基于图域泛化的直升机尾传动系统故障诊断方法

doi:10.7527/S1000-6893.2025.32642

Abstract

Abstract:

To address the challenges of limited access to high-quality fault data and the poor generalization performance of traditional transfer learning methods on unseen fault types， this paper proposes an Adaptive-weight Multi-level Fusion Graph Domain Generalization （AMFGDG） framework for fault diagnosis of helicopter tail transmission systems. Specifically， multi-node vibration signals under various health conditions are generated by integrating the concentrated mass method with the finite element method， and their validity is experimentally verified. Graph-structured samples with finite element nodes are constructed from the generated data for multiple source domains， while test data from different sensors are formulated as target domain graphs. A self-attention mechanism is employed to adaptively update edge weights， enabling precise modeling of inter-node influence. Additionally， a mixture-of-experts gating strategy is introduced to fuse information across graph layers and mitigate the over-smoothing effect in deep GNNs. The model is optimized via a joint loss function combining classification loss， deep clustering loss， and local distribution alignment loss. Diagnostic results and ablation studies on a private helicopter tail transmission test bench dataset and the public Case Western Reserve University （CWRU） bearing dataset demonstrate the proposed method’s low data dependency and strong generalization capability， achieving an accuracy of up to 95.4%.

Key words: fault diagnosis, helicopter tail transmission system, graph convolutional networks, domain generalization, dynamic modeling

CLC Number:

V267

Guowang CHEN, Qian TANG, Liu HE. Fault diagnosis method of helicopter tail transmission systems based on graph domain generalization[J]. Acta Aeronautica et Astronautica Sinica, 2026, 47(7): 232642.

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References 43

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数据集名称	数据集简称	故障全称	故障简称	转速-负载
重庆大学直升机数据集	数据集A	健康	HY	1 000 r/min-0（N·m） 1 000 r/min-20（N·m） 3 000 r/min-0（N·m） 3 000 r/min-20（N·m）
		轴承外圈轻度裂纹	OLC
		轴承外圈重度裂纹	OHC
		轴承内圈轻度裂纹	ILC
		轴承内圈轻度磨损	ILW
		轴承内圈重度磨损	IHW
		空心轴裂纹	HSC
		外花键磨损	ESW
		联轴器裂纹	CC
西储大学轴承数据集	数据集B	健康	HY	1 797 r/min-0（HP） 1 772 r/min-1（HP） 1 750 r/min-2（HP） 1 730 r/min-3（HP）
		轴承内圈轻度故障	ILF
		轴承内圈中度故障	IMF
		轴承内圈重度故障	IHF
		轴承外圈轻度故障	OLF
		轴承外圈中度故障	OMF
		轴承外圈重度故障	OHF
		轴承滚珠轻度故障	BLF
		轴承滚珠中度故障	BMF
		轴承滚珠度故障	BHF

数据集	泛化任务	源域（仿真域）工况	目标域（测试域）工况	源域（仿真域）标签	目标域（测试域）标签
重庆大学数据集	T1	1 000-20， 3 000-0， 3 000-20	1 000-0	2，3，4	1
	T2	1 000-0， 3 000-0， 3 000-20	1 000-20	1，3，4	2
	T3	1 000-0， 1 000-20， 3 000-20	3 000-0	1，2，4	3
	T4	1 000-0， 1 000-20， 3 000-0	3 000-20	1，2，3	4
西储大学数据集	T1	1 797-0， 1 772-1， 1 750-2	1 730-3	2，3，4	1
	T2	1 797-0， 1 772-1， 1 730-3	1 750-2	1，3，4	2
	T3	1 797-0， 1 750-2， 1 730-3	1 772-1	1，2，4	3
	T4	1 772-1， 1 750-2， 1 730-3	1 797-0	1，2，3	4

数据集名称	泛化任务	目标域精度/%
数据集名称	泛化任务	DANN	DCORAL	CNN-C	CCDG	AMFGDG
数据集A	T1	66.92	58.75	67.72	67.28	71.9
	T2	60.16	60.36	66.85	69.78	70.65
	T3	74.70	73.51	74.78	73.49	81.87
	T4	84.27	75.20	82.56	83.85	95.40
数据集B	T1	50.54	71.11	77.62	75.62	86.32
	T2	55.52	70.43	69.86	70.04	82.59
	T3	56.82	66.34	67.50	67.77	90.18
	T4	56.71	64.52	74.86	73.66	88.25

Fault diagnosis method of helicopter tail transmission systems based on graph domain generalization

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