[go: up one dir, main page]
Skip to main content

Advertisement

Springer Nature Link
Log in

RT-OPTICS: real-time classification based on OPTICS method to monitor bearings faults

  • Published:
Journal of Intelligent Manufacturing Aims and scope Submit manuscript

Abstract

The complexity of the current installations requires advanced and effective monitoring techniques. The most commonly used technique is the vibratory analysis. Despite the large number of existing methods for detection, diagnosis and monitoring of bearing defects, the scientific community is widely interested in learning methods. These methods allow automatic detection and reliable diagnosis. This paper presents anew real-time unsupervised pattern recognition approach for the detection and diagnosis of bearings defects: RT-OPTICS. This approach focuses on two steps of damage evolution: defect detection by classification and monitoring of the new cluster representing the degraded state of the bearing. These two steps are performed by a two-dimensional method implementing scalar indicators: Kurtosis and Root Mean Square values. These two indicators provide additional information about the presence of defects in the bearing. The first step deploys RT-OPTICS based on the real-time unsupervised ordering points to identify clustering structure (OPTICS) classification to detect defects on inner and/or outer bearing races. The next step is to monitor the state of degradation using three parameters of the new cluster: the center jump, density and contour of this cluster. After a validation on simulated signals which variations of parameters were tested, this approach was tested under experimental conditions on a test bench made up of N.206.E.G15bearings, with varying load and angular velocity. A comparative study is carried out between the suggested approach and (i) a classical approach: monitoring of scalar indicators over time and (ii) a dynamic classification method (DBSCAN).

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18

Similar content being viewed by others

References

  • Antoni, J., & Randall, B. (2002). Differential diagnosis of gear and bearing faults. Journal of Vibration and Acoustics, 124(2), 165–171.

    Article  Google Scholar 

  • Ankerst, M., Breunig, M. M., Kriegel, H., & Sander, J. (1999). OPTICS: Ordering points to identify the clustering structure, SIGMOD ’99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data, 8(2), pp. 49–60.

  • Barber, C. B., Dobkin, D. P., & Huhdanpaa, H. T. (1996). The quickhull algorithm for convex hulls. ACM Transactions on Mathematical Software, 22(4), 469–483.

    Article  Google Scholar 

  • Baydar, N., & Ball, A. (2001). A comparative study of acoustic and vibration signals in detection of gear failures using Wigner–Ville distribution. Mechanical Systems and Signal Processing, 15(6), 1091–1107.

    Article  Google Scholar 

  • Chen, Y., Miao, D., & Wang, R. (2010). A rough set approach to feature selection based on ant colony optimization. Pattern Recognition Letters, 31(3), 226–233.

    Article  Google Scholar 

  • Chen, Z., & Li, Y. F. (2011). Anomaly detection based on enhanced DBScan algorithm. Procedia Engineering, 15, 178–182.

    Article  Google Scholar 

  • Chiementin, X. (2007). Localisation et quantification des sources vibratoires dans le cadre d’une maintenance préventive conditionnelle en vue de fiabiliser le diagnostic et le suivi de l’endommagement des composants mécaniques tournants : application aux roulements à billes. Ph.D. thesis, Université de Reims Champagne-Ardenne, France, 2007.

  • Chinmaya, K., & Mohanty, A. R. (2006). Monitoring gear vibrations through motor current signature analysis and wavelet transform. Mechanical Systems and Signal Processing, 20(1), 158–187.

    Article  Google Scholar 

  • Dellomo, M. R. (1999). Helicopter gearbox fault detection: A neural network based approach. Journal of Vibration and Acoustics, 121(3), 265–272.

    Article  Google Scholar 

  • Dybala, J., & Zimroz, R. (2012). Application of empirical mode decomposition for impulsive signal extraction to detect bearing damage : industrial case study (pp. 257–266). Berlin: Springer.

    Google Scholar 

  • Denaud, L. E. (2006). Analyses vibratoires et acoustiques du déroulage. PhD thesis, l’Ecole Nationale Supérieure d’Arts et Métiers, 2006. 30, 32.

