A Comparison of Vibration based Bearing Fault Diagnostic Methods
##plugins.themes.bootstrap3.article.main##
##plugins.themes.bootstrap3.article.sidebar##
Abstract
This paper presents a benchmark study in which three vibration based bearing diagnostic algorithms are compared. The three methods are a data driven approach developed by the Linz Center of Mechatronics (LCM), a physics based method of Flanders Make (FM) and an approach developed by the Center for Intelligent Maintenance Systems (IMS). Two experimental tests have been performed, an accelerated lifetime test to degrade a bearing and introduce an operational bearing fault and a gearbox test containing various faulty test bearings. The methods are compared based on their diagnostic performance, practical applicability, training and configuration requirements. Based on the accelerated lifetime test, it is concluded that the method of IMS and FM, employing bearing specific features, showed to be more sensitive for early bearing fault detection than purely statistical features used in the method of LCM. On the contrary, the method of LCM does not require specific system knowledge and is not limited to bearing monitoring only. The method is more widely applicable to fault monitoring problems. The methods of IMS and LCM seem to outperform the method of FM in the gearbox test. However, the training and testing data used by those methods were acquired for the same bearing sample and for the same bearing assembly. This could lead to a high correlation between the training and testing data and hence a misleading classification accuracy. Therefore, attention should be paid to the quality of the training data. It is concluded that the training data should comprise all relevant system variations, including e.g. remounting of the bearing, to ensure that the classification is uniquely based on bearing fault related effects. The methods of IMS and LCM require validated training data of both healthy and faulty bearing scenarios, whereas the method of FM relies on training data of healthy bearings only. In practice, the availability of training data of faulty bearings is often scarce and could make the adoption more complicated. The findings presented in this paper serve as a guideline to support the selection of an appropriate method for practical applications.
##plugins.themes.bootstrap3.article.details##
diagnostic algorithm, bearing diagnostics, accelerated life tests, gearbox test, benchmark
Albarbar, A., Mekid, S., Starr, A., & Pietruszkiewicz, R. (2008). Suitability of MEMS accelerometers for condition monitoring: An experimental study. Sensors, 8(2), 784–799.
Antoni, J., & Randall, R. (2006). The spectral kurtosis: application to the vibratory surveillance and diagnostics of rotating machines. Mechanical Systems and Signal Processing, 20, 308-331.
Assaad, B., Eltabach, M., & Antoni, J. (2014). Vibration based condition monitoring of a multistage epicyclic gearbox in lifting cranes. Mechanical Systems and Signal Processing, 42, 351-367.
Bajric, R., Zuber, N., Skrimpas, G., & Mijatovic, N. (2016). Feature extraction using discrete wavelet transform for gear fault diagnosis of wind turbine gearbox. Shock and Vibration, 2016.
Barbini, L., Ompusunggu, A., Hillis, A., du Bois, J., & Bartic, A. (2017, jul). Phase editing as a signal pre-processing step for automated bearing fault detection. Mechanical Systems and Signal Processing, 91, 407–421.
Bechhoefer, E., & Kingsley, M. (2009). A review of time synchronous average algorithms. In Annual conference of the prognostics and health management society, san diego, ca, sept (pp. 24–33).
Boldt, F., Rauber, T., & Varej˜ao, F. (2013). Feature extraction and selection for automatic fault diagnosis of rotating machinery..
Borghesani, P., Pennacchi, P., Randall, R. B., Sawalhi, N., & Ricci, R. (2013). Application of cepstrum prewhitening for the diagnosis of bearing faults under variable speed conditions. Mechanical Systems and Signal Processing, 36(2), 370–384.
Decker, H., & Lewicki, D. (2003). Spiral bevel pinion crack detection in a helicopter gearbox. In Proceedings of the 59th annual forum and technology display.
de Ridder, D., et al. (Eds.). (2017). Classification, parameter estimation and state estimation: An engineering approach using matlab, 2nd edition. John Wiley & Sons, Ltd.
Di, Y., Jin, C., Bagheri, B., Shi, Z., Ardakani, H., Tang, Z., & Lee, J. (2018). Fault prediction of power electronics modules and systems under complex working conditions. Computers in Industry, 97, 1–9.
Dy, J., & Brodley, C. (2004). Feature selection for unsupervised learning. Journal of Machine Learning Research, 5.
Guyon, I., & Elisseeff, A. (2003). An introduction to variable and feature selection. Journal of Machine Learning Research, 3, 1157-1182.
Halme, J., & Andersson, P. (2009). Rolling contact fatigue and wear fundamentals for rolling bearing diagnostics - state of the art. Journal of Engineering Tribology, 224, 377–393.
Hawkins, D., & Olwell, D. (Eds.). (1998). Cumulative sum charts and charting for quality improvement. Springer.
Heidari Bafroui, H., & Ohadi, A. (2014). Application of wavelet energyband shannon entropy for feature extraction in gearbox fault detection under varying speed conditions. Neurocomputing, 133, 437-445.
Henriquez, P., Alonso, J., Ferrer, M., & Travieso, C. (2014). Review of automatic fault diagnosis systems using audio and vibration signals. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 44(5), 642–652.
