Bearing fault diagnosis based on multi-scale permutation entropy and adaptive neuro fuzzy classifier

JVC/Journal of Vibration and Control

Volumn 21, Issue 3, 2015, Pages 461-467

  Tiwari, Rohit ^a   Gupta, Vijay K ^a   Kankar, P K ^a  

^a PDPM INDIAN INSTITUTE OF INFORMATION TECHNOLOGY   (India)

Author keywords

Adaptive neuro fuzzy classifier; fault diagnosis; multi scale permutation entropy

Indexed keywords

ENTROPY; FAILURE ANALYSIS; FUZZY INFERENCE; FUZZY NEURAL NETWORKS; FUZZY SETS; ROLLER BEARINGS;

ADAPTIVE NEURO-FUZZY; BEARING FAULT DIAGNOSIS; DIAGNOSIS PROCEDURE; MACHINERY BREAKDOWN; PERMUTATION ENTROPY; PRODUCTIVITY LOSS; ROLLING ELEMENT BEARING; ROTATING MACHINE;

FAULT DETECTION;

EID: 84920964609     PISSN: 10775463     EISSN: 17412986     Source Type: Journal    
DOI: 10.1177/1077546313490778     Document Type: Article

References (22)

1. Aziz W and Arif M (2005) Multiscale permutation entropy of physiological time series. In Proceedings of 9th IEEE International Multitopic Conference, Karachi, Pakistan, Dec. 24-25.
2. Bandit C, Pompe B. Permutation entropy: A natural complexity measure for time series. Physical Review Letters. 2002 ; 88: 174102-1-174102-4
3. Bruzzo AA, Gesierich B, Santi M, Tassinari CA, Birnaumer N, Rubboli G. Permutation entropy to detect vigilance changes and preictal state from scalp EEG in epileptic patients: A preliminary study. Neurological Science. 2008 ; 29: 3-9
4. Cetisli B. Development of adaptive neuro-fuzzy classifier using linguistic hedges: Part 1. Expert Systems with Applications. 2010 ; 37: 6093-6101
5. Costa M, Goldberger AL, Peng CK. Multiscale entropy analysis of complex physiological time series. Physical Review Letters. 2002 ; 89: 068102-1-068102-4
6. Costa M, Goldberger AL, Peng CK. Multiscale entropy analysis of biological signals. Physical Review E. 2005 ; 71: 021906-021906
7. El-Mezyani T, Wilson R, Sattler M, Srivastava SK, Edrington CS, Cartes DA. Quantification of complexity of power electronics based system. IET Electrical Systems in Transportation. 2012 ; 2 (4). 211-222
8. Kankar PK, Sharma SC, Harsha SP. Fault diagnosis of ball bearing using machine learning methods. Expert Systems with Applications. 2011a ; 38(3): 1876-1886
9. Kankar PK, Sharma SC, Harsha SP. Rolling element bearing fault diagnosis using autocorrelation and continuous wavelet transform. Journal of Vibration and Control. 2011b ; 17 (14). 2081-2094
10. Kankar PK, Sharma SC and Harsha SP (2012a) Vibration signature analysis of a high speed rotor supported on ball bearings due to localized defects. Journal of Vibration and Controlhttp://dx.doi.org/10.1177/1077546312448506.
11. Kankar PK, Sharma SC, Harsha SP. Vibration-based fault diagnosis of a rotor bearing system using artificial neural network and support vector machine. International Journal of Modelling, Identification and Control. 2012b ; 15 (3). 185-198
12. Kankar PK, Sharma SC, Harsha SP. Fault diagnosis of rolling element bearing using cyclic autocorrelation and wavelet transform. Neurocomputing. 2013 ; 110: 9-17
13. Li X, Ouyang G, Liang Z. Complexity measure of motor current signals for tool flute breakage detection in end milling. International Journal of Machine Tools and Manufacture. 2008 ; 48: 371-379
14. Loparo KA (2013) Bearing vibration data set Case Western Reverse University. Available at: http://csegroups.case.edu/bearingdatacenter/pages/download-data-file (accessed January 2013).
15. Moller MF. A scaled conjugate gradient algorithm for fast supervised learning. Neural Networks. 1993 ; 6 (4). 525-533
16. Richman JS, Moorman JR. Physiological time series analysis using approximate entropy and sample entropy. American Journal of Physiology-Heart and Circulatory Physiology. 2000 ; 278 (6). H2039 - H2049
17. Samanta B, Al-Balushi KR. Artificial neural networks based fault diagnostics of rolling element bearing using time domain features. Mechanical System and Signal Processing. 2003 ; 17: 317-328
18. Vyas NS, Kumar SD. Artificial neural network design for fault identification in a rotor bearing system. Mechanism and Machine Theory. 2001 ; 36: 157-175
19. Wu SD, Wu PH, Wu CW, Ding JJ, Wang CC. Bearing fault diagnosis based on multiscale entropy and support vector machine. Entropy. 2012 ; 14: 1343-1356
20. Yang JY, Zhang YY, Zhu YS. Intelligent fault diagnosis of rolling element bearing based on SVMs and fractal dimension. Mechanical System and Signal Processing. 2007a ; 21 (5). 2012-2024
21. Yang Y, Dejie Y, Chang J. Fault diagnosis approach for roller bearing based on IMF envelope spectrum and SVM. Measurement. 2007b ; 40: 943-950
22. Zhang L, Xiong G, Liu H, Zou H, Guo W. Bearing fault diagnosis using multi-scale entropy and adaptive neuro-fuzzy inference. Expert Systems with Applications. 2010 ; 37: 6077-6085