Rolling Bearing Diagnosing Method Based on Time Domain Analysis and Adaptive Fuzzy -Means Clustering
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Yi Liu | Sheng Fu | Yonggang Xu | Kun Liu | Kun Liu | Yonggang Xu | S. Fu | Yi Liu
[1] 陈鹏,et al. Feature Extraction Method Based on Pseudo-Wigner-Ville Distribution for Rotational Machinery in Variable Operating Conditions , 2011 .
[2] M. Moalem,et al. Intelligent Diagnosis of Broken Bars in Induction Motors Based on New Features in Vibration Spectrum , 2007, 2007 IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives.
[3] Dong Pyo Hong,et al. Infrared Thermographic Diagnosis Mechnism for Fault Detection of Ball Bearing under Dynamic Loading Conditions , 2011 .
[4] Simon Iwnicki,et al. Application of power spectrum, cepstrum, higher order spectrum and neural network analyses for induction motor fault diagnosis , 2013 .
[5] Aaas News,et al. Book Reviews , 1893, Buffalo Medical and Surgical Journal.
[6] Shulin Liu,et al. A novel method of fault diagnosis for rolling element bearings based on the accumulated envelope spectrum of the wavelet packet , 2015 .
[7] Yichuang Sun,et al. A New Neural-Network-Based Fault Diagnosis Approach for Analog Circuits by Using Kurtosis and Entropy as a Preprocessor , 2010, IEEE Transactions on Instrumentation and Measurement.
[8] Sun,et al. Feature Extraction Method Based on Pseudo-Wigner-Ville Distribution for Rotational Machinery in Variable Operating Conditions , 2011 .
[9] Pl . Teatralny. Rolling bearing diagnosing method based on Empirical Mode Decomposition of machine vibration signal , 2014 .
[10] J. Haddadnia,et al. Intelligent Fault Detection of Ball bearing Using FFT , STFT Energy Entropy and RMS , 2012 .
[11] M. S. Safizadeh,et al. Using multi-sensor data fusion for vibration fault diagnosis of rolling element bearings by accelerometer and load cell , 2014, Inf. Fusion.
[12] Vineeta Agarwal,et al. Bearing Fault Detection Using Hilbert and High Frequency Resolution Techniques , 2015 .
[13] Jérôme Antoni,et al. Time-frequency approach to extraction of selected second-order cyclostationary vibration components for varying operational conditions , 2013 .
[14] Sheng Zhang,et al. Fault Diagnosis System for Rotary Machine Based on Fuzzy Neural Networks , 2003 .
[15] Bao. Liu. Machinery fault diagnosis by wavelet analysis. , 2000 .
[16] W. Marsden. I and J , 2012 .
[17] Boycho Marinov,et al. Full Dynamic Reactions in the Basic Shaft Bearings of Big Band Saw Machines , 2013 .
[18] S. Ravikumar,et al. Condition monitoring of Self aligning carrying idler (SAI) in belt-conveyor system using statistical features and decision tree algorithm , 2014 .
[19] J. I. Taylor,et al. Identification of Bearing Defects by Spectral Analysis , 1980 .
[20] Satish C. Sharma,et al. Fault diagnosis of ball bearings using continuous wavelet transform , 2011, Appl. Soft Comput..
[21] Alfred Zmitrowicz,et al. Wear debris: a review of properties and constitutive models , 2005 .
[22] Qinghua He,et al. Application of PCA method and FCM clustering to the fault diagnosis of excavator's hydraulic system , 2007, 2007 IEEE International Conference on Automation and Logistics.
[23] James R. Wilson,et al. N-Skart: A nonsequential skewness- and autoregression-adjusted batch-means procedure for simulation analysis , 2011, Proceedings of the 2009 Winter Simulation Conference (WSC).