基于细菌觅食决策和深度置信网络的滚动轴承故障诊断研究

陶洁1,2,刘义伦1,3,杨大炼1,4,宾光富4

振动与冲击 ›› 2017, Vol. 36 ›› Issue (23) : 68-74.

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振动与冲击 ›› 2017, Vol. 36 ›› Issue (23) : 68-74.
论文

基于细菌觅食决策和深度置信网络的滚动轴承故障诊断研究

  • 陶洁1,2刘义伦1,3杨大炼1,4宾光富4
作者信息 +

Rolling bearing fault diagnosis based on bacterial foraging algorithm and deep belief network

  • Tao Jie1,2Liu Yilun1,3Yang Dalian1,4Bin Guangfu4
Author information +
文章历史 +

摘要

在利用深度置信网络进行滚动轴承故障诊断时,网络结构的设置不仅影响诊断的结果,还影响计算效率。为提高滚动轴承故障诊断的准确率,本文提出基于细菌觅食决策和深度置信网络的滚动轴承故障诊断方法。该方法利用采集的样本数据对深度置信网络进行训练,以构造细菌觅食决策算法的适应度函数,通过计算各个细菌的适应度来衡量模型的优劣。由于细菌觅食决策算法具有并行搜索能力,能有效选取深度置信网络各隐节点数、学习率、动量等参数,生成合适的分类器提高滚动轴承故障诊断的准确率。实验中,与SVM、BPNN、KNN等方法做比较,本文所提方法对滚动轴承故障诊断的准确率达到97.83%,能更加高效、准确的识别滚动轴承故障。
 

Abstract

When studying rolling bearing fault diagnosis with the deep belief network method, parameters in the deep belief network have a great effect on fault diagnosis results and it is hard to obtain suitable parameters. Here, the fault diagnosis method based on the bacterial foraging algorithm and the deep belief network was proposed to improve the correct rate of bearing fault diagnosis. The parallel search ability of the bacterial foraging algorithm was adopted to effectively choose the number of hidden layer, the number of hidden nodes, the learning rate in a deep belief network. The deep belief network’s training data classification error was used to calculate the fitness function of the bacterial foraging algorithm to build an appropriate fault classifier and finish rolling bearing fault diagnosis. The test results showed that the correct rate of the proposed method for rolling bearing fault diagnosis reaches 98.5%; compared with BPNN, SVM and KNN methods, the proposed method can more stably and more accurately identify rolling bearing faults.


关键词

深度置信网络 / 细菌觅食决策算法 / 滚动轴承 / 故障诊断

Key words

deep belief network / bacterial foraging algorithm / rolling bearings / fault diagnosis

引用本文

导出引用
陶洁1,2,刘义伦1,3,杨大炼1,4,宾光富4 . 基于细菌觅食决策和深度置信网络的滚动轴承故障诊断研究[J]. 振动与冲击, 2017, 36(23): 68-74
Tao Jie1,2Liu Yilun1,3Yang Dalian1,4Bin Guangfu4. Rolling bearing fault diagnosis based on bacterial foraging algorithm and deep belief network[J]. Journal of Vibration and Shock, 2017, 36(23): 68-74

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