Bearing Fault Diagnosis based on ACSBP Algorithm

Cheng Jiatang; Xiong Yan

doi:10.16578/j.issn.1004.2539.2017.09.035

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Bearing Fault Diagnosis based on ACSBP Algorithm

更新时间：2022-10-20

- Bearing Fault Diagnosis based on ACSBP Algorithm
- Vol. 41, Issue 9, Pages: 175-178(2017)
- 作者机构：
  
  1. 红河学院工学院
- 作者简介：
- 基金信息：
- DOI：10.16578/j.issn.1004.2539.2017.09.035
  CLC：
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[1]程加堂,熊燕.应用ACSBP算法的轴承故障诊断[J].机械传动,2017,41(09):175-178.

Cheng Jiatang, Xiong Yan. Bearing Fault Diagnosis based on ACSBP Algorithm. [J]. 41(9):175-178(2017)
[1]程加堂,熊燕.应用ACSBP算法的轴承故障诊断[J].机械传动,2017,41(09):175-178. DOI： 10.16578/j.issn.1004.2539.2017.09.035.

Cheng Jiatang, Xiong Yan. Bearing Fault Diagnosis based on ACSBP Algorithm. [J]. 41(9):175-178(2017) DOI： 10.16578/j.issn.1004.2539.2017.09.035.

摘要

为进行轴承故障位置和损失程度的有效识别,提出一种基于自适应布谷鸟搜索算法结合BP神经网络（ACSBP）的故障诊断模型。在ACS算法中,取消了Levy飞行策略,降低了算法搜索过程的随机性。同时,步长的更新由适应度函数值决定,无需进行参数初始化,以体现面向不同优化问题的普适性。此外,发现概率采用动态调整方式,提高了ACS算法的寻优精度以及收敛速度。以此为基础,实现了轴承故障诊断模型的构建。仿真实例表明,与布谷鸟搜索算法结合BPNN（CSBP）和粒子群算法结合BPNN（PSOBP）相比,ACSBP模型具有较强的容错性,可有效提高轴承故障诊断的精度。

Abstract

To effectively identify the bearing fault position and the degree of damage,a model of adaptive cuckoo search algorithm combined with BP neural network（ ACSBP） is proposed. In the ACS algorithm,the Levy flight strategy is eliminated to reduce the randomness of the search process. Simultaneously,in order to reflect the universality for different optimization problems,the updating of step size is implemented by the fitness function value,which did not need to be initialized. Furthermore,the dynamic adjustment method of discovery probability is adopted to improve the optimization accuracy and convergence rate. Based on these approaches,the model of bearing fault diagnosis is established. The diagnostic results show that the ACSBP algorithm has stronger fault tolerance compared with CSBP and PSOBP models,and can effectively improve the accuracy of bearing fault diagnosis.

关键词

轴承故障诊断布谷鸟搜索自适应神经网络

Keywords

BearingFault diagnosisCuckoo searchAdaptiveNeural network

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