基于扩散模型和DenseNet的少样本齿轮箱故障诊断方法

doi:10.13229/j.cnki.jdxbgxb.20241268

Abstract

Abstract:

Aiming at the problem of few samples in the fault diagnosis of rotating machinery， a fault diagnosis method for planetary gearboxes based on a diffusion model is proposed. The time-frequency diagrams of fault samples are used as the representation of fault features for fault classification. Firstly， the continuous wavelet transform is used to convert the one-dimensional vibration signals of the gearbox into time-frequency diagrams as training samples. Secondly， the diffusion model is optimized and improved to enable it to fully learn the fault features of the training samples and generate high-quality fault samples. Finally， a classifier based on a dense convolutional network is constructed， which combines the attention mechanism to enhance the performance of the classifier. The performance of the classifier is tested using a test set containing multiple fault types. The results show that the diagnostic accuracy of the proposed method is close to that under the condition of sufficient samples， reaching over 99%， and experimental comparisons with existing methods have been carried out. In addition， ablation experiments on the optimization settings of the wavelet transform method and the classifier are conducted to verify their effectiveness.

Key words: diffusion model, fault diagnosis, gears, small sample, attention mechanism

CLC Number:

TH132.425

Jie CAO,Zhi-feng CHEN,Jin-hua WANG,Li CHEN. Fault diagnosis method for gearbox with few samples based on diffusion model and DenseNet[J].Journal of Jilin University(Engineering and Technology Edition), 2026, 56(7): 1787-1797.

Figures/Tables 21

Fig.1

Fig.2

Fig.3

Table 1

Network model parameters"

模型	输出尺寸	网络结构
卷积层	$64 × 64$	$7 × 7 c o n v, 步长 2$
池化层	$32 × 32$	$3 × 3 最大池化, 步长 2$
EMA分支1	$32 × 1$	$沿高度 H 维度平均池化, 保留宽度为 1$
	$1 × 32$	$沿宽度 W 维度平均池化, 保留高度为 1$
	$32 × 32$	$1 × 1 c o n v, 通道数 64, 分组 16, 每组 4 通道$
	$32 × 32$	$组归一化, 每组 4 通道$
EMA分支2	$32 × 32$	$3 × 3 c o n v, 通道数保持 64$
融合分支1、2	$32 × 32$	—
密集层1	$32 × 32$	$1 × 1 3 × 3 × 6$
过渡层1	$32 × 32$	$1 × 1 c o n v$
过渡层1	$16 × 16$	$2 × 2 平均池化, 步长 2$
密集层2	$16 × 16$	$1 × 1 3 × 3 × 12$
过渡层2	$16 × 16$	$1 × 1 c o n v$
过渡层2	$8 × 8$	$2 × 2 平均池化, 步长 2$
密集层3	$8 × 8$	$1 × 1 3 × 3 × 48$
过渡层3	$8 × 8$	$1 × 1 c o n v$
过渡层3	$4 × 4$	$2 × 2 平均池化, 步长 2$
密集层4	$4 × 4$	$1 × 1 3 × 3 × 32$
分类层	$1 × 1$	$7 × 7 全局平均池化$
分类层	$5$	$S o f t m a x$

Table 1

Fig.4

Fig.5

Fig.6

Table 2

Planetary gearbox parameters"

参数	数值
太阳轮	28
环形齿轮	100
行星齿轮齿数（数量）	36（4）
啮合频率	（175/8） $f r$
太阳轮故障频率	（25/8） $f r$

Table 2

Table 3

Fig.7

Fig.8

Table 4

Table 5

Fig.9

Table 6

Fig.10

Table 7

Table 8

Fig.11

Fig.12

Fig.13

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标签	损伤种类	标签	损伤种类
1	齿轮断齿	4	齿根裂纹
2	健康状态	5	齿轮磨损
3	缺齿

标签	FID	LPIPS	MMD
平均值	49.894 7	0.186 1	0.144 8
1	54.775 2	0.264 6	0.200 7
2	47.824 5	0.144 2	0.138 9
3	46.171 4	0.149 7	0.125 9
4	45.745 4	0.124 9	0.113 2
5	54.957 2	0.247 2	0.145 4

方法	FID	MMD
WGAN?GP	150.21	0.214
AVAEGAN^［26］	113.41	0.181
UDWGAN^［27］	70.21	0.148
本文方法	49.89	0.145

实验序号	真实样本数量	生成样本数量	测试样本数量	准确率/%
1	40	0	1 000	98.074
2	40	20	1 000	98.872
3	40	40	1 000	99.240
4	40	60	1 000	99.120
5	40	80	1 000	99.370
6	30	0	1 000	96.748
7	30	20	1 000	97.300
8	30	40	1 000	98.466
9	30	60	1 000	98.108
10	30	80	1 000	99.098
11	20	0	1 000	96.072
12	20	20	1 000	98.216
13	20	40	1 000	96.962
14	20	60	1 000	96.902
15	20	80	1 000	98.228
16	10	0	1 000	89.666
17	10	20	1 000	94.192
18	10	40	1 000	96.404
19	10	60	1 000	96.066
20	10	80	1 000	97.354

标签	精准率	召回率	Specificity
1	0.996	0.999	0.999
2	0.996	0.999	0.999
3	0.998	0.98	1.0
4	0.984	0.996	0.996
5	1.0	1.0	1.0

Fault diagnosis method for gearbox with few samples based on diffusion model and DenseNet

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