Condition Monitoring of Bearing Damage in Electromechanical Drive Systems by Using Motor Current Signals of Electric Motors: A Benchmark Data Set for Data-Driven Classification
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Abstract
This paper presents a benchmark data set for condition monitoring of rolling bearings in combination with an extensive description of the corresponding bearing damage, the data set generation by experiments and results of data-driven classifications used as a diagnostic method. The diagnostic method uses the motor current signal of an electromechanical drive system for bearing diagnostic. The advantage of this approach in general is that no additional sensors are required, as current measurements can be performed in existing frequency inverters. This will help to reduce the cost of future condition monitoring systems. A particular novelty of the present approach is the monitoring of damage in external bearings which are installed in the drive system but outside the electric motor. Nevertheless, the motor current signal is used as input for the detection of the damage. Moreover, a wide distribution of bearing damage is considered for the benchmark data set. The results of the classifications show that the motor current signal can be used to identify and classify bearing damage within the drive system. However, the classification accuracy is still low compared to classifications based on vibration signals. Further, dependency on properties of those bearing damage that were used for the generation of training data are observed, because training with data of artificially generated and real bearing damages lead to different accuracies. Altogether a verified and systematically generated data set is presented and published online for further research.
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condition monitoring, Condition Based Maintenance, Bearing Faults, Motor Current Signature Analysis, data-driven method, Benchmark Dataset
Bartz, W. J. (1985). Wälzlagertechnik // Lagerungen, Technologie, Berechnungen, Auswahl, Konstruktion und Anwendung. Sindelfingen: Expert Verlag.
Bellini, A., Filippetti, F., Tassoni, C., & Capolino, G.-A. (2008). Advances in Diagnostic Techniques for Induction Machines. IEEE Transactions on Industrial Electronics, 55(12), pp. 4109–4126. doi:10.1109/TIE.2008.2007527
Blödt, M., Granjon, P., Raison, B., & Rostaing, G. (2008). Models for Bearing Damage Detection in Induction Motors Using Stator Current Monitoring. IEEE Transactions on Industrial Electronics, 55(4), pp. 1813–1822. doi:10.1109/TIE.2008.917108
Bonnett, A., & Yung, C. (2008). Increased Efficiency Versus Increased Reliability. IEEE Industry Applications Magazine, 14(1), 29–36. doi:10.1109/MIA.2007.909802
Djeddi, M., Granjon, P., & Leprettre, B. (2007). Bearing Fault Diagnosis in Induction Machine Based on Current Analysis Using High-Resolution Technique. In Institute of Electrical and Electronics Engineers (Ed.), 2007 IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics & Drives (pp. 23–28). Piscataway, NJ, USA: IEEE.
Herold, T., Piantsop Mbo’o, C., & Hameyer, K. (2013). Evaluation of the use of an electrical drive as a sensor for the detection of bearing damage. In Conference on Acoustics, AIA-DAGA 2013. Meran, Italy. Retrieved from http://134.130.107.200/uploads/bibliotest/2013THEvaluation.pdf
International Standards Organization (ISO) (2004). Rolling bearings - Damage and failures - Terms, characteristics and causes. ISO 15243:2010. Genève, Switzerland: International Standards Organization.
Kankar, P., Sharma, S. C., & Harsha, S. (2011). Fault diagnosis of ball bearings using machine learning methods. Expert Systems with Applications, 38, pp. 1876–1886.
Kimotho, J. K., & Sextro, W. (2014). An approach for feature extraction and selection from non-trending data for machinery prognosis. Second European Conference of the Prognostics and Health Management Society, July 8-10, Nantes.
Lessmeier, C., Piantsop Mbo'o, C., Coenen, I., Zimmer, D., & Hameyer, K. (2012). Untersuchung von Bauteilschäden elektrischer Antriebsstränge im Belastungsprüfstand mittels Statorstromanalyse. In K. Nienhaus & P. Burgwinkel (Eds.): Vol. 81. Aachener Schriften zur Rohstoff- und Entsorgungstechnik des Instituts für Maschinentechnik der Rohstoffindustrie, AKIDA 2012. Aachener Kolloquium für Instandhaltung, Diagnose und Anlagenüberwachung (1st ed., pp. 509–521). Aachen: Zillekens.
Lessmeier, C., Enge-Rosenblatt, O., Bayer, C., & Zimmer, D. (2014). Data Acquisition and Signal Analysis from Measured Motor Currents for Defect Detection in Electromechanical Drive Systems. Second European Conference of the Prognostics and Health Management Society, July 8-10, Nantes.
Mbo'o, C. P., Herold, T., & Hameyer, K. (2004). Impact of the load in the detection of bearing faults by using the stator current in PMSM's. In XXI International Conference on Electrical Machines (ICEM) (pp. 1621–1627).
Nandi, S., Toliyat, H. A., & Li, X. (2005). Condition Monitoring and Fault Diagnosis of Electrical Motors—A Review. IEEE Transactions on Energy Conversion, 20(4), 719–729. doi:10.1109/TEC.2005.847955
Nectoux, P., Gouriveau, R., Medjaher, K., Ramasso, E., Morello, B., Zerhouni, N., & Varnier, C. (2012). PRONOSTIA : An experimental platform for bearings accelerated degradation tests. IEEE International Conference on Prognostics and Health Management, PHM'12, July 18 -21, Denver.
