Fusion information entropy method of rolling bearing fault diagnosis based on n-dimensional characteristic parameter distance

S.J. Sheldon, E. Carney, H. Lee et al., Detection of incipient bearing faults in a gas turbine engine using integrated signal processing techniques, in: Proceedings of the American Helicopter Society 63th Annual Forum, Virginia Beach, VA, May 1–3, 2007, pp. 1–9 J. Benali, M. Sayadi, F. Fnaiech, B. Morello, N Zerhouni, Importance of the fourth and fifth intrinsic mode functions for bearing fault diagnosis, in: Proceedings of the IEEE 14th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering, Sousse, Tunisia, December, 2013, pp. 259–264 Tandon, 1999, A review of vibration and acoustic measurement methods for the detection of defects in rolling element bearings, Tribol. Int., 32, 469, 10.1016/S0301-679X(99)00077-8 Jantunen, 2006, How to diagnose the wear of rolling element bearings based on indirect condition monitoring methods, Int. J. COMADEM, 9, 24 Rai, 2016, A review on signal processing techniques utilized in the fault diagnosis of rolling element bearing, Tribol. Int., 96, 289, 10.1016/j.triboint.2015.12.037 Martin, 1995, Application of statistical moments to bearing failure detection, Appl. Acoust., 44, 67, 10.1016/0003-682X(94)P4420-B Heng, 1998, Statistical analysis of sound and vibration signals for monitoring rolling element bearing condition, Appl. Acoust., 53, 211, 10.1016/S0003-682X(97)00018-2 Mechefske, 1992, Fault detection and diagnosis in low speed rolling element bearings part I: the use of parametric spectra, Mech. Syst. Signal Process., 6, 297, 10.1016/0888-3270(92)90032-E Mechefske, 1992, Fault detection and diagnosis in low speed rolling element bearings part II: the use of nearest neighbor classification, Mech. Syst. Signal Process., 6, 309, 10.1016/0888-3270(92)90033-F Logan, 1996, Using correlation dimension for vibration fault diagnosis of rolling element bearing-I. Basic concept, Mech. Syst. Signal Process., 10, 241, 10.1006/mssp.1996.0018 Logan, 1996, Using the correlation dimension for vibration fault diagnosis of rolling element bearings—II. Selection of experimental parameters, Mech. Syst. Signal Process., 10, 251, 10.1006/mssp.1996.0019 Mori, 1996, Prediction of spalling on a ball bearing by applying the discrete wavelet transform to vibration signals, Wear, 195, 162, 10.1016/0043-1648(95)06817-1 Yan, 1992, Application of the impulse index in rolling-element bearing fault diagnosis, Mech. Syst. Signal Process., 6, 167, 10.1016/0888-3270(92)90063-O Brie, 2000, Modelling of the spalled rolling element bearing vibration signal: an over view and some new results, Mech. Syst. Signal Process., 14, 353, 10.1006/mssp.1999.1237 Pineyro, 2000, Effectiveness of new spectral tools in the anomaly detection of rolling element bearings, J. Alloy. Compd., 310, 276, 10.1016/S0925-8388(00)00964-6 McFadden, 1984, Vibration monitoring of rolling element bearings by the high-frequency resonance technique—a review, Tribol. Int., 17, 3, 10.1016/0301-679X(84)90076-8 Altmann, 2001, Multiple band-pass autoregressive demodulation for rolling-element bearing fault diagnosis, Mech. Syst. Signal Process., 15, 963, 10.1006/mssp.2001.1410 Prabhakar, 2002, Application of discrete wavelet transform for detection of ball bearing race faults, Tribol. Int., 35, 793, 10.1016/S0301-679X(02)00063-4 Lou, 2004, Bearing fault diagnosis based on wavelet transform and fuzzy inference, Mech. Syst. Signal Process., 18, 1077, 10.1016/S0888-3270(03)00077-3 Bozchalooi, 2008, A joint resonance frequency estimation and in-band noise reduction method for enhancing the detect ability of bearing fault signals, Mech. Syst. Signal Process., 22, 915, 10.1016/j.ymssp.2007.10.006 