基于注意力卷积胶囊网络的电液比例伺服阀故障诊断

doi:10.7527/S1000-6893.2024.30407

Abstract

Abstract:

To address the issues of unknown parameters and difficult modeling in current model-based proportional servo valve fault diagnosis methods， we propose the Attention Convolution Capsule Network （ACCN） algorithm for the first time and design a new proportional servo valve fault diagnosis method based on this network. This algorithm first uses convolutional neural networks and an efficient channel attention mechanism to extract and fuse features from the original multi-channel one-dimensional time-domain signals. The fused results are then input into the subsequent capsule network for further feature extraction and fault classification， yielding the corresponding diagnosis results. The effectiveness of the proposed algorithm and diagnostic method was validated on a proportional servo valve fault simulation test bench. The proposed method achieved 100% accuracy on the test set， and the inclusion of the channel attention mechanism and capsule network enhanced the diagnostic performance of the model to varying degrees.

Key words: attention mechanism, convolutional neural networks, capsule network, proportional servo valve, fault diagnosis

CLC Number:

V245.1

Yuanhao HU, Yibo SONG, Jiahui LIU, Xiaoli ZHAO, Wenxiang DENG, Jian HU, Jianyong YAO. Fault diagnosis of electro-hydraulic proportional servo valves based on attention convolutional capsule networks[J]. Acta Aeronautica et Astronautica Sinica, 2024, 45(15): 630407.

Figures/Tables 9

Table 1

Dynamic routing algorithm

步骤	操作
1	循环 $(u j \| i, T, l)$
2	对所有 $l$ 层的胶囊 $i$ 和 $l + 1$ 层的胶囊 $j$ ： $b i j ← 0$
3	For r=1 to T do
4	$c i j ← s o f t m a x (b i j)$
5	$s j ← ∑ i = 1 L 2 c i j u j \| i$ 见式（8）
6	$v j ← s q u a s h (s j)$ 见式（9）
7	$b i j ← b i j + u j \| i$ · $v j$
8	Return $v j$

Table 1

Fig. 1

Fig.2

Table 2

Fig.3

Table 3

Table 4

Fig.4

Fig.5

References 22

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标签	故障类型	故障位置
0	比例电磁铁轻度退化	比例电磁铁
1	比例电磁铁中度退化	比例电磁铁
2	比例电磁铁严重退化	比例电磁铁
3	阀芯轻度卡滞故障	阀芯
4	阀芯中度卡滞故障	阀芯
5	阀芯严重卡滞故障	阀芯
6	正常	无

层类型	核尺寸	步长	核个数	输出尺寸
输入				（6，1 000）
卷积层（bn+LeakyReLU）	100	4	16	（16，226）
卷积层（bn+LeakyReLU）	20	2	32	（32，104）
卷积层（bn+LeakyReLU）	10	2	64	（64，48）
卷积层（bn+LeakyReLU）	5	2	64	（64，22）
ECA层				（64，22）
初级胶囊层	4	2	20	（200，16）
数字胶囊层				（7，32）

模型	准确率/%	平均准确率/%
DCNN	92.7，91.5，90.7，93.1，91.4	91.9
DCNN+ECA	94.0，94.8，93.0，94.4，95.5	94.3
ACCN	100.0，100.0，100.0，100.0，100.0	100.0

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Fault diagnosis of electro-hydraulic proportional servo valves based on attention convolutional capsule networks

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