2015 .
[44] Robert B. Randall,et al. Differential diagnosis of spall vs. cracks in the gear tooth fillet region: Experimental validation , 2009 .