Abstract
In this paper, a novel method for rolling bearings fault diagnosis based on empirical wavelet transform with approximate entropy and fuzzy c-means clustering is proposed. The method can identify different types of rolling bearing faults. Firstly, the original vibration signals of rolling bearing are decomposed by empirical wavelet transform to obtain several amplitude and frequency modulation components that have physical meaning. Then, with correlation analysis, the first three amplitude–frequency modulation components most relevant to the original vibration signal are selected and their approximate entropy is calculated as the eigenvector. Finally, the constructed eigenvector matrix is used as the feature for fuzzy c-means clustering to realize the fault diagnosis of rolling bearing. The experimental results show that compared with the fault diagnosis method based on empirical mode decomposition or approximate entropy, the proposed rolling bearing fault diagnosis method has a better fault recognition effect.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Hong S, Zhou Z, Zio E, Han K (2014) Condition assessment for the performance degradation of bearing based on a combinatorial feature extraction method. Digit Signal Proc 27:159–166
Li J, Li M, Zhang J (2017) Rolling bearing fault diagnosis based on time-delayed feed back monostable stochastic resonance and adaptive minimum entropy deconvolution. Sound Vib 401:139–151
Feng Z, Liang M, Chu F (2013) Recent advances in time–frequency analysis methods for machinery fault diagnosis: a review with application examples. Mech Syst Sig Process 38:165–205
Chen J, Li Z, Pan J, Chen G, Zi Y, Yuan J, Chen B, He Z (2016) Wavelet transform based on inner product in fault diagnosis of rotating machinery: a review. Mech Syst Sig Process 71:1–35
Feldman M, Braun S (2017) Nonlinear vibrating system identification via Hilbert decomposition. Mech Syst Sig Process 84:65–96
Lei Y, Lin J, He Z, Zuo MJ (2013) A review on empirical mode decomposition in fault diagnosis of rotating machinery. Mech Syst Sig Process 35:108–126
Zhongliang LV, Peiwen AN, Tang B, Zhang L (2013) The application of EEMD to fault diagnosis of rolling bearing. In: Advanced materials research, pp 765–767
Gilles J (2013) Empirical wavelet transform. IEEE Trans Sig Process 61(16):3999–4010
Cao H, Fan F, Zhou K, He Z (2016) Wheel-bearing fault diagnosis of trains using empirical wavelet transform. Measurement 82:439–449
Zhang J, Sun G, Li L (2013) Research on mechanical fault diagnosis based on LMD approximate entropy and FCM clustering. J Sci Instrum 34(3):714–720
Yan R, Gao RX (2007) Approximate entropy as a diagnostic tool for machine health monitoring. Mech Syst Sig Process 21(2):824–839
Lotfan S, Salehpour N, Adiban H, Mashroutechi A (2015) Bearing fault detection using fuzzy C-means and hybrid C-means-subtractive algorithms. In: 2015 IEEE international conference on fuzzy systems (FUZZ-IEEE). IEEE, Istanbul, Turkey, pp 1–7
Wang YY, Yang JG, Song BY (2011) Study on gasoline engine knock diagnosis based on wavelet transform and fuzzy c-means clustering. Chin Intern Combust Engine Eng 32(4):56–59
Bearing Data Center. Case Western Reserve University. http://www.eecs.case.edu/laboratory/bearing
Acknowledgements
This research was supported by the special foundation of basic scientific research of central colleges, Chang’an University, No. 300102328201 and No. 300102328203.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Bai, L., Zhu, C., Ye, Z., Hui, M. (2020). Rolling Bearings Fault Diagnosis Method Based on EWT Approximate Entropy and FCM Clustering. In: Qin, Y., Jia, L., Liu, B., Liu, Z., Diao, L., An, M. (eds) Proceedings of the 4th International Conference on Electrical and Information Technologies for Rail Transportation (EITRT) 2019. EITRT 2019. Lecture Notes in Electrical Engineering, vol 639. Springer, Singapore. https://doi.org/10.1007/978-981-15-2866-8_7
Download citation
DOI: https://doi.org/10.1007/978-981-15-2866-8_7
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-2865-1
Online ISBN: 978-981-15-2866-8
eBook Packages: EngineeringEngineering (R0)