基于INGO-CSA-LSTMN的变转速齿轮故障智能识别方法

陈向民; 李博; 张亢; 姚鹏; 李泳辉; 雷瀚霖

doi:10.19805/j.cnki.jcspe.2025.240243

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基于INGO-CSA-LSTMN的变转速齿轮故障智能识别方法

数字化与智能化 | 更新时间：2026-10-10

- 基于INGO-CSA-LSTMN的变转速齿轮故障智能识别方法
- Intelligent Fault Identification Method of Variable Speed Gear Based on INGO-CSA-LSTMN
- 动力工程学报 2025年45卷第6期页码：913-923
- 作者机构：
  
  长沙理工大学能源与动力工程学院,湖南,长沙,410114
- 作者简介：
  
  陈向民(1984—),男,湖南祁东人,讲师,博士,研究方向为机械设备状态监测与故障诊断技术,E-mail:cxiangmin3377@foxmail.com
- 基金信息：
  
  湖南省自然科学基金资助项目(2018JJ3541);湖南省教育厅资助项目(20B019,21B0347)
- DOI：10.19805/j.cnki.jcspe.2025.240243
  中图分类号：
- 网络首发：2025-06-16，
  
  纸质出版：2025
- 稿件说明：
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陈向民,李博,张亢,姚鹏,李泳辉,雷瀚霖. 基于INGO-CSA-LSTMN的变转速齿轮故障智能识别方法动力工程学报, 2025, 45(6): 913-923 https://doi.

org/10.19805/j.cnki.jcspe.2025.240243
陈向民,李博,张亢,姚鹏,李泳辉,雷瀚霖. 基于INGO-CSA-LSTMN的变转速齿轮故障智能识别方法动力工程学报, 2025, 45(6): 913-923 https://doi. DOI： 10.19805/j.cnki.jcspe.2025.240243.

org/10.19805/j.cnki.jcspe.2025.240243 DOI：

摘要

为提高齿轮在变转速工况下的故障识别效率和准确率

提出了一种基于改进北方苍鹰优化(improved northern goshawk optimization

INGO)算法优化卷积自注意力长短期记忆网络(convolutional self-attention long short-term memory network

CSA-LSTMN)的变转速齿轮故障智能识别方法

即INGO-CSA-LSTMN。针对传统北方苍鹰优化算法训练时间过长和容易陷入局部最优的问题

引入正弦脉冲调制混沌映射和随机莱维飞行策略

提出一种INGO算法

并将其应用于所构建的CSA-LSTMN模型的关键参数寻优

以提高该模型的稳定性及训练效率。通过测试函数的检验表明:INGO算法具有更快的收敛速度

可更准确地找到最优解。通过2种不同试验台齿轮数据集的分析表明:相较于其他常用网络模型

INGO-CSA-LSTMN模型对于不同工况下的齿轮故障具有更高的识别精度

准确率均在99.9%以上。

Abstract

In order to improve the efficiency and accuracy of gear fault identification under variable speed conditions

an intelligent fault identification method of variable speed gear

namely INGO-CSA-LSTMN

was proposed based on improved northern goshawk optimization (INGO) algorithm to optimize convolutional self-attention long short-term memory network (CSA-LSTMN). In response to the problems that the traditional northern goshawk optimization algorithm taking too long training time and being easy to fall into local optimization

an INGO algorithm was proposed by introducing sinusoidal pulse modulated chaotic map and random Levy flight strategy

and it was applied to optimize the key parameters of the CSA-LSTMN model to improve the stability and training efficiency of the model. The validation through test functions demonstrates that the INGO algorithm has a faster convergence speed and can find the optimal solution more accurately. The analysis of gear datasets from two different experimental rigs reveals that compared to other commonly used network models

the INGO-CSA-LSTMN model has higher recognition accuracy for gear faults under different operating conditions

with the accuracy rates exceeding 99.9%.

关键词

Keywords

references

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