Niknam, S. A., Thomas, T., Hines, W. J., & Sawhney, R. (2013). Analysis of Acoustic Emission Data for Bearings subject to Unbalance. International Journal of Prognostics and Health Management, 2013(015).
Obaid, R. R., Habetler, T. G., & Stack, J. R. (2003). Stator current analysis for bearing damage detection in induction motors. In IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives (pp. 182–187).
Pacas, M., Villwock, S., & Dietrich, R. Bearing damage detection in permanent magnet synchronous machines (2009). In 2009 IEEE Energy Conversion Congress and Exposition. ECCE 2009 (pp. 1098–1103).
Paschke, F., Bayer, C., Bator, M., Mönks, U., Dicks, A., Enge-Rosenblatt, O., & Lohwe, V. (2013). Sensorlose Zustandsüberwachung an Synchronmotoren. In F. Hoffmann & E. Hüllermeier (Eds.), Schriftenreihe des Instituts für Angewandte Informatik, Automatisierungstechnik am Karlsruher Institut für Technologie: Vol. 46. Proceedings / 23. Workshop Computational Intelligence. Dortmund, 5. - 6. Dezember 2013 . Karlsruhe: KIT Scientific Publ.
Patil, M. S., Mathew, J., Rajendrakumar, P. K., & Desai, S. (2010). A theoretical model to predict the effect of localized defect on vibrations associated with ball bearing. Special Issue on Advances in Materials and Processing Technologies, 52(9), pp. 1193–1201. doi:10.1016/j.ijmecsci.2010.05.005
Picot, A., Obeid, Z., Régnier, J., Poignant, S., Darnis, O., & Maussion, P. (2014). Statistic-based spectral indicator for bearing fault detection in permanent-magnet synchronous machines using the stator current. Mechanical Systems and Signal Processing, 46(2), pp. 424–441. doi:10.1016/j.ymssp.2014.01.006
Qiu, H., Lee, J., Lin, J., & Yu, G. (2006). Wavelet filter-based weak signature detection method and its application on rolling element bearing prognostics. Journal of Sound and Vibration, 289(4-5), pp.1066–1090. doi:10.1016/j.jsv.2005.03.007
Randall, R. B. (2011). Vibration-based Condition Monitoring. Chichester, UK: John Wiley & Sons, Ltd.
Schaeffler Technologies AG & Co. KG. (2015). Wälzlagerpraxis: Handbuch zur Gestaltung und Berechnung von Wälzlagerungen (4. Aufl.). Mainz: Vereinigte Fachverl.
Schoen, R. R., Habetler, T. G., Kamran, F., & Bartfield, R. G. (1995). Motor bearing damage detection using stator current monitoring. IEEE Transactions on Industry Applications, 31(6), 1274–1279. doi:10.1109/28.475697
Silva, J., & Cardoso, A. (2005). Bearing failures diagnosis in three-phase induction motors by extended Park's vector approach. In 31st Annual Conference of IEEE Industrial Electronics Society, 2005. IECON 2005 (pp. 6 pp).
Smith, W. A., & Randall, R. B. (2015). Rolling element bearing diagnostics using the Case Western Reserve University data: A benchmark study. Mechanical Systems and Signal Processing. doi:10.1016/j.ymssp.2015.04.021
Stack, J. R., Habetler, T. G., & Harley, R. G. (2003, August). Fault classification and fault signature production for rolling element bearings in electric machines. IEEE. Diagnostics for Electric Machines, Power Electronics and Drives,
Tandon, N., & Choudhury, A. (1999). A review of vibration and acoustic measurement methods for the detection of defects in rolling element bearings. Tribology International, 32(8), pp. 469–480. doi:10.1016/S0301-679X(99)00077-8
VDI 3832 (2013). Measurement of structure-borne sound of rolling element bearings in machines and plants for evaluation of condition. Verein Deutscher Ingenieure e.V., Düsseldorf: Beuth Verlag GmbH.
Villwock, S. (2007). Identifikationsmethoden für die automatisierte Inbetriebnahme und Zustandsüberwachung elektrischer Antriebe (Dissertation). Universität Siegen.
Yang, H., Mathew, J., & Ma, L. (2005). Fault diagnosis of rolling element bearings using basis pursuit. Mechanical Systems and Signal Processing, 19(2), pp. 341–356. doi:10.1016/j.ymssp.2004.03.008
Yang, Z., Merrild, U. C., Runge, M. T., Pedersen, G. K., & Hakon Børsting. (2009). A Study of Rolling-Element Bearing Fault Diagnosis Using Motor’s Vibration and Current Signatures. I F A C Workshop Series, pp. 354–359.
Zarei, J., & Poshtan, J. (2009). An advanced Park’s vectors approach for bearing fault detection. Tribology International, 42, pp. 213–219.
Zoubek, H., Villwock, S., & Pacas, M. (2008). Frequency Response Analysis for Rolling-Bearing Damage Diagnosis. IEEE Transactions on Industrial Electronics, 55(12), 4270–4276. doi:10.1109/TIE.2008.2005020
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