基于层级数字孪生的机电系统故障数据生成

doi:10.7527/S1000-6893.2025.32663

Abstract

Abstract:

Multi-coupled Electromechanical Systems （MES） are the important component of modern industry， their stable operation heavily relies on effective fault diagnosis. With the development of artificial intelligence technique， sufficient fault data plays an important role in MES’s fault diagnosis， while is quite difficult to obtain in practices. In this regard， there is urgent need to generate the virtual fault data through the system simulation and improve the fault diagnosis performance， where the Digital Twin technique with superior virtual mapping capabilities for entity characteristics provides the potential opportunity. However， there is a lack of an effective method that can decouple the complicated MES entity and construct its full-system fault Digital Twin model. Aiming at the above target， we propose a hierarchical collaborative Digital Twin modeling method to implement the fault data generation. In order to structurally describe the complicated entity， MES is decoupled and identified as the multidimensional and multimodal triplet representations， namely， element， relationship， and data. Given the element and data， the hierarchical data-model combined technique is proposed to develop the four-level （space， behavior， process， and status） sub-Digital Twin models， to well balance the modeling adaptability and modeling precision during the local virtualization of MES. Thanks for the proposed collaborative orchestration algorithm， these heterogeneous sub-Digital Twin models are further interacted and integrated into the full-system Digital Twin according to the aforementioned relationship representation， which jointly constitutes the global mirror of the MES entity under various fault modes. We conducted the experiments to validate the proposed method by using a multi-coupled electromechanical fault test bench. The experimental results show that our method improved the accuracy of fault diagnosis by an average of 11.95%， which demonstrates its superiority in the fault data generation for the complicated entity such as MES.

Key words: multi-coupled electromechanical systems, Digital Twin, data generation, fault diagnosis, collaborative orchestration algorithm

CLC Number:

V241.0

Yu DING, Guoao NING, Kaixin JIN, Bo SUN, Huai LI, Xuanyuan SU. Fault data generation for electromechanical systems based on hierarchical Digital Twin[J]. Acta Aeronautica et Astronautica Sinica, 2026, 47(8): 432663.

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References 78

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方法	对象层级	生成方式	信息模态
提出方法	系统级	数据-模型融合	知识、数据、逻辑
文献［38］	转子-滚动体轴承：组件级	动力学模型：模型驱动	知识
文献［39］	滚动轴承：组件级	数据-模型融合	知识、数据
文献［40］	转子系统：子系统级	有限元：模型驱动	知识
文献［41］	三缸泵：子系统级	Simscape模型：模型驱动	知识
文献［42］	变速箱：子系统级	动力学模型：模型驱动	知识

参数	模型驱动值	数据驱动值	算法
额定功率、电压（线电压）、频率	［948.17，460.00，60.000］	［948.17，460.00，60.000］	基于Levenberg-Marquardt算法的数据驱动模型优化^［68］
定子电阻和电感	［12.010， 0.023 000］	［12.070， 0.023 400］
转子电阻和电感	［6.350 0， 0.023 000］	［6.362 0， 0.023 400］
互感	［0.109 00］	［0.109 70］

模型驱动参数	数值	数据驱动参数	数值	融合机制
刚度/（N∙mm^-1）	1.0×10⁵	编码器维度	32	预训练与微调
指数	1.5	解码器维度	32
阻尼/（N∙s∙mm^-1）	50	特征维度	8
最大变形量/mm	1.0×10^-2	激活函数	ReLU
静摩擦系数	3.0×10^-1	学习率e	1.0×10^-3
动摩擦系数	2.0×10^-1	丢弃率	1.0×10^-1

模型名称	变量名称	变量方向	变量类型
动力子模型	电机转速	输出	交互变量
	电机转角	输出	交互变量
	电机扭矩	输入	交互变量
	三相定子电流	输出	状态变量
传动子模型	驱动转角	输入	交互变量
	驱动扭矩	输出	交互变量
	负载扭矩	输入	交互变量
	传动转速	输出	交互变量
	近端轴承振动	输出	状态变量
	远端轴承振动	输出	状态变量
负载子模型	负载转速	输入	交互变量
	泵出口流量	输出	状态变量
	泵压差	输出	状态变量

类型	真实实体数据集	数字孪生生成数据集
精确率/%	75.58±2.85	87.53±1.47
召回率/%	75.21±2.82	87.36±1.44
F1分数/%	74.63±2.79	87.30±1.43

Fault data generation for electromechanical systems based on hierarchical Digital Twin

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传感器	传感器模型	安装位置	信号名称
电流夹	FLUKE i400E	电动机母线	A相定子电流
			B相定子电流
			C相定子电流
加速度计	Hansford HS-100	电机端盖	电机Z向加速度
		近端轴承座	近端轴承Z向加速度
		远端轴承座	远端轴承Z向加速度
转速表	SQI	转速表	电机转速

参数	正常	负载	动力		传动				耦合
参数	normal	pump	ITSC	BR	comb	ball	inner	outer	ball-pump	inner-pump	outer-pump	Acc
Average Acc												0.752 1
normal	425	10	62	56	0	15	0	0	3	0	0	0.744 3
pump	40	433	16	38	0	43	0	0	14	0	0	0.741 4
ITSC	101	282	177	10	0	23	1	0	1	0	0	0.297 5
BR	136	38	34	358	0	11	0	0	0	0	0	0.620 5
comb	0	0	0	0	532	0	10	5	0	31	1	0.918 8
ball	28	5	3	10	0	391	0	5	118	14	3	0.677 6
inner	0	0	0	0	17	1	465	1	1	88	0	0.811 5
outer	0	0	0	0	3	2	0	540	3	0	14	0.960 9
ball-pump	0	13	0	2	0	134	0	0	428	3	0	0.737 9
inner-pump	0	0	0	0	38	6	66	1	22	438	2	0.764 4
outer-pump	0	0	0	0	0	0	0	1	0	0	587	0.998 3

类型	正常	负载	动力	传动	耦合	Acc
正常	425	10	118	15	3	0.744 3
负载	40	433	54	43	14	0.741 4
动力	237	320	579	35	1	0.494 0
传动	28	5	13	1 972	273	0.860 8
耦合	0	13	2	246	1 480	0.850 1

类型	正常	负载	动力	传动	耦合	Acc
正常	419	41	89	20	2	0.733 8
负载	17	471	41	52	3	0.806 5
动力	133	128	900	9	2	0.767 9
传动	1	3	0	2 233	54	0.974 7
耦合	0	5	0	96	1 640	0.942 0

层级	可更新参数
空间维	定子匝数，定子外径，铁芯截面积，定子长度，转子长度
行为维	互感、定子电感、转子电感
过程维	电压斜率中的磁通量及频率
状态维	定子绕组短路匝数（ITSC故障）、转子条断裂电阻增益系数（BR故障）

层级	优化器	实体数据
空间维	逆向工程
行为维	序列二次规划	稳定状态下的正常数据
过程维		变速条件下的正常数据
状态维		ITSC和BR故障下数据

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序号	信号类型	单位	校准值
1	A相定子电流	mA	1
2	B相定子电流	mA	1
3	C相定子电流	mA	1
4	电机Z向加速度	m/s²	100
5	近端轴承Z向加速度	m/s²	100
6	远端轴承Z向加速度	m/s²	100
7	电机转速	mV	1