Performance degradation assessment is crucial to realize equipment’s near-zero downtime and maximum productivity. In this paper, a new method for performance degradation assessment of rolling element bearings is proposed based on hierarchical entropy (HE) and general distance. First, considering the nonlinear dynamic characteristics of bearing vibration signals, the HE method is utilized to extract feature vectors, which can obtain more bearing state information hidden in the vibration signals than sample entropy (SampEn) and multi-scale entropy (MSE). Then, the general distance between the feature vectors of the normal data and those of the tested data is designed as a degradation indicator by combining Euclidean distance and cosine angle distance. The experimental results indicate that this indicator can detect the incipient defects well and can effectively reflect the whole degradation process of rolling element bearings. Moreover, the designed indicator has some advantages over kurtosis and root mean square (RMS) values.
AbbasionSRafsanjaniAFarshidianfarAet al. (2007) Rolling element bearings multi-fault classification based on the wavelet denoising and support vector machine. Mechanical Systems and Signal Processing21(7): 2933–2945.
2.
AntoniJ (2007) Cyclic spectral analysis of rolling-element bearing signals: Facts and fictions. Journal of Sound and Vibration304(3–5): 497–529.
3.
CostaMGoldbergerALPengCK (2002) Multiscale entropy analysis of physiologic time series. Physical Review Letters89(6): 068102.
4.
CrupiVGuglielminoEMilazzoG (2004) Neural-network-based system for novel fault detection in rotating machinery. Journal of Vibration and Control10(8): 1137–1150.
5.
HuangRQXiLFLiXLet al. (2007) Residual life predictions for ball bearings based on self-organizing map and back propagation neural network methods. Mechanical Systems and Signal Processing21(1): 193–207.
6.
JardineAKSLinDMBanjevicD (2006) A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mechanical Systems and Signal Processing20(7): 1483–1510.
7.
JiangHMChenJDongGMet al. (2016) An intelligent performance degradation assessment method for bearings. Journal of Vibration and Control. Epub ahead of print 13 January 2016. DOI: 10.1177/ 1077546315624996.
8.
JiangYPengCKXuYS (2011) Hierarchical entropy analysis for biological signals. Journal of Computational and Applied Mathematics236: 728–742.
9.
KanMSTanACCMathewJ (2015) A review on prognostic techniques for non-stationary and non-linear rotating systems. Mechanical Systems and Signal Processing62–63: 1–20.
10.
LakeDERichmanJSGriffinMPet al. (2002) Sample entropy analysis of neonatal heart rate variability. American Journal of Physiology: Regulatory, Integrative and Comparative Physiology283(3): R787–R797.
11.
Lee J, Qiu H, Yu G, et al. (2007) Bearing data set. IMS, University of Cincinnati, NASA Ames Prognostics Data Repository, Rexnord Technical Services.
12.
LiuTChenJDongGM (2015) Singular spectrum analysis and continuous hidden Markov model for rolling element bearing fault diagnosis. Journal of Vibration and Control21: 1506–1521.
13.
LiuZWCaoHRChenXFet al. (2013) Multi-fault classification based on wavelet SVM with PSO algorithm to analyze vibration signals from rolling element bearings. Neurocomputing99: 399–410.
14.
NelwamondoFVMarwalaTMaholaU (2006) Early classifications of bearing faults using hidden Markov models, Gaussian mixture models, Mel-frequency cepstral coefficients and fractals. International Journal of Innovative Computing Information and Control2(6): 1281–1299.
15.
NikolaouNGAntoniadisIA (2002) Rolling element bearing fault diagnosis using wavelet packets. NDT&E International35(3): 197–205.
16.
OcakHLoparoKA (2005) HMM-based fault detection and diagnosis scheme for rolling element bearings. Journal of Vibration and Acoustics127(4): 299–306.
17.
OcakHLoparoKADiscenzoFM (2007) Online tracking of bearing wear using wavelet packet decomposition and probabilistic modeling: a method for bearing prognostics. Journal of Sound and Vibration302(4–5): 951–961.
18.
PanYNChenJGuoL (2009) Robust bearing performance degradation assessment method based on improved wavelet packet–support vector data description. Mechanical Systems and Signal Processing23(3): 669–681.
19.
PanYNChenJLinLX (2010) Bearing performance degradation assessment based on lifting wavelet packet decomposition and fuzzy c-means. Mechanical Systems and Signal Processing24: 559–566.
20.
PengYDongMZuoMJ (2010) Current status of machine prognostics in condition-based maintenance: a review. International Journal of Manufacturing Technology50: 297–313.
21.
PincusSM (1991) Approximate entropy as a measure of system complexity. Proceedings of the National Academy of Sciences of the United States of America88: 2297–2301.
22.
PincusSM (2002) Assessing serial irregularity and its implications for health. Annals of the New York Academy of Sciences954: 245–267.
23.
QianYNYanRQ (2014) Bearing degradation evaluation using recurrence quantification analysis and Kalman filter. IEEE Transactions on Instrumentation and Measurement63: 2599–2610.
24.
QiuHLeeJLinJet al. (2003) Robust performance degradation assessment methods for enhanced rolling element bearing prognostics. Advanced Engineering Informatics17(3–4): 127–140.
25.
QiuHLeeJLinJet al. (2006) Wavelet filter-based weak signature detection method and its application on rolling element bearing prognostics. Journal of Sound and Vibration289: 1066–1090.
26.
SamantaBAl-BalushiKRAl-AraimiSA (2006) Artificial neural networks and genetic algorithm for bearing fault detection. Soft Computing10(3): 264–271.
27.
SharmaAAmarnathMKankarPK (2016) Feature extraction and fault severity classification in ball bearings. Journal of Vibration and Control22(1): 176–192.
28.
ShenZJHeZJChenXFet al. (2012) A monotonic degradation assessment index of rolling bearings using fuzzy support vector data description and running time. Sensors12: 10109–10135.
29.
TiwariRGuptaVKKankarPK (2015) Bearing fault diagnosis based on multi-scale permutation entropy and adaptive neuro fuzzy classifier. Journal of Vibration and Control21: 461–467.
30.
WangHCChenJ (2014) Performance degradation assessment of rolling bearing based on bispectrum and support vector data description. Journal of Vibration and Control20: 2032–2041.
31.
Wang SS (1989) Artificial intelligence and expert systems for diagnostics. In: Proceedings of 1st international conference for machining and diagnostics and exhibition, Las Vegas, NV, 11–14 September 1989. Union College.
32.
YanRGaoRX (2007) Approximate entropy as a diagnostic tool for machine health monitoring. Mechanical Systems and Signal Processing21(2): 824–839.
33.
YangYYuDJChengJS (2006) A roller bearing fault diagnosis method based on EMD energy entropy and ANN. Journal of Sound and Vibration294: 269–277.
34.
YuJB (2011) Bearing performance degradation assessment using locality preserving projections and Gaussian mixture models. Mechanical Systems and Signal Processing25: 2573–2588.
35.
ZhangLXiongGLLiuHSet al. (2010) Bearing fault diagnosis using multi-scale entropy and adaptive neuro-fuzzy inference. Expert Systems with Applications37: 6017–6085.
36.
ZhuKHSongXGXueDX (2014) A roller bearing fault diagnosis method based on hierarchical entropy and support vector machine with particle swarm optimization algorithm. Measurement47: 669–675.
37.
ZhuXRZhangYYZhuYS (2013) Bearing performance degradation assessment based on the rough support vector data description. Mechanical Systems and Signal Processing34: 203–217.
