Application of Ant Colony Algorithm in Fault Diagnosis of Roller Bearing

Liu Yang Song; Hua Qing Wang; Jia Pan; Jin Ji Gao; Ke Li

doi:10.4028/www.scientific.net/AMR.291-294.1957

Paper Titles

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 291-294Application of Ant Colony Algorithm in Fault...

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.

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Materials Processing Technology, AEMT2011

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Periodical:

Advanced Materials Research (Volumes 291-294)

Pages:

1957-1960

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.291-294.1957

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Online since:

July 2011

Authors:

Liu Yang Song, Hua Qing Wang, Jia Pan, Jin Ji Gao, Ke Li

Keywords:

Ant Colony Optimization (ACO), Clustering, Fault Diagnosis, Roller Bearings

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References

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