  • Ester, M., Kriegel, H. P., Sander, J., & Xu, X. (1996). A density based algorithm for discovering clusters in large spatial databases with noise. In Proceedings of the 2nd ACM International Conference on Knowledge Discovery and Data Mining (KDD), 226–231. Portland, OR.

  • Ettefagh, M. M., Ghaemi, M., & Yazdanian Asr, M. (2014). Bearing fault diagnosis using hybrid genetic algorithm k-means clustering. Innovations in Intelligent Systems and Applications, Alberobello, 38(64), 69.

    Google Scholar 

  • He, Y., Pan, M., Luo, F., Chen, D., & Hu, X. (2013). Support vector machine and optimized feature extraction in integrated Eddy current instrument. Measurement, 46, 764–774.

    Article  Google Scholar 

  • Huang, N. E., Shen, Z., Long, S. R., Wu, M. C., Shih, H. H., Zheng, Q., et al. (1998). The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 454, 903–995.

    Article  Google Scholar 

  • Jack, L. B., & Nandi, A. K. (2000). Genetic algorithms for feature selection in machine condition monitoring with vibration signals. IEE Proceedings - Vision, Image and Signal Processing., 147(3), 205–212.

    Article  Google Scholar 

  • Jack, L. B., & Nandi, A. K. (2002). Fault detection using support vector machines and artificial neural networks, augmented by genetic algorithms. Mechanical Systems and Signal Processing, 16(2–3), 373–390.

    Article  Google Scholar 

  • Jahirabadkar, S., & Kulkarni, P. (2014). Algorithm to determine \(\varepsilon \)-distance parameter in density based clustering. Expert Systems with Applications, 41, 2939–2946.

    Article  Google Scholar 

  • Jardine, A. K. S., Lin, D., & Banjevic, D. (2006). A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mechanical Systems and Signal Processing, 20(4), 1483–1510.

    Article  Google Scholar 

  • Jiang, L., Cao, Y., Yin, H., & Deng, K. (2013). An improved kernel k-mean cluster method and its application in fault diagnosis of roller bearing. Engineering, 5(1), 44–49.

    Article  Google Scholar 

  • Kerroumi, S., Chiementin, X., & Rasolofondraibe, L. (2013). Dynamic classification method of fault indicators for bearings monitoring. Mechanics & Industry, 14(2), 115–120.

    Article  Google Scholar 

  • Khushaba, R. N., Al-Ani, A., & Al-Jumaily, A. (2011). Feature subset selection using differential evolution and a statistical repair mechanism. Expert Systems with Applications, 38(9), 11515–11526.

    Article  Google Scholar 

  • Konar, P., & Chattopadhyay, P. (2011). Bearing fault detection of induction motor using wavelet and Support Vector Machines ( SVMs ). Applied Soft Computing Journal, 11(6), 4203–4211.

    Article  Google Scholar 

  • Kurek, J., & Osowski, S. (2010). Support vector machine for fault diagnosis of the broken rotor bars of squirrel-cage induction motor. Neural Computing and Applications, 19(4), 557–564.

    Article  Google Scholar 

  • Kudo, M., & Sklansky, J. (2000). Comparison of algorithms that select features for pattern classifiers. Pattern Recognition, 33(1), 25–41.

    Article  Google Scholar 

  • Li, H., Deng, X., & Dai, H. (2007). Structural damage detection using the combination method of emd and wavelet analysis. Mechanical Systems and Signal Processing, 21(1), 298–306.

    Article  Google Scholar 

  • Li, Z., Yan, X., Tian, Z., Yuan, C., Peng, Z., & Li, L. (2013). Blind vibration component separation and nonlinear feature extraction applied to the nonstationary vibration signals for the gearbox multi-fault diagnosis. Measurement, 46(1), 259–271.

    Article  Google Scholar 

  • Liu, H., & Yu, L. (2005). Toward integrating feature selection algorithms for classification and clustering. IEEE Transactions on Knowledge and Data Engineering, 17(4), 491–502.

    Article  Google Scholar 

  • Martin, H., & Honarvar, F. (1995). Application of statistical moments to bearing failure detection. Applied Acoustics, 44(1), 67–77.

