[1]荆双喜, 杨鑫, 冷军发,等. 基于改进EMD与谱峭度的滚动轴承故障特征提取[J]. 机械传动, 2016, 40(4):125-128.
JING Shuangxi, YANG Xin, LENG Junfa, et al. Fault Feature Extraction of Rolling Bearing Based on Improved EMD and Spectrum Kurtosis[J]. Journal of Mechanical Transmission,2016,40(4):125-128.
[2]RANDALL R B, JRME A. Rolling Element Bearing Diagnostics:a Tutorial[J]. Mechanical Systems and Signal Processing, 2011, 25(2):485-520.
[3]DWYER R. Use of the Kurtosis Statistic in the Frequency Domain as an Aid in Detecting Random Signals[J]. IEEE Journal of Oceanic Engineering, 1984, 9(2):85-92.
[4]DWYER R. Detection of Non-Gaussian Signals by Frequency Domain Kurtosis Estimation[C]∥IEEE International Conference on Acoustics, Speech, and Signal Processing. Boston, 1983:607-610.
[5]LIU H, HUANG W, WANG S, et al. Adaptive Spectral Kurtosis Filtering Based on Morlet Wavelet and Its Application for Signal Transients Detection[J]. Signal Processing, 2014, 96(5):118-124.
[6]ANTONI J. Fast Computation of the Kurtogram for the Detection of Transient Faults[J]. Mechanical Systems and Signal Processing, 2007, 21(1):108-124.
[7]ANTONI J. The Sprctral Kurtosis:a Useful Tool for Characterizing Non-stationary Signals[J]. Mechanical Systems and Signal Processing, 2006,20(2):282-307.
[8]LEI Y, LIN J, HE Z, et al. Application of an Improved Kurtogram Method for Fault Diagnosis of Rolling Element Bearings[J]. Mechanical Systems and Signal Processing, 2011, 25(5):1738-1749.
[9]BARSZCZ T, JABLONSKI A. A Novel Method for the Optimal Band Selection for Vibration Signal Demodulation and Comparison with the Kurtogram[J]. Mechanical Systems and Signal Processing, 2011, 25(1):431-451.
[10]WANG D, TSE P W, TSUI K L. An Enhanced Kurtogram Method for Fault Diagnosis of Rolling Element Bearings[J]. Mechanical Systems and Signal Processing, 2013, 35(1/2):176-199.
[11]MOSHREFZADEH A, FASANA A. The Autogram:an Effective Approach for Selecting the Optimal Demodulation Band in Rolling Element Bearings Diagnosis[J]. Mechanical Systems and Signal Processing, 2018, 105:294-318.
[12]王兴龙,郑近德,潘海洋, 等. 基于MED与自相关谱峭度图的滚动轴承故障诊断方法[J].振动与冲击,2020,39(18):118-124.
WANG Xinglong, ZHENG Jinde, PAN Haiyang, et al. Fault Diagnosis Method for Rolling Bearings Based on Minimum Entropy Deconvolution and Autogram[J]. Journal of Vibration and Shock, 2020,39(18):118-124.
[13]HU Y, LI F, LI H, et al. An Enhanced Empirical Wavelet Transform for Noisy and Non-stationary Signal Processing[J]. Digital Signal Processing, 2016, 60:220-229.
[14]XU Y, ZHANG K, MA C, et al. Adaptive Kurtogram and Its Applications in Rolling Bearing Fault Diagnosis[J]. Mechanical Systems and Signal Processing, 2019, 130:87-107.
[15]郑近德, 潘海洋, 戚晓利, 等. 基于改进经验小波变换的时频分析方法及其在滚动轴承故障诊断中的应用[J]. 电子学报, 2018,46(2):358-364.
ZHENG Jinde, PAN Haiyang, QI Xiaoli, et al. Enhanced Empirical Wavelet Transform Based Time-frequency Analysis and Its Application to Rolling Bearing Fault Diagnosis[J]. Acta Electronica Sinica, 2018,46(2):358-364.
[16]GILLES J. Empirical Wavelet Transform[J]. IEEE Transactions on Signal Processing, 2013, 61(16):3999-4010.
[17]ZHENG J, PAN H, YANG S, et al. Adaptive Parameterless Empirical Wavelet Transform Based Time-frequency Analysis Method and Its Application to Rotor Rubbing Fault Diagnosis[J]. Signal Processing, 2017, 130:305-314.
[18]王昌明, 张征, 李峰, 等. 改进经验小波变换的齿轮箱故障诊断新方法与应用[J]. 噪声与振动控制, 2018, 38(5):173-178.WANG Changming, ZHANG Zheng, LI Feng, et al. A New Method and Its Application for Fault Diagnosis of Gearboxes Based on Improved Empirical Wavelet Transform[J]. Noise and Vibration Control, 2018, 38(5):173-178.
[19]GILLES J, HEALK. A Parameterless Scale-space Approach to Find Meaningful Modes in Histograms:Application to Image and Spectrum Segmentation[J]. International Journal of Wavelets, Multiresolution and Information Processing, 2014, 12(6):1-17.
[20]周航. 基于改进最大重叠离散小波包变换的线性系统参数识别[D].南京:南京航空航天大学,2018.
ZHOU Hang. Parameter Identification for Linear Systems Based on Improved Maximal Overlap Discrete Wavelet Packet Transform[D]. Nanjing:Nanjing University of Aeronautics and Astronautics,2018.
[21]RANDALL R B, ANTONI J, CHOBSAARD S. The Relationship between Spectral Correlation and Envelope Analysis in the Diagnostics of Bearing Faults and Other Cyclostationary Machine Signals[J]. Mechanical Systems and Signal Processing,2001,15(5):945-962.
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