基于融合时频变换的MobileNetV3-AHFF和MS-HNNE模型的行星齿轮箱故障诊断方法

戚晓利; 王志文; 杨文好; 崔德海; 赵方祥; 王兆俊

doi:10.13224/j.cnki.jasp.20240702

基于融合时频变换的MobileNetV3-AHFF和MS-HNNE模型的行星齿轮箱故障诊断方法

doi: 10.13224/j.cnki.jasp.20240702

安徽工业大学机械工程学院，安徽马鞍山 243032

基金项目: 国家自然科学基金面上项目（51975004）

详细信息

作者简介:
戚晓利（1975-），男，副教授、硕士生导师，博士，主要从事故障诊断和撞击动力学等方面研究

通讯作者:
王志文（1998-），男，硕士生，主要研究方向为机械系统状态监控和故障诊断等。E-mail：1254956827@qq.com

中图分类号: V232.8；TH165⁺.3；TN911.7
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出版历程
- 收稿日期: 2024-10-15
- 网络出版日期: 2025-08-19

Fault diagnosis method of planetary gearbox based on MobileNetV3-AHFF and MS-HNNE models with fused time-frequency transformations

School of Mechanical Engineering，Anhui University of Technology，Maanshan Anhui 243032，China

摘要

摘要:
针对现有特定时频变换方法在提取振动信号中的复杂动态特征时存在一定局限性，以及传统MobileNetV3网络中存在的通道注意力机制特征选择偏差与池化层策略设计不当导致的信息丢失等问题，提出了一种基于融合时频变换的MobileNetV3-AHFF和MS-HNNE（Mahalanobis distance hierarchical nearest neighbor graph embedding for efficient dimensionality reduction）的行星齿轮箱故障诊断方法。通过集成短时傅里叶变换、连续小波变换和Chirplet变换图像编码技术，将行星齿轮箱的振动信号转化为多维时频图像，进而融合这些时频特征，构建出全面表征信号特性的特征图像。通过设计自适应分层特征融合（AHFF）模块，提高深度学习网络的表征能力。采用监督型MS-HNNE算法取代MobileNetV3全连接层前的池化层，在维度约简的过程中保留数据的内在结构和关键信息。使用Softmax函数完成低维数据的分类任务。DDS（drivetrain diagnostics simulator）和东南大学行星齿轮箱故障诊断实验结果表明：该方法相较于现有故障诊断模型，不仅诊断准确率显著提高，而且模型泛化能力也得到了增强，其最高诊断准确率达到99.9%，具有一定的应用前景。
- 故障诊断 /
- 行星齿轮箱 /
- MobileNetV3 /
- 时频变换 /
- 分层最近邻图嵌入的有效降维算法 /
- 特征融合
Abstract:
To address the limitations of existing specific time-frequency transformation methods in extracting complex dynamic features from vibration signals, as well as the feature selection bias in the channel attention mechanism and the improper pooling strategy design in the traditional MobileNetV3 network leading to information loss, a planetary gearbox fault diagnosis method based on MobileNetV3-AHFF and MS-HNNE (Mahalanobis distance hierarchical nearest neighbor graph embedding for efficient dimensionality reduction) with fused time-frequency transformations was proposed. By integrating short-time Fourier transform, continuous wavelet transform, and chirplet transform image coding techniques, the vibration signals of the planetary gearbox were transformed into multidimensional time-frequency images, which were then fused to construct a comprehensive feature image representing the signal characteristics. An adaptive hierarchical feature fusion (AHFF) module was designed to enhance the representational ability of the deep learning network. The supervised MS-HNNE algorithm was used to replace the pooling layer before the fully connected layer of MobileNetV3. During the process of dimensionality reduction, the internal structure and key information of the data were retained. The Softmax function was used to complete the classification task for the low-dimensional data. Compared with the existing fault diagnosis models, experimental results from the DDS (drivetrain diagnostics simulator) and Southeast University planetary gearbox fault diagnosis showed that this method not only significantly improved the diagnostic accuracy, but also enhanced the model generalization ability. Its highest diagnostic accuracy reached 99.9% with certain application prospects.
- fault diagnosis /
- planetary gear box /
- MobileNetV3 /
- time-frequency transformation /
- hierarchical nearest neighbor graph embedding for efficient dimensionality reduction /
- feature fusion