    Article  Google Scholar 

  • McCormick, A. C., Nandi, A. K., & Member, S. (1997). Real-time classification of rotating shaft loading conditions using artificial neural networks. IEEE Transactions on Neural Networks, 8(3), 748–757.

    Article  Google Scholar 

  • McCormick, A. C., & Nandi, A. K. (1997). Classification of the rotating machine condition using artificial neural networks. Proceedings of Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 211(6), 439–450.

    Google Scholar 

  • Nikolaou, N. G., & Antoniadis, I. A. (2002). Rolling element bearing fault diagnosis using wavelet packets. Ndt & E International, 35(3), 197–205.

    Article  Google Scholar 

  • Parey, A., El Badaoui, M., Guillet, F., & Tandona, N. (2006). Dynamic modeling of spur gear pair and application of empirical mode decomposition-based statistical analysis for early detection of localized tooth defect. Journal of Sound and Vibration, 294(3), 547–561.

    Article  Google Scholar 

  • Paya, B. A., Esat, I. I., & Badi, M. N. M. (1997). Artificial neural networkbased fault diagnostics of rotating machinery using wavelettransforms as a preprocessor. Mechanical Systems and SignalProcessing, 11, 751–765.

    Article  Google Scholar 

  • Qian, Y., Xu, L., Li, X., Lin, X., Kraslawski, L., & Lubres, A. (2008). An expert system development and implementation for real-time fault diagnosis of a lubricating oil refining process. Expert Systems with Applications, 35(3), 1251–1266.

    Article  Google Scholar 

  • Rafiee, J., Arvani, F., Harifi, A., & Sadeghi, M. H. (2007). Intelligent condition monitoring of a gearbox using artificial neural network. Mechanical Systems and Signal Processing, 21, 1746–1754.

    Article  Google Scholar 

  • Rafiee, J., Rafiee, M. A., & Tse, P. W. (2010). Expert systems with applications application of mother wavelet functions for automatic gear and bearing fault diagnosis. Expert Systems With Applications, 37(6), 4568–4579.

    Article  Google Scholar 

  • Randall, B. (2011). Vibration-based condition monitoring: Industrial, aerospace and automotive applications. New York: Wiley.

    Book  Google Scholar 

  • Rubini, R., & Meneghetti, U. (2001). Application of the envelope and wavelet transform analyses for the diagnosis of incipient faults in ball bearings. Mechanical Systems and Signal Processing, 15(2), 287–302.

    Article  Google Scholar 

  • Saimurugana, M., Ramachandran, K. I., Sugumaran, V., & Sakthivel, N. R. (2011). Multi component fault diagnosis of rotational mechanical system based on decision tree and support vector machine. Expert Systems with Applications, 2011(51), 59.

    Google Scholar 

  • Safizadeh, M.S., Lakis, A.A., & Thomas, M. (2005). Using short-time fourier transform in machinery diagnosis. In Proceedings of the Fourth WSEAS International Conference on Electronic. Signal Processing and Control, 20, (pp. 1–7).

  • Samanta, B. (2004). Gear fault detection using artificial neural networks and support vector machines with genetic algorithms. Mechanical Systems and Signal Processing, 18(3), 625–644.

    Article  Google Scholar 

  • Samanta, B., Al-Balushi, K. R., & Al-Araimi, S. A. (2001). Use of genetic algorithm and artificial neural network for gear condition diagnostics (pp. 449–456). UK: Proceedings of COMADEM, University of Manchester.

  • Samanta, B., Al-Balushi, K. R., & Al-Araimi, S. A. (2003). Artificial neural networks and support vector machines with genetic algorithm for bearing fault detection. Engineering Applications of Artificial Intelligence, 16(7–8), 657–665.

    Article  Google Scholar 

  • Stepanic, P., Latinovic, I. V., & Djurovic, Z. (2009). A new approach to detection of defects in rolling element bearings based on statistical pattern recognition. International Journal of Advanced Manufacturing Technology, 45(1), 91–100.

    Article  Google Scholar 

  • Sung, C. K., Tai, H. M., & Chen, C. W. (2000). Locating defects of gear system by the technique of wavelet transform. Mechanism and Machine Theory, 35(5), 1169–1182.