Jablonski, A., Barszcz, T., Bielecka, M., & Breuhaus, P. (2013). Modeling of probability distribution functions for automatic threshold calculation in condition monitoring systems. Measurement: Journal of the International Measurement Confederation, 46(1), 727–738. doi: 10.1016/j.measurement.2012.09.011
Jafarizadeh, M., Hassannejad, R., Ettefagh, M., & Chitsaz, S. (2008). Asynchronous input gear damage diagnosis using time averaging and wavelet filtering. Mechanical Systems and Signal Processing, 22, 172-201.
Jalil, M., Butt, F. A., & Malik, A. (2013). Short-time energy, magnitude, zero crossing rate and autocorrelation measurement for discriminating voiced and unvoiced segments of speech signals. In The international conference on technological advances in electrical, electronics and computer engineering (p. 208-212).
Jia, X., Zhao, M., Di, Y., Jin, C., & Lee, J. (2017). Investigation on the kurtosis filter and the derivation of convolutional sparse filter for impulsive signature enhancement. Journal of Sound and Vibration, 386, 433–448.
Jia, X., Zhao, M., Di, Y., Li, P., & Lee, J. (2018). Sparse filtering with the generalized lp/lq norm and its applications to the condition monitoring of rotating machinery. Mechanical Systems and Signal Processing, 102, 198–213.
Kohonen, T. (1998). The self-organizing map. Neurocomputing, 21(1-3), 1–6.
Kollialil, E. S., Gopan, K. G., Harsha, A., & Joseph, L. A. (2013, Oct). Single feature-based non-convulsive epileptic seizure detection using multi-class svm. In 2013 international conference on emerging trends in communication, control, signal processing and computing applications (c2spca) (p. 1-6).
Konstantin-Hansen, H., & Herlufsen, H. (2010). Envelope and ceptstrum analyses for machinery fault identification. Sound & Vibration, 10-12.
Lei, Y., He, Z., Zi, Y., & Hu, Q. (2007). Fault diagnosis of rotating machinery based on multiple anfis combination with gas. Mechanical Systems and Signal Processing, 21, 2280-2294.
McClintic, K., Lebold, M., Maynard, K., Byington, C., & Campbell, R. (2000). Residual and difference feature analysis with transitional gearbox data. In Proceedings of the 54th meeting of the society for machinery failure prevention technology.
McFadden, P., & Toozhy, M. (2000). Applications of synchronous averaging to vibration monitoring of rolling element bearings. Mechanical Systems and Signal Processing, 14(6), 891-906.
McLachlan, G. (1999). Mahalanobis distance. Resonance, 4(6), 20-26.
Ompusunggu, A. P., Ooijevaar, T., Kilundu, B., & Devos, S. (2018). Automated bearing fault diagnostics with cost-effective vibration sensor. In J. Mathew, C. Lim, & L. Ma (Eds.), Asset intelligence through integration and interoperability and contemporary vibration engineering technologies. proceedings of the 12th world congress on engineering asset management (pp. 463–472). Springer International Publishing.
Randall, R., & Antoni, J. (2011, feb). Rolling element bearing diagnostics - A tutorial. Mechanical Systems and Signal Processing, 25(2), 485–520.
Rao, V. (2015). Spectral kurtosis theory: A review through simulations. Global Journal of Researches in Engineering: Electrical and Electronics Engineering, 15(6), 49-61.
Sait, A., & Sharaf-Eldeen, Y. (2011). A Review of Gearbox Condition Monitoring Based on vibration Analysis Techniques Diagnostics and Prognostics. In T. Proulx (Ed.), Rotating machinery, structural health monitoring, shock and vibration, volume 5, proceedings of the 29th imac, a conference on structural dynamics, 2011 (pp. 359–374). Jacksonville, FL; United States: The Society for Experimental Mechanics, Inc.
Satyam, M., Sudhakara, R., & Devy, C. (1994). Cepstrum analysis - an advanced technique in vibration analysis of defects in rotating machinery. Defence Science Journal, 44(1), 53-60.
Sawalhi, N., Randall, R. B., & Forrester, D. (2014). Separation and enhancement of gear and bearing signals for the diagnosis of wind turbine transmission systems. Wind Energy, 17(5), 729–743.
Sharma, V., & Parey, A. (2016). A review of gear fault diagnosis using various condition indicators. Procedia Engineering, 144, 253-263.
Shen, C.,Wang, D., Kong, F., & Tse, P. (2013). Fault diagnosis of rotating machinery based on the statistical parameters of wavelet packet paving and a generic support vector regressive classifier. Measurement, 46, 1551-1564.
Singh, S., & Vishwakarma, M. (2015). A review of vibration analysis techniques for rotating machines. International Journal of Engineering Research & Technology, 4(03), 757-761.
Suma, S. A., & Gurumurthy, K. S. (2010). Novel pitch extraction methods using average magnitude difference function (amdf) for lpc speech coders in noisy environments. In 2010 2nd international conference on signal processing systems (Vol. 1, p. V1-636-V1-640).
Tax, D. (2001). One-class classification (Unpublished doctoral dissertation). Delft University of Technology.
Wang, D., Tsui, K., & Mia, Q. (2017). Prognostics and health management: A review of vibration based bearing and gear health indicators. IEEE Access, 6, 665-676.
Wang, H., & Chen, P. (2007). Fault diagnosis of centrifugal pump using symptom parameters in frequency domain. Agricultural Engineering International: the CIGR Ejournal, IX.