Asynchronous Motor Test Bench for the Generation and Current Signal Diagnostics of Accelerated Bearing Damage



Christoph Anger Christian Preusche Uwe Klingauf


The data acquisition of run to failure data by means of de- grading components is one of the most delicate tasks in evaluating new diagnostic and prognostic approaches, since it is cost-intensive and time-consuming. Therefore, a test rig for a cost-efficient generation of artificial bearing damages is described below. The test rig is thereby based on an ordinary asynchronous motor.

This paper mainly concentrates on the description of the test rig’s setup and first diagnostic findings. One aim of the experiments is the investigation of several variations of the applied loads for the artificially accelerated bearing aging. Thus, radial force and fluting are examined. The latter causes a damage triggered by a current flow through the test bearing. Both load types reduce the overall lifespan of bearings to about few weeks.

The generated faults are a broken cage and chattermarks due to a radial force higher than the design point. The bearings are diagnosed by means of frequency analysis of the phase current signal, which is produced in the stator of the motor. Beside the current signal, also temperature, vibration and revolution of the shaft are measured, whereas the vibration signal is used only for the comparison to the current signal. The comprehensive measurement concept allows a performance evaluation of diagnostic and prognostic algorithms based on different physical indicators.

It can be shown that especially the current frequency spectrum of a faulty bearing differs significantly from a healthy one. In order to face the high amount of measurement data, the Principal Component Analysis is used for data reduction to generate features for the diagnosis and prognosis. Thus, a classification of different fault modes and loading conditions is possible.

How to Cite

Anger, C., Preusche, C. ., & Klingauf, U. . (2015). Asynchronous Motor Test Bench for the Generation and Current Signal Diagnostics of Accelerated Bearing Damage. Annual Conference of the PHM Society, 7(1).
Abstract 295 | PDF Downloads 103



test rig, bearing diagnostics, Motor Current Signature Analysis

Alpaydin, E. (2014). Introduction to machine learning (Third edition ed.).

Aye, S. A., Heyns, P. S., & Thiart, C. J. (2014). A review of slow speed bearing diagnostics and prognostics. In International journal of engineering science and technology (Vol. Vol. 6 No.10, pp. 726–739).

Bellini, A., Immovilli, F., Rubini, R., & Tassoni, C. (2008). Diagnosis of Bearing Faults in Induction Machines by Vibration or Current Signals: A Critical Comparison. Industry Applications Society Annual Meeting, 2008. IAS’08. IEEE(46), 1–8.

Blo ̈dt, 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), 1813–1822. DOI: 10.1109/TIE.2008.917108

Boyanton, H. E., & Hodges, G. (2002). Bearing fluting: the results of a long-term investigation into bearing fluting on AC motors, DC motors, and Rolls on paper machines. IEEE Industry Applications Magazine, 2002(Sep/Oct issue), 53–57.

Chirico, A. J., Kolodziej, J. R., & Hall, L. (2012). A data driven frequency based feature extraction and classification method for ema fault detection and isolation. In Asme 2012 5th annual dynamic systems and control conference joint with the jsme 2012 11th motion and vibration conference (pp. 751–760).

Delgado, M., Garcia, A., & Ortega, J. A. (2011). Evaluation of feature calculation methods for electromechanical system diagnosis. In 8th IEEE international symposium on diagnostics for electric machines, power electronics and drives - (sdemped 2011) (pp. 495–502). DOI: 10.1109/DEMPED.2011.6063669.

Janjarasjitt, S., Ocak, H., & Loparo, K. (2008). Bearing condition diagnosis and prognosis using applied nonlinear dynamical analysis of machine vibration signal. Journal of Sound and Vibration, 317(1-2), 112–126. DOI: 10.1016/j.jsv.2008.02.051.

Jardine, A. K., Lin, D., & Banjevic, D. (2006). A review on machinery diagnostics and prognostics implement- ing condition-based maintenance. Mechanical Sys- tems and Signal Processing, 20(7), 1483–1510. DOI: 10.1016/j.ymssp.2005.09.012

Jolliffe, I. T. (2002). Principal component analysis (2nd ed.). New York: Springer.

Kim, & Parlos. (2002). Induction motor fault diagnosis based on neuropredictors and wavelet signal processing - Mechatronics, IEEE/ASME Transactions on. IEEE Transactions on Industrial Electronics.

Malhi, A., & Gao, R. X. (2004). PCA-based feature selection scheme for machine defect classification. IEEE Transactions on Instrumentation and Measurement, 53(6),
1517–1525. DOI: 10.1109/TIM.2004.834070

McFadden, P. D., & Smith, J. D. (1985). The vibration produced by multiple point defects in a rolling element bearing. Journal of Sound and Vibration, 98(2), 263–273. DOI: 10.1016/0022-460X(85)90390-6 .

Raison, B., Rostaing, G., Butscher, O., & Maroni, C.-S. (2002). Investigations of algorithms for bearing fault detection in induction drives - Industrial Electronics Society, IEEE 2002 28th Annual Conference of the. IECON 02 [Industrial Electronics Society, IEEE 2002 28th Annual Conference of the] (Volume:2 )(2), 1696–1701.

Stack, J. R., Habetler, T. G., & Harley, R. G. (2005). Experimentally Generating Faults in Rolling Element Bearings Via Shaft Current. IEEE Transactions on Industry Applications, 41(1), 25–29. DOI: 10.1109/TIA.2004.840966
Technical Research Papers