Multichannel Vibration Fault Diagnosis for Rolling Bearings Based on QPCA and SVM

Abstract:

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A new method had been proposed in this paper of fault diagnosis for rolling bearings based on multichannel vibration signals and QPCA-SVM-based method. The vibration signals were obtained by some multi-sensors with three channels X, Y, Z, that were orthogonal axes. The three orthogonal axes signals were constructed a pure quaternion sequences as samples for processing. The pure quaternion sequences data set was processed by quaternion principle components analysis (QPCA) for feature extraction, and then combined with pattern recognition tools support vector machine (SVM) for classifying some faults patterns. The experimental results indicated its efficiency, and it provided a method for fault diagnosis on multichannel vibration signals.

Info:

Periodical:

Advanced Materials Research (Volumes 199-200)

Edited by:

Jianmin Zeng, Zhengyi Jiang, Taosen Li, Daoguo Yang and Yun-Hae Kim

Pages:

927-930

DOI:

10.4028/www.scientific.net/AMR.199-200.927

Citation:

Z. F. Li and J. G. Li, "Multichannel Vibration Fault Diagnosis for Rolling Bearings Based on QPCA and SVM", Advanced Materials Research, Vols. 199-200, pp. 927-930, 2011

Online since:

February 2011

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$35.00

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