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Early detection of bearing damage by means of decision trees

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This paper presents a procedure for early detection of rolling bearing damages on the basis of vibration measurements. First, an envelope analysis is performed on bandpass filtered signals. For each frequency range, a feature indicator is defined as sum of spectral lines. These features are passed through a principal component model to generate a single variable, which allows tracking change in the bearing health. Thresholds and rules for early detection are learned thanks to decision trees. Experimental results demonstrate that this procedure enables early detection of bearing defects.
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Bibliografia
  • [1] Gebraeel N., Lawley M., Liu C.R., Parmeshwaran V., “Residual life predictions from vibration-based degradation signals: A neural network approach”, IEEE Transactions on Industrial Electronics, vol. 51, issue 3, 2004, pp. 694-700.
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  • [3] Tandon N., Choudhury A., “A Review of Vibration and Acoustic Measurement Methods for the Detection of Defects in Rolling Element Bearing”,Tribology International, vol. 32, no. 8, 1999, pp. 469-480.
  • [4] Li Y., Zhang C., Kurfess T. Danyluk S., Liang S., “Adaptative Prognostics for Rolling Element Bearing Condition”,Mechanical Systems and Signal Processing, vol. 13, no. 1, 1999, pp. 103-113.
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  • [7] McGregor J., Kourti T., Nomikos P., “Analysis, monitoring and fault diagnosis of industrial processes usind multivariate statistical projection methods” In:Proceedings of IFAC Congress. San Francisco 1996. vol. M, 1996, pp. 145-150.
  • [8] Mohamed-Faouzi H.,Détection et localisation de défaut par analyse en composantes principales. Institut National Polytechnique de Lorraine. 2003. Ph.D thesis (in French).
  • [9] Iserman R.,Fault-diagnosis System. An introduction from fault detection to fault tolerance. s.l.: Springer, 2006.
  • [10] Sugumaran V., Ramachandran K., “Automatic rule learning using decision tree for fuzzy classifier in fault diagnosis of roller bearing”,Mechanical Systems and Signal Processing, vol. 21, no. 5, 2007, pp. 2237-2247.
  • [11] Chen Y., “Impending failure detection for a discrete process”,Mechanical Systems and Signal Processin, vol. 7, no. 2, 1993, pp. 121-132.
  • [12] Chen Z.,Computational Intelligence for Decision Support, New York: CRC Press LLC, 2000.
  • [13] Quilan R., C4.5: Programs for Machine Learning, San Mateo: Morgan Kaufmann Publishers, 1993.
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bwmeta1.element.baztech-article-BUJ5-0025-0009
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