基于改进HHO-LSTM的滚动轴承故障诊断研究

邵良杉; 朱思佳

doi:10.16579/j.issn.1001.9669.2024.01.003

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基于改进HHO-LSTM的滚动轴承故障诊断研究

振动·噪声·监测·诊断 | 更新时间：2024-04-18

- 基于改进HHO-LSTM的滚动轴承故障诊断研究
- RESEARCH ON ROLLING BEARING FAULT DIAGNOSIS BASED ON IMPROVED HHO-LSTM
- 机械强度 2024年46卷第1期页码：17-23
- 作者机构：
  
  1. 辽宁工程技术大学系统工程研究所
  2. 辽宁工程技术大学工商管理学院
- 作者简介：
- 基金信息：
- DOI：10.16579/j.issn.1001.9669.2024.01.003
  中图分类号： TP277;TH133.33
- 纸质出版日期：2024-02-15，
- 稿件说明：
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邵良杉, 朱思佳. 基于改进HHO-LSTM的滚动轴承故障诊断研究[J]. 机械强度, 2024,46(1):17-23.

SHAO LiangShan, ZHU SiJia. RESEARCH ON ROLLING BEARING FAULT DIAGNOSIS BASED ON IMPROVED HHO-LSTM[J]. Journal of Mechanical Strength , 2024,46(1):17-23.
邵良杉, 朱思佳. 基于改进HHO-LSTM的滚动轴承故障诊断研究[J]. 机械强度, 2024,46(1):17-23. DOI： 10.16579/j.issn.1001.9669.2024.01.003.

SHAO LiangShan, ZHU SiJia. RESEARCH ON ROLLING BEARING FAULT DIAGNOSIS BASED ON IMPROVED HHO-LSTM[J]. Journal of Mechanical Strength , 2024,46(1):17-23. DOI： 10.16579/j.issn.1001.9669.2024.01.003.

摘要

为了解决滚动轴承故障诊断问题，提出一种将改进哈里斯鹰优化（Harris Hawks Optimization，HHO）算法和长短时记忆（Long Short-Term Memory，LSTM）网络相融合的智能诊断模型IHHO-LSTM。HHO 算法在求解过程中容易陷入局部最优、收敛缓慢，基于这些问题引入 Cauchy分布函数和模拟退火（Simulated Annealing，SA）算法，拓展全局搜索的广泛性，避免陷入局部最优。运用改进的HHO 算法快速确定LSTM 模型的最优超参数值，从而提高时序诊断精度。利用凯斯西储大学滚动轴承实验数据进行故障诊断实验，结果表明，IHHO-LSTM 模型能够实现对滚动轴承的特征提取和故障诊断，模型准确率高达近97%。

Abstract

In order to solve the problem of rolling bearing fault diagnosis

an intelligent diagnosis model IHHO-LSTM was proposed

which combined the improved Harris hawks optimization (HHO) algorithm with long short-term memory (LSTM) network. HHO algorithm was prone to fall into local optimum and slow convergence in the solution process. Based on thesc problems

Cauchy distribution function and simulated annealing (SA) algorithm were introduced to expand the universality of global search and avoid falling into local optimization. The improved HHO was used to quickly determine the optimal super parameter values of LSTM model

so as to improve the accuracy of time series diagnosis. The rolling bearing experimental data of Case Western Reserve University were used for fault diagnosis experiments. The results show that IHHO-LSTM model can realize the feature extraction and fault diagnosis of rolling bearing

and the accuracy of the model is nearly 97%.

关键词

滚动轴承深度学习哈里斯鹰优化算法长短时记忆网络工业大数据故障诊断

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