A Signal Processing Technique for Predictive Fault Detection based on Vibration Data

Ye Won Song, Hong Seong Lee, Hoonseok Park, Young Jin Kim, Jae-Yoon Jung


Many problems in rotating machinery such as aircraft engines, wind turbines and motors are caused by bearing defects. The abnormalities of the bearing can be detected by analyzing signal data such as vibration or noise, proper pre-processing through a few signal processing techniques is required to analyze their frequencies. In this paper, we introduce the condition monitoring method for diagnosing the failure of the rotating machines by analyzing the vibration signal of the bearing. From the collected signal data, the normal states are trained, and then normal or abnormal state data are classified based on the trained normal state. For preprocessing, a Hamming window is applied to eliminate leakage generated in this process, and the cepstrum analysis is performed to obtain the original signal of the signal data, called the formant. From the vibration data of the IMS bearing dataset, we have extracted 6 statistic indicators using the cepstral coefficients and showed that the application of the Mahalanobis distance classifier can monitor the bearing status and detect the failure in advance.

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Alejandro, D., Mejia, T., Medjaher, K., Zerhouni, N., and Tripot, G., “A Data- Driven Failure Prognostics Method Based on Mixture of Gaussians Hidden Markov Models,” IEEE Transactions on Reliability, Vol. 61, No. 2, pp. 491-501, 2012.

Jeon, B. C., Jung, J. H., Youn, B. D., Kim, Y. W., and Bae, Y. C., “Evaluation of Datum Unit for Diagnostics of Journal-Bearing Systems,” Transactions of the Korean Society of Mechanical Engineers, Vol. 39, No. 8, pp. 801-806, 2015.

Lee, S. H. and Lim, G., “Performance Comparison of Mahalanobis-Taguchi System and Logistic Regression-A Case Study,” Journal of the Korean Institute of Industrial Engineers, Vol. 39, No. 5, pp. 393-402, 2013.

Lee, S. H. and Yoon, B. D., “Industry 4.0 and direction of prognostics and health management (PHM),” Vol. 25, No. 2-4, pp. 351-357, 2015.

Lim, H. J., Kim, S. D., Jung, S. H., Hong, S. W., Oh, G. H., and Park, J. H., “Analysis of Vibration Signal for Failure Diagnosis of Rotating Devices,” Proceedings of Korean Society of Precision Engineering Spring Conference, pp. 301-307, 1995.

Paik, Y. S., Mok, Y. J., Lee, S. J., and Lee, Y. B., “Data Processing of Vibration Records and Its Application,” Journal of the Korean Society of Civil Engineers, Vol. 16, Vol. 2-4, No. III-4, pp. 351-358, 1995.

Park, C. S. and Youn, D. J., “A Noise Reduction Signal Processing for Online Monitoring: Minimum Variance Cepstrum,” Journal of the Korean Society for Nondestructive Testing, Vol. 31, No. 6, pp. 671-676, 2011.

Park, S. G., Park, W. S., Jung, J. E., Lee, Y. Y., and Oh, J. E., “A Fault Diagnosis on the Rotating Machinery Using Mahalanobis Distance,” Transactions of the Korean Society of Mechanical Engineers, Vol. 32, No. 7, pp. 556-560, 2008.

Yang, J. H. and Kwon, O. K., “Model-based Fault Diagnosis Applied to Vibration Data,” Journal of Institute of Control, Robotics and Systems, Vol. 18, No. 12, pp. 1090-1095, 2012.


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