Tao, 2007, An alternative time-domain index for condition monitoring of rolling element bearings—a comparison study, Reliab. Eng. Syst. Saf., 92, 660, 10.1016/j.ress.2006.03.005 Wang, 2008, Novel approach based on chaotic oscillator for machinery fault diagnosis, Measurement, 41, 904, 10.1016/j.measurement.2008.01.001 Hao, 2009, Morphological undecimated wavelet decomposition for fault diagnostics of rolling element bearings, J. Sound Vib., 320, 1164, 10.1016/j.jsv.2008.09.014 Cheng, 2006, A fault diagnosis approach for roller bearings based on EMD method and AR model, Mech. Syst. Signal Process., 20, 350, 10.1016/j.ymssp.2004.11.002 Wang, 2011, An adaptive SK technique and its application for fault detection of rolling element bearings, Mech. Syst. Signal Process., 25, 1750, 10.1016/j.ymssp.2010.12.008 Wang, 2011, Multiwavelet denoising with improved neighboring coefficients for application on rolling bearing fault diagnosis, Mech. Syst. Signal Process., 25, 285, 10.1016/j.ymssp.2010.03.010 Wang, 2014, Feature extraction of rolling bearing's early weak fault based on EEMD and tunable Q-factor wavelet transform, Mech. Syst. Signal Process., 48, 103, 10.1016/j.ymssp.2014.04.006 Guo, 2012, Faulty bearing signal recovery from large noise using a hybrid method based on spectral kurtosis and ensemble empirical mode decomposition, Measurement, 45, 1308, 10.1016/j.measurement.2012.01.001 Zhao, 2013, Quantitative diagnosis of a spall-like fault of a rolling element bearing by empirical mode decomposition and the approximate entropy method, Mech. Syst. Signal Process., 40, 154, 10.1016/j.ymssp.2013.04.006 Xing, 2001, Physical entropy, information entropy and their evolution equations, Sci. China A: Math., 44, 1331, 10.1007/BF02877022 Cui, 2009, Research on fault diagnosis for reciprocating compressor valve using information entropy and SVM method, J. Loss Prev. Process Ind., 22, 864, 10.1016/j.jlp.2009.08.012 Fei, 2014, Quantitative diagnosis of rotor vibration fault using process power spectrum entropy and support vector machine method, Shock Vib., 2014, 957531 Fei, 2013, Wavelet correlation feature scale entropy and fuzzy support vector machine approach for aeroengine whole-body vibration fault diagnosis, Shock Vib., 20, 341, 10.1155/2013/403920 Hua, 2008, Power quality disturbance identification using wavelet packet energy entropy and weighted support vector machines, Expert Syst. Appl., 35, 143, 10.1016/j.eswa.2007.06.005 Dube, 2013, Vibration based condition assessment of rolling element bearings with localized defects, Int. J. Sci. Technol. Res., 2, 149 Cong, 2013, Vibration model of rolling element bearings in a rotor-bearing system for fault diagnosis, J. Sound Vib., 332, 2081, 10.1016/j.jsv.2012.11.029 Mathew, 1984, The condition monitoring of rolling element bearings using vibration analysis, J. Vib. Acoust., 106, 447, 10.1115/1.3269216 B.S. Kim, D.S. Gu, J.G. Kim, Y.C. Kim, B.K. Choi, Rolling element bearing fault detection using acoustic emission signal analyzed by envelope analysis with discrete wavelet transform, in: Engineering Asset Life Cycle Management, Springer, London, 2010, pp. 596–602 Pandya, 2013, Fault diagnosis of rolling element bearing with intrinsic mode function of acoustic emission data using APF-KNN, Expert Syst. Appl., 40, 4137, 10.1016/j.eswa.2013.01.033 Chacon, 2015, A novel approach for incipient defect detection in rolling bearings using acoustic emission technique, Appl. Acoust., 89, 88, 10.1016/j.apacoust.2014.09.002 Qu, 2003, Enhanced diagnostic certainty using information entropy theory, Adv. Eng. Inform., 17, 141, 10.1016/j.aei.2004.08.002 Weis, 2012, Entropy distance: new quantum phenomena, J. Math. Phys., 53, 102206, 10.1063/1.4757652