Fault diagnosis of rolling bearing based on ITD fuzzy entropy and GG clustering

Yi Qi Yi Biao Xue Bao/Chinese Journal of Scientific Instrument

Volumn 35, Issue 11, 2014, Pages 2624-2632

  Zhang, Liguo ^a   Li, Pan ^a   Li, Meimei ^b   Zhang, Shuqing ^a   Zhang, Zhifu ^a  

^a YANSHAN UNIVERSITY   (China)

^b NORTHEASTERN UNIVERSITY AT QINHUANGDAO   (China)

Author keywords

Fault diagnosis; Fuzzy entropy; Gath Geva fuzzy clustering; ITD

Indexed keywords

EIGENVALUES AND EIGENFUNCTIONS; ENTROPY; FAILURE ANALYSIS; FAULT DETECTION; FUZZY CLUSTERING; ROLLER BEARINGS; SIGNAL PROCESSING;

APPROXIMATE ENTROPY; FEATURE INFORMATION; FUZZY ENTROPY; INTRINSIC TIME-SCALE DECOMPOSITIONS; ORIGINAL SIGNAL; ROLLING BEARING VIBRATION; ROLLING BEARINGS; SAMPLE ENTROPY;

CLUSTERING ALGORITHMS;

EID: 84919497011     PISSN: 02543087     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (15)

1. ZHANG X, WANG H L. Evolutionary wavelet denoising and its application to ball bearing fault diagnosis [J]. Journal of Mechanical Engineering, 2010, 46(15): 76-81.
2. CAI J H, GONG Y R, WANG X CH. Crack fault diagnosis of gear based on EMD-Wigner higher order moment spectrum [J]. Journal of Vibration, Measurement& Diagnosis, 2010, 46(15): 76-81.
3. WANG Y J, JANG Y CH, KANG SH Q, et al. Diagnosis method of fault location and performance degradation degree of rolling bearing based on optimal ensemble EMD [J]. Chinese Journal of Scientific Instrument, 2013, 34(8): 1834-1840.
4. WANG J G, WU L F, QIN X H. Rolling bearing vibration signal fault feature extraction based on autocorrelation analysis and LMD [J]. Chinese Journal of Mechanical Engineering, 2014, 25(2): 186-191.
5. YAO Y F, ZHANG X. Fault diagnosis approach for roller bearing based on EMD momentary energy entropy and SVM [J]. Journal of Electronic Measurement and Instrument, 2013, 27(10): 957-962.
6. WRN Y, XIAO M Q, WANG Y, et al. Aero-engine fault diagnosis based on information entropy attribute reduction [J]. Chinese Journal of Scientific Instrument, 2012, 33(8): 1773-1778.
7. YUAN Q, ZHOU W D, LI SH F, et al. Approach of EEG detection based on ELM and approximate entropy [J]. Chinese Journal of Scientific Instrument, 2012, 33(3): 514-519.
8. ZHANG W B, GUO D W, PU Y S, et al. Harmonic window decomposition sample entropy and grey relation degree in rotor fault recognition [J]. Proceedings of the CSEE, 2013, 33(21): 132-137.
9. ZAHNG Y, TANG B P, DENG L. Mechanical fault diagnosis based on non-negative matrix factorization with spectral clustering initialization enhancer [J]. Chinese Journal of Scientific Instrument, 2013, 34(12): 2806-2811.
10. LIU H T, NI ZH W. Clustering method of time series based on EMD and K-means algorithm [J]. PR & AI, 2009, 22(5): 803-808.
11. ZARINBAL M, FAZEL ZARANDI M H, TURKSEN I B. Relative entropy fuzzy c-means clustering [J]. Information Sciences, 2014, 260: 74-97.
12. LISA S, EMMANUEL R, NOUREDDINE Z. Evidential evolving Gustafson-Kessel algorithm for online data streams partitioning using belief function theory [J]. International Journal of Approximate Reasoning, 2012, 53(5): 747-768.
13. XU Y G, XIE ZH C, CUI L L, et al. Study on feature extraction method of gear magnetic memory signal based on ITD [J]. Chinese Journal of Scientific Instrument, 2013, 34(3): 672-676.
14. DENG AI D, ZHAO L, BAO Y Q. Recognition of rub-impact acoustic emission signal based on fuzzy entropy [J]. Journal of Mechanical Engineering, 2010, 46(3): 71-75.
15. WANG SH T, ZHANG J M, LI Y Y, et al. Rotating machinery fault diagnosis based on mathematical morphology and fuzzy clustering [J]. Chinese Journal of Scientific Instrument, 2012, 33(5): 1056-1061.