A new technique for bearing fault detection in the time-frequency domain
Document Type : Research Paper
Authors
Mechanical Engineering Department, Shahid Chamran University of Ahvaz, Ahvaz, Iran
Abstract
This paper presents a new Fast Kurtogram Method in the time-frequency domain using novel types of statistical features instead of the kurtosis. For this study, the problem of four classes for Bearing Fault Detection is investigated using various statistical features. This research is conducted in four stages. At first, the stability of each feature for each fault mode is investigated. Then, resistance to load changes as well as failure growth is studied. In the end, the resolution and fault detection for each feature using comparison with a determined pattern and the coherence rate is calculated. From the above results, the best feature that is both resistant and repeatable to different variations, as well as having suitable accuracy of detection and resolution, is selected. It is found that kurtosis feature is not in a good condition in comparison with other statistical features such as harmmean and median. This approach increases the fault identification accuracy significantly.
Keywords
[1] H. Yang, J. Mathew, L. Ma, Fault diagnosis of rolling element bearings using basis pursuit, Mechanical Systems and Signal Processing, Vol. 19, No. 2, pp. 341-356, 2005/03/01/, 2005.
[2] M. Liang, I. Soltani Bozchalooi, An energy operator approach to joint application of amplitude and frequency-demodulations for bearing fault detection, Mechanical Systems and Signal Processing, Vol. 24, No. 5, pp. 1473-1494, 2010/07/01/, 2010.
[3] W. Su, F. Wang, H. Zhu, Z. Zhang, Z. Guo, Rolling element bearing faults diagnosis based on optimal Morlet wavelet filter and autocorrelation enhancement, Mechanical Systems and Signal Processing, Vol. 24, No. 5, pp. 1458-1472, 2010/07/01/, 2010.
[4] D. Dou, J. Yang, J. Liu, Y. Zhao, A rule-based intelligent method for fault diagnosis of rotating machinery, Knowledge-Based Systems, Vol. 36, pp. 1-8, 2012/12/01/, 2012.
[5] B. Li, P.-y. Liu, R.-x. Hu, S.-s. Mi, J.-p. Fu, Fuzzy lattice classifier and its application to bearing fault diagnosis, Applied Soft Computing, Vol. 12, No. 6, pp. 1708-1719, 2012/06/01/, 2012.
[6] D. Wang, P. W. Tse, K. L. Tsui, An enhanced Kurtogram method for fault diagnosis of rolling element bearings, Mechanical Systems and Signal Processing, Vol. 35, No. 1, pp. 176-199, 2013/02/01/, 2013.
[7] H. Xu, G. Chen, An intelligent fault identification method of rolling bearings based on LSSVM optimized by improved PSO, Mechanical Systems and Signal Processing, Vol. 35, No. 1, pp. 167-175, 2013/02/01/, 2013.
[8] H. Al-Bugharbee, I. Trendafilova, A fault diagnosis methodology for rolling element bearings based on advanced signal pretreatment and autoregressive modelling, Journal of Sound and Vibration, Vol. 369, pp. 246-265, 2016/05/12/, 2016.
[9] P. Baraldi, F. Cannarile, F. Di Maio, E. Zio, Hierarchical k-nearest neighbours classification and binary differential evolution for fault diagnostics of automotive bearings operating under variable conditions, Engineering Applications of Artificial Intelligence, Vol. 56, pp. 1-13, 2016/11/01/, 2016.
[10] V. Vakharia, V. K. Gupta, P. K. Kankar, Bearing Fault Diagnosis Using Feature Ranking Methods and Fault Identification Algorithms, Procedia Engineering, Vol. 144, pp. 343-350, 2016/01/01/, 2016.
[11] J. Singh, A. K. Darpe, S. P. Singh, Rolling element bearing fault diagnosis based on Over-Complete rational dilation wavelet transform and auto-correlation of analytic energy operator, Mechanical Systems and Signal Processing, Vol. 100, pp. 662-693, 2018/02/01/, 2018.
[12] M. Ö. Yaylı, Stability analysis of gradient elastic microbeams with arbitrary boundary conditions, Journal of Mechanical Science and Technology, Vol. 29, No. 8, pp. 3373-3380, 2015/08/01, 2015.
[13] M. Özgür Yayli, An efficient solution method for the longitudinal vibration of nanorods with arbitrary boundary conditions via a hardening nonlocal approach, Journal of Vibration and Control, Vol. 24, No. 11, pp. 2230-2246, 2018/06/01, 2016.
