基于改进天鹰优化算法优化LSTM的滚动轴承故障诊断方法

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振动与冲击 ›› 2024, Vol. 43 ›› Issue (23) : 144-154.

论文

基于改进天鹰优化算法优化LSTM的滚动轴承故障诊断方法

王妍1,王新发1,王延峰1，顾晓光2，孙军伟1

作者信息 +

A rolling bearing fault diagnosis method based on improved Aquila optimization algorithm to optimize LSTM

WANG Yan1, WANG Xinfa1, WANG Yanfeng1, GU Xiaoguang2, SUN Junwei1

Author information +

文章历史 +

摘要

针对天鹰优化（Aquila optimizer，AO）算法容易陷入局部最优，长短时记忆（long short-term memory，LSTM）神经网络容易受参数影响的问题，提出了一种基于改进天鹰(Improved AO，IAO)算法优化LSTM神经网络的模型，并将其应用于滚动轴承的故障诊断中。首先，引入超立方策略优化了种群初始质量，设计自适应螺旋策略平衡了AO算法的全局搜索和局部搜索能力，并通过利用高斯变异策略增强了AO算法跳出局部最优的能力。然后，将所提IAO算法对LSTM的权值和阈值进行优化，构建了基于IAO-LSTM网络的滚动轴承故障诊断模型。最后，凯斯西储大学(Case Western Reserve University，CWRU)轴承数据集和帕德伯恩大学(Paderborn University，PU)轴承数据集的实验结果表明：与其他故障诊断模型相比，IAO优化后的LSTM模型的分类准确率更高，能有效识别滚动轴承的各种故障类型。

Abstract

Aiming at the problem that Aquila optimizer (AO) is prone to local optimization and the accuracy of long short-term memory (LSTM) network is affected by parameters, a model of LSTM neural network based on improved Aquila optimizer （IAO） algorithm is proposed and applied to the fault diagnosis of rolling bearings. Firstly, the hypercube strategy is introduced to optimize the initial mass of the population, and the adaptive spiral strategy is designed to balance the global search ability and local search ability of AO algorithm, and the ability of AO algorithm to jump out of the local optimal is enhanced by using Gaussian mutation strategy. Then, the weights and thresholds of LSTM are optimized by the proposed IAO algorithm, and a rolling bearing fault diagnosis model based on IAO-LSTM network is constructed. Finally, the experimental results of Case Western Reserve University (CWRU) bearing data set and Paderborn University (PU) bearing data set show that compared with other fault diagnosis models, the IAO-optimized LSTM model has higher classification accuracy and can effectively identify various fault types of rolling bearings.

导出引用

王妍1, 王新发1, 王延峰1, 顾晓光2, 孙军伟1. 基于改进天鹰优化算法优化LSTM的滚动轴承故障诊断方法[J]. 振动与冲击, 2024, 43(23): 144-154

WANG Yan1, WANG Xinfa1, WANG Yanfeng1, GU Xiaoguang2, SUN Junwei1. A rolling bearing fault diagnosis method based on improved Aquila optimization algorithm to optimize LSTM[J]. Journal of Vibration and Shock, 2024, 43(23): 144-154

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