    Article  Google Scholar 

  • Shin, K., & Hammond, J. (2008). Fundamentals of signal processing for sound and vibration engineers. Hoboken: John Wiley & Sons Ltd.

    Google Scholar 

  • Vachtsevanos, G., lewis, F., Roemer, M., Hess, A., & Wu, B. (2006). Intelligent fault diagnosis and prognosis for engineering systems. New Jersey: Wiley.

    Book  Google Scholar 

  • Vapnik, V. N. (1998). Statistical learning theory. NewYork: Wiley Interscience publication.

    Google Scholar 

  • Vapnik, V. N. (2000). The nature of statistical learning theory (2nd ed.). Berlin: Springer.

    Book  Google Scholar 

  • Wang, W. J., & MacFadden, P. D. (1996). Application of wavelets to gearbox vibration signals for fault detection. Journal of Sound and Vibration, 192(5), 927–939.

    Article  Google Scholar 

  • Widodo, A., Yang, B., & Han, T. (2007). Combination of independent component analysis and support vector machines for intelligent faults diagnosis of induction motors. Expert Systems with Applications, 32(2), 299–312.

    Article  Google Scholar 

  • Williams, W. J., & Zalubas, E. J. (2000). Helicopter transmission fault detection via timefrequency, scale and spectral methods. Mechanical system and signal processing, 14(4), 545–559.

    Article  Google Scholar 

  • Wu, X., Yu, K., Ding, W., Wang, H., & Zhu, X. (2013). Online feature selection with streaming features. Pattern Analysis and Machine Intelligence, 35(5), 1178–1192.

    Article  Google Scholar 

  • Yang, Y., & Junsheng, C. (2006). A roller bearing fault diagnosis method based on emd energy entropy and ann. Journal of Sound and Vibration, 294(1–2), 269–277.

    Google Scholar 

  • Yang, Y., Yu, D., & Cheng, J. (2007). A fault diagnosis approach for roller bearing based on IMF envelope spectrum and SVM. Measurement, 40(9–10), 943–950.

    Article  Google Scholar 

  • Yang, Z. L., Wang, B., Dong, X. H., & Liu, H. (2012). Expert system of fault diagnosis for gear box in wind turbine. Systems Engineering Procedia, 4, 189–195.

    Article  Google Scholar 

  • Yiakopoulos, C. T., Gryllias, K. C., & Antoniadis, I. A. (2011). Expert systems with applications rolling element bearing fault detection in industrial environments based on a K -means clustering approach. Expert Systems With Applications, 38(3), 2888–2911.

    Article  Google Scholar 

  • Zhang, Y., Zuo, H., & Bai, F. (2013). Classification of fault location and performance degradation of a roller bearing. Measurement, 46(3), 1178–1189.

    Article  Google Scholar 

  • Zheng, H., Li, Z., & Chen, X. (2002). Gear faults diagnosis based on continuous wavelet transform. Mechanical Systems and Signal Processing, 16(2–3), 447–457.

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to thank the Algerian Ministry of Higher Education and Scientific Research for financial support in the framework of the PNE 2015-2016 Program.

Author information

Authors and Affiliations

  1. Applied Precision Mechanics Laboratory, Institute of Optics and Precision Mechanics, Setif -1- University, 19000, Sétif, Algeria

    D. Benmahdi & A. Felkaoui

  2. CReSTIC, University of Reims Champagne Ardenne, Moulin de la Housse, 51687, Reims Cedex 2, France

    L. Rasolofondraibe

  3. GRESPI, University of Reims Champagne Ardenne, Moulin de la Housse, 51687, Reims Cedex 2, France

    X. Chiementin & S. Murer

Authors
  1. D. Benmahdi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. L. Rasolofondraibe
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. X. Chiementin
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. S. Murer
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. A. Felkaoui
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to D. Benmahdi.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Benmahdi, D., Rasolofondraibe, L., Chiementin, X. et al. RT-OPTICS: real-time classification based on OPTICS method to monitor bearings faults. J Intell Manuf 30, 2157–2170 (2019). https://doi.org/10.1007/s10845-017-1375-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10845-017-1375-6

Keywords