基于萤火虫群算法优化最小二乘支持向量回归机的滚动轴承故障诊断

徐 强;刘永前;田 德;张晋华;龙 泉

振动与冲击 ›› 2014, Vol. 33 ›› Issue (10) : 8-12.

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PDF(818 KB)
振动与冲击 ›› 2014, Vol. 33 ›› Issue (10) : 8-12.
论文

基于萤火虫群算法优化最小二乘支持向量回归机的滚动轴承故障诊断

  • 徐 强1,刘永前1,田 德1,张晋华1,龙 泉2
作者信息 +

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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文章历史 +

摘要

由于滚动轴承故障诊断可提高设备利用率、降低运行及维护成本,而最小二乘支持向量回归机为有效的故障诊断方法。为解决其参数选取受限于主观经验问题,将萤火虫群算法用于惩罚系数 与核参数 寻优,提出基于萤火虫群算法优化最小二乘支持向量回归机的滚动轴承故障诊断方法。实验结果表明,该方法能对滚动轴承故障位置及程度进行准确诊断,与常规最小二乘支持向量回归机、BP神经网络相比精度更高,由此验证该方法的可靠性。

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.

关键词

滚动轴承 / 故障诊断 / 最小二乘支持向量回归机 / 萤火虫群算法

Key words

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

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导出引用
徐 强;刘永前;田 德;张晋华;龙 泉. 基于萤火虫群算法优化最小二乘支持向量回归机的滚动轴承故障诊断[J]. 振动与冲击, 2014, 33(10): 8-12
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

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