Fault diagnosis of rolling bearings using least square support vector regression optimized by glowworm swarm optimization algorithm

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Journal of Vibration and Shock ›› 2014, Vol. 33 ›› Issue (10) : 8-12.

论文

Fault diagnosis of rolling bearings using least square support vector regression optimized by glowworm swarm optimization algorithm

XU Qiang1，LIU Yong-qian1，TIAN De1，ZHANG Jin-hua1，LONG Quan2

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Abstract

Fault diagnosis of rolling bearings is the key to improve equipment availability and reduce operation and maintenance cost. Least square support vector regression (LSSVR) is a kind of effective method for fault diagnosis, and glowworm swarm optimization (GSO) algorithm is applied to obtain the optimal combination of penalty parameter and kernel parameter which are always restricted by subjective experience. A rolling bearing fault diagnosis method based on LSSVR optimized by GSO was proposed. Experiments show that the presented method can precisely diagnose both fault location and fault severity of rolling bearings, with higher accuracy compared with normal LSSVR and BP neural network, which has validated the reliability of the proposed method.

Key words

rolling bearings / fault diagnosis / least square support vector regression / glowworm swarm optimization algorithm

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XU Qiang;LIU Yong-qian;TIAN De;ZHANG Jin-hua;LONG Quan. Fault diagnosis of rolling bearings using least square support vector regression optimized by glowworm swarm optimization algorithm[J]. Journal of Vibration and Shock, 2014, 33(10): 8-12