[14] M. Ö. Yayli, Buckling analysis of a microbeam embedded in an elastic medium with deformable boundary conditions, Micro & Nano Letters, Vol. 11, No. 11, pp. 741-745, 2016.
[15] M. Ö. Yayli, On the torsional vibrations of restrained nanotubes embedded in an elastic medium, Journal of the Brazilian Society of Mechanical Sciences and Engineering, Vol. 40, No. 9, pp. 419, 2018/08/14, 2018.
[16] M. Ö. Yayli, Torsional vibrations of restrained nanotubes using modified couple stress theory, Microsystem Technologies, Vol. 24, No. 8, pp. 3425-3435, 2018/08/01, 2018.
[17] A. Moradi, H. Makvandi, I. B. Salehpoor, Multi objective optimization of the vibration analysis of composite natural gas pipelines in nonlinear thermal and humidity environment under non-uniform magnetic field, JOURNAL OF COMPUTATIONAL APPLIED MECHANICS, Vol. 48, No. 1, pp. 53-64, 2017.
[18] P. Alimouri, S. Moradi, R. Chinipardaz, UPDATING FINITE ELEMENT MODEL USING FREQUENCY DOMAIN DECOMPOSITION METHOD AND BEES ALGORITHM, THE JOURNAL OF COMPUTATIONAL APPLIED MECHANICS, Vol. 48, No. 1, pp. 75-88, 2017. English
[19] M. Goodarzi, M. N. Bahrami, V. Tavaf, Refined plate theory for free vibration analysis of FG nanoplates using the nonlocal continuum plate model, Journal of Computational Applied Mechanics, Vol. 48, No. 1, pp. 123-136, 2017.
[20] A. Zargaripoor, A. Bahrami, M. Nikkhah bahrami, Free vibration and buckling analysis of third-order shear deformation plate theory using exact wave propagation approach, Journal of Computational Applied Mechanics, Vol. 49, No. 1, pp. 102-124, 06/01, 2018. en
[21] A. Zargaripoor, M. Nikkhah bahrami, A wave-based computational method for free vibration and buckling analysis of rectangular Reddy nanoplates, Journal of Computational Applied Mechanics, 05/23, 2018. en
[22] M. Zarchi, B. Attaran, Performance improvement of an active vibration absorber subsystem for an aircraft model using a bees algorithm based on multi-objective intelligent optimization, Engineering Optimization, Vol. 49, No. 11, pp. 1905-1921, 2017/11/02, 2017.
[23] M. Zarchi, B. Attaran, Improved design of an active landing gear for a passenger aircraft using multi-objective optimization technique, Structural and Multidisciplinary Optimization, Vol. 59, No. 5, pp. 1813-1833, 2019/05/01, 2019.
[24] S. Patil, V. Phalle, Fault Detection of Anti-friction Bearing using Ensemble Machine Learning Methods, International Journal of Engineering, Vol. 31, No. 11, pp. 1972-1981, 2018. En
[25] M. Heidari, Fault Detection of Bearings Using a Rule-based Classifier Ensemble and Genetic Algorithm, International Journal of Engineering, Transactions A: Basics, Vol. 30, pp. 604-609, 04/01, 2017.
[26] X. Li, L. Han, H. Xu, Y. Yang, H. Xiao, Rolling bearing fault analysis by interpolating windowed DFT algorithm, International Journal of Engineering, Transactions A: Basics, Vol. 32, pp. 121-126, 01/01, 2019.
[27] A. Moshrefzadeh, A. Fasana, The Autogram: An effective approach for selecting the optimal demodulation band in rolling element bearings diagnosis, Mechanical Systems and Signal Processing, Vol. 105, pp. 294-318, 2018/05/15/, 2018.
[28] J. Antoni, The spectral kurtosis of nonstationary signals: Formalisation, some properties, and application, in Proceeding of, 1167-1170.
[29] Diagnostic Techniques, Vibration‐based Condition Monitoring, pp. 167-227, 2010/12/19, 2010.
[30] J. Antoni, R. B. Randall, The spectral kurtosis: application to the vibratory surveillance and diagnostics of rotating machines, Mechanical Systems and Signal Processing, Vol. 20, No. 2, pp. 308-331, 2006/02/01/, 2006.
[31] J. Antoni, Fast computation of the kurtogram for the detection of transient faults, Mechanical Systems and Signal Processing, Vol. 21, No. 1, pp. 108-124, 2007/01/01/, 2007.
)
Volume 51, Issue 1
June 2020Pages 137-143
- Receive Date: 24 May 2019
- Revise Date: 30 August 2019
- Accept Date: 11 September 2019