Application of Mayfly Algorithm Optimized Dual-Channel Network in Gearbox Fault Diagnosis

YU Ning; WEI Chenqian; TIAN Liyong; ZHAO Jianjun; YU Xiaohan

doi:10.7652/xjtuxb202505021

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Application of Mayfly Algorithm Optimized Dual-Channel Network in Gearbox Fault Diagnosis

更新时间：2025-08-26

- Application of Mayfly Algorithm Optimized Dual-Channel Network in Gearbox Fault Diagnosis
- Journal of Xi’an Jiaotong University Vol. 59, Issue 5, Pages: 217-228(2025)
- 作者机构：
  
  辽宁工程技术大学机械工程学院， 123000，辽宁阜新
- 作者简介：
- 基金信息：
- DOI：10.7652/xjtuxb202505021
  CLC： TH133.3
- Received：21 September 2024，
  
  Online First：10 January 2025，
  
  Published：10 May 2025
- 稿件说明：
移动端阅览
YU Ning, WEI Chenqian, TIAN Liyong, et al. Application of Mayfly Algorithm Optimized Dual-Channel Network in Gearbox Fault Diagnosis[J]. Journal of Xi’an Jiaotong University, 2025, 59(5): 217-228.
DOI：

YU Ning, WEI Chenqian, TIAN Liyong, et al. Application of Mayfly Algorithm Optimized Dual-Channel Network in Gearbox Fault Diagnosis[J]. Journal of Xi’an Jiaotong University, 2025, 59(5): 217-228. DOI： 10.7652/xjtuxb202505021.

摘要

为了有效提取齿轮箱中齿轮和滚动轴承的故障信号特征，并克服深度学习模型超参数选取依赖人工经验的局限性，提高故障诊断的准确性和稳定性，提出了一种基于蜉蝣算法（MA）优化的双通道神经网络故障诊断模型。该模型采用一维时序输入的门控循环单元（GRU）和二维图像输入的卷积神经网络（CNN）构建双通道并行架构，并引入自适应批标准化（AdaBN）算法。利用MA的全局优化能力，以CNN-GRU的诊断精度为优化目标，自适应调整模型超参数。将蜉蝣算法优化效果与粒子群算法和遗传算法进行了对比验证，以评估其在模型参数优化方面的有效性。基于东南大学齿轮箱数据集和凯斯西储大学轴承数据集的实验结果表明：该模型能够有效提取振动信号特征，其故障识别精度与稳定性均优于典型深度学习模型，并展现出较强的鲁棒性。在稳态工况下，优化后的CNN-GRU（MA-CNN-GRU）模型在各数据集上的识别精度显著提高；在噪声工况下，MA优化的CNN-GRU模型表现出优异的抗噪性；在变负载工况下，结合AdaBN算法的MA-CNN-GRU模型实现了最高的平均识别精度。所提模型能够高效、准确地检测齿轮箱故障，为机械设备的维护和稳定运行提供了重要的参考价值。

Abstract

In order to effectively extract the fault signal characteristics of gears and rolling bearings in gearboxes

overcome the limitation of deep learning model hyperparameter selection relying on human experience

and improve the accuracy and stability of fault diagnosis

a two-channel neural network fault diagnosis model based on Mayfly algorithm (MA) optimization was proposed. The model uses a one-dimensional timing input gated recurrent unit (GRU) and a two-dimensional image input convolutional neural network (CNN) to construct a dual-channel parallel architecture

and introduces an adaptive batch normalization (AdaBN) algorithm. By using the global optimization ability of MA

the hyperparameters of the model are adaptively adjusted with the diagnostic accuracy of CNN-GRU as the optimization objective. At the same time

MA optimization effort is compared with particle swarm optimization and genetic algorithm to evaluate its effectiveness in model parameter optimization. The experimental results based on the gearbox data set of Southeast University and the bearing data set of Case Western Reserve University show that the model can effectively extract the characteristics of vibration signals

and its fault recognition accuracy and stability are better than typical deep learning models

and it shows strong robustness. Under steady-state conditions

the recognition accuracy of the optimized CNN-GRU model on each data set is significantly improved. Under noise conditions

the MA-optimized CNN-GRU (MA-CNN-GRU) model exhibits excellent noise immunity. Under variable load conditions

the MA-CNN-GRU model combined with the AdaBN algorithm achieves the highest average recognition accuracy. In summary

the proposed model can detect gearbox faults efficiently and accurately

and it can provide an important reference for the maintenance and stable operation of mechanical equipment.

关键词

Keywords

references

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