Paper Title:
Application of Ant Colony Algorithm in Fault Diagnosis of Roller Bearing
  Abstract

This paper presents a condition diagnosis method for a roller bearing using the ant colony optimization (ACO). The symptom parameters in frequency domain are considered for reflecting the feature of vibration signals measured under different states. The states identification for machinery diagnosis is converted to clustering problem of different states. The distance-based diagnosis method, which distinguishes the machinery states by comparing the distance, is proposed using the ACO clustering algorithm and utilized to detect faults and recognize fault types. The analysis results demonstrate that the proposed method can recognize the faults types effectively.

  Info
Periodical
Advanced Materials Research (Volumes 291-294)
Chapter
Vibration, Noise Analysis and Control
Edited by
Yungang Li, Pengcheng Wang, Liqun Ai, Xiaoming Sang and Jinglong Bu
Pages
1957-1960
DOI
10.4028/www.scientific.net/AMR.291-294.1957
Citation
L. Y. Song, H. Q. Wang, J. Pan, J. J. Gao, K. Li, "Application of Ant Colony Algorithm in Fault Diagnosis of Roller Bearing", Advanced Materials Research, Vols. 291-294, pp. 1957-1960, 2011
Online since
July 2011
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Price
$32.00
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