Identification of mechanical compound-fault based on the improved parameter-adaptive variational mode decomposition.
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Jing Lin | Ming Zhao | Yonghao Miao | Ming Zhao | Jing Lin | Yonghao Miao
[1] Minping Jia,et al. Compound fault diagnosis of rotating machinery based on OVMD and a 1.5-dimension envelope spectrum , 2016 .
[2] Yaguo Lei,et al. A review on empirical mode decomposition in fault diagnosis of rotating machinery , 2013 .
[3] 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 .
[4] Ming Zhang,et al. Research on variational mode decomposition in rolling bearings fault diagnosis of the multistage centrifugal pump , 2017 .
[5] J. Antoni. Fast computation of the kurtogram for the detection of transient faults , 2007 .
[6] Ming Li,et al. Variational mode decomposition denoising combined the detrended fluctuation analysis , 2016, Signal Process..
[7] Han Zhang,et al. Convolutional Sparse Learning for Blind Deconvolution and Application on Impulsive Feature Detection , 2018, IEEE Transactions on Instrumentation and Measurement.
[8] Farhat Fnaiech,et al. Bi-spectrum based-EMD applied to the non-stationary vibration signals for bearing faults diagnosis , 2014, 2014 6th International Conference of Soft Computing and Pattern Recognition (SoCPaR).
[9] Fulei Chu,et al. Compound faults detection in gearbox via meshing resonance and spectral kurtosis methods , 2017 .
[10] R. M. Stewart,et al. Detection of Rolling Element Bearing Damage by Statistical Vibration Analysis , 1978 .
[11] Ming Zhao. Finite Element Analysis on Temperature Field and Weld Profile of Carbon Dioxide Gas Shielded Arc Welding , 2013 .
[12] Anoushiravan Farshidianfar,et al. Rolling element bearings multi-fault classification based on the wavelet denoising and support vector machine , 2007 .
[13] Yaguo Lei,et al. Application of an improved maximum correlated kurtosis deconvolution method for fault diagnosis of rolling element bearings , 2017 .
[14] Yaguo Lei,et al. A tacho-less order tracking technique for large speed variations , 2013 .
[15] Viliam Makis,et al. Fault severity recognition of aviation piston pump based on feature extraction of EEMD paving and optimized support vector regression model , 2017 .
[16] 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 .
[17] Zhixiong Li,et al. A new compound faults detection method for rolling bearings based on empirical wavelet transform and chaotic oscillator , 2016 .
[18] Ming Zhao,et al. Identification of multiple faults in rotating machinery based on minimum entropy deconvolution combined with spectral kurtosis , 2016 .
[19] Shuilong He,et al. Bearing fault diagnosis based on variational mode decomposition and total variation denoising , 2016 .
[20] Nan Pan,et al. Mechanical compound faults extraction based on improved frequency domain blind deconvolution algorithm , 2017, Mechanical Systems and Signal Processing.
[21] Yonghao Miao,et al. Improvement of kurtosis-guided-grams via Gini index for bearing fault feature identification , 2017 .
[22] Hongkai Jiang,et al. An improved EEMD with multiwavelet packet for rotating machinery multi-fault diagnosis , 2013 .
[23] Fengshou Gu,et al. A novel procedure for diagnosing multiple faults in rotating machinery. , 2015, ISA transactions.
[24] Shuilong He,et al. Multifractal entropy based adaptive multiwavelet construction and its application for mechanical compound-fault diagnosis , 2016 .
[25] Robert B. Randall,et al. Rolling element bearing diagnostics—A tutorial , 2011 .
[26] Yongbo Li,et al. Application of Bandwidth EMD and Adaptive Multiscale Morphology Analysis for Incipient Fault Diagnosis of Rolling Bearings , 2017, IEEE Transactions on Industrial Electronics.
[27] Yaguo Lei,et al. Tacholess Envelope Order Analysis and Its Application to Fault Detection of Rolling Element Bearings with Varying Speeds , 2013, Sensors.
[28] Fulei Chu,et al. Envelope calculation of the multi-component signal and its application to the deterministic component cancellation in bearing fault diagnosis , 2015 .
[29] Yanyang Zi,et al. Enhancement of signal denoising and multiple fault signatures detecting in rotating machinery using dual-tree complex wavelet transform , 2010 .
[30] Yu Jiang,et al. Multi-dimensional variational mode decomposition for bearing-crack detection in wind turbines with large driving-speed variations , 2018 .
[31] N. Huang,et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis , 1998, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.
[32] Ming Liang,et al. Identification of multiple transient faults based on the adaptive spectral kurtosis method , 2012 .
[33] Zhang Wei. Compound Fault Features Separation of Rolling Element Bearing Based on the Wavelet Decomposition and Spectrum Auto-correlation , 2013 .
[34] Yanyang Zi,et al. Independence-oriented VMD to identify fault feature for wheel set bearing fault diagnosis of high speed locomotive , 2017 .
[35] Tomasz Barszcz,et al. A novel method for the optimal band selection for vibration signal demodulation and comparison with the Kurtogram , 2011 .
[36] Yaguo Lei,et al. Application of a Novel Hybrid Intelligent Method to Compound Fault Diagnosis of Locomotive Roller Bearings , 2008 .
[37] Jay Lee,et al. Investigation on the kurtosis filter and the derivation of convolutional sparse filter for impulsive signature enhancement , 2017 .
[38] Robert B. Randall,et al. The spectral kurtosis: application to the vibratory surveillance and diagnostics of rotating machines , 2006 .
[39] Gang Tang,et al. A Compound Fault Diagnosis for Rolling Bearings Method Based on Blind Source Separation and Ensemble Empirical Mode Decomposition , 2014, PloS one.
[40] Peter W. Tse,et al. An enhanced Kurtogram method for fault diagnosis of rolling element bearings , 2013 .
[41] Qing Zhao,et al. Maximum correlated Kurtosis deconvolution and application on gear tooth chip fault detection , 2012 .
[42] Qiang Miao,et al. A parameter-adaptive VMD method based on grasshopper optimization algorithm to analyze vibration signals from rotating machinery , 2018, Mechanical Systems and Signal Processing.
[43] Dominique Zosso,et al. Variational Mode Decomposition , 2014, IEEE Transactions on Signal Processing.
[44] Yonghao Miao,et al. Sparse maximum harmonics-to-noise-ratio deconvolution for weak fault signature detection in bearings , 2016 .
[45] Xiaoyuan Zhang,et al. Multi-fault diagnosis for rolling element bearings based on ensemble empirical mode decomposition and optimized support vector machines , 2013 .
[46] Peter W. Tse,et al. The design of a new sparsogram for fast bearing fault diagnosis: Part 1 of the two related manuscripts that have a joint title as “Two automatic vibration-based fault diagnostic methods using the novel sparsity measurement – Parts 1 and 2” , 2013 .
[47] Robert B. Randall,et al. Enhancement of autoregressive model based gear tooth fault detection technique by the use of minimum entropy deconvolution filter , 2007 .
[48] Andrew Lewis,et al. Grasshopper Optimisation Algorithm: Theory and application , 2017, Adv. Eng. Softw..
[49] Yu Jiang,et al. Recent progress on decoupling diagnosis of hybrid failures in gear transmission systems using vibration sensor signal: A review , 2016 .
[50] Yanxue Wang,et al. Research on variational mode decomposition and its application in detecting rub-impact fault of the rotor system , 2015 .