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图 1 研究思路框架图

Figure 1. Research frame diagram

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图 2 MobileNetV3-AHFF和MS-HNNE模型结构图

Figure 2. Structure diagram of MobileNetV3 AHFF and MS-HNNE models

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图 3 时频变换融合过程

Figure 3. Fusion process of time-frequency transformation

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图 4 MobileNetV3 bneck结构图

Figure 4. MobileNetV3 bneck structure diagram

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图 5 自适应分层特征融合模块

Figure 5. Adaptive layered feature fusion module

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图 6 HNNE算法流程图

Figure 6. Flow chart of HNNE algorithm

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图 7 基于MobileNetV3-AHFF和MS-HNNE网络模型的行星齿轮箱故障诊断流程

Figure 7. Fault diagnosis flow of planetary gearbox based on MobileNetV3-AHFF and MS-HNNE model

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图 8 DDS行星齿轮箱故障诊断台

Figure 8. DDS planetary gear box fault diagnosis table

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图 9 不同时频变换方法的实验结果对比（单位：%）

Figure 9. Comparison of experimental results of different time-frequency transformation methods （unit：%）

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图 10 不同网络模型的DDS行星齿轮箱诊断性能对比

Figure 10. Comparison of diagnostic performance of DDS planetary gearboxes for different network models

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图 11 消融实验结果

Figure 11. Results of ablation experiment

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图 12 对DDS测试集的预测混淆矩阵和分类效果图

Figure 12. Prediction confusion matrix and classification performance diagram for DDS test set

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图 13 6种降维算法的三维特征可视化效果

Figure 13. Visualization effects of six dimensionality reduction algorithms on 3D features

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图 14 6种降维算法和2种池化方法故障识别准确率对比（单位：%）

Figure 14. Comparison of fault identification accuracy between six dimensionality reduction algorithms and two pooling methods （unit：%）

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图 15 不同网络模型的东南大学行星齿轮箱故障诊断准确率对比

Figure 15. Comparison of fault diagnosis accuracy for different network models on the Southeast University planetary gearbox

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表 1 行星齿轮箱故障样本分布

Table 1. Planetary gearbox fault sample distribution

故障类型	标签	样本数	训练集样本数	测试集样本数
裂纹	0	1000	800	200
健康	1	1000	800	200
缺齿	2	1000	800	200
断齿	3	1000	800	200
磨损	4	1000	800	200

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表 2 不同时频变换组合结果

Table 2. Results of different time-frequency transform combinations

不同时频组合	CT+ CWT	CT+ STFT	CWT+ STFT	CT+ CWT+STFT
准确率/%	98.2	98.0	97.8	98.5

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表 3 6种算法降维后的性能指标

Table 3. Performance indicators of six algorithms after dimensionality reduction

降维方法	降维性能指标
降维方法	S_b	S_w	S_b/S_w
Isomap	2199857.93382	95239.404406	23.10
LPP	418.49×10⁻⁴	38.26×10⁻⁴	10.94
LTSA	483.59×10⁻⁴	107.70×10⁻³	4.49
MDS	79514.712984	817425.5373	10.28
HNNE	522022.31	14826.33	35.21
MS-HNNE	82.52	1.40	58.94

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表 4 东南大学行星齿轮箱数据集样本分布

Table 4. Sample distribution of planetary gearbox dataset of Southeast University

实验工况	故障类型	标签	样本数	训练样本	测试样本
1200 r/min 0 N·m	缺损	0	1500	1200	300
	健康	1	1500	1200	300
	断齿	2	1500	1200	300
	齿根断裂	3	1500	1200	300
	齿面磨损	4	1500	1200	300

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表 5 不同模型在−5 dB噪声下故障识别准确率及变化

Table 5. Fault diagnosis accuracy and variation of different models under −5 dB noise conditions

模型	故障识别准确率/%		准确率变化/%
模型	0 dB	−5 dB	准确率变化/%
VGG16	92.6	86.2	−6.4
GoogleNet	96.8	92.9	−3.9
ResNet34	97.6	95.0	−2.6
ShuffleNetV2	98.0	96.1	−1.9
MobileNetV3	98.6	97.1	−1.5
MobileNetV3-AHFF- MS-HNNE	99.9	99.0	−0.9

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