基于两阶段域泛化学习框架的轴承故障诊断方法

谢刚; 韩秦; 聂晓音; 石慧; 张晓红; 田娟

doi:10.16383/j.aas.c230716

基于两阶段域泛化学习框架的轴承故障诊断方法

doi: 10.16383/j.aas.c230716 cstr: 32138.14.j.aas.c230716

谢刚^{1, 2,},
韩秦^{1, 2,},
聂晓音^{1, 2,},
石慧^{1, 2,},
张晓红^{1, 2,},
田娟^{1, 2,}

1.
太原科技大学电子信息工程学院太原 030024
2.
先进控制与工业智能山西省重点实验室太原 030024

基金项目: 山西省科技重大专项计划“揭榜挂帅”项目(202201090301013), 山西省重点研发计划项目(202102020101002, 202202100401002, 202202150401005), 山西省基础研究计划(自由探索类)面上项目(20210302123206, 202203021211194, 202203021221142)资助

详细信息

作者简介:
谢刚：太原科技大学教授. 主要研究方向为机器视觉与图像处理, 大数据驱动的智能故障诊断. E-mail: xiegang@tyust.edu.cn

韩秦：太原科技大学电子信息工程学院硕士研究生. 2021年获得中原工学院测控技术与仪器专业学士学位. 主要研究方向为大数据驱动的智能故障诊断. E-mail: hanqin@stu.tyust.edu.cn

聂晓音：太原科技大学电子信息工程学院讲师. 主要研究方向为大数据驱动的智能故障诊断. 本文通信作者. E-mail: 2022036@tyust.edu.cn

石慧：太原科技大学电子信息工程学院教授. 主要研究方向为复杂系统故障预测与健康管理. E-mail: huishi@stu.tyust.edu.cn

张晓红：太原科技大学教授. 主要研究方向为复杂系统故障预测与健康管理, 大数据装备与制造. E-mail: zhangxh@stu.tyust.edu.cn

田娟：太原科技大学电子信息工程学院讲师. 主要研究方向为大数据驱动的智能故障诊断. E-mail: juantian@stu.tyust.edu.cn

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出版历程
- 收稿日期: 2023-11-16
- 录用日期: 2024-08-07
- 网络出版日期: 2024-09-27
- 刊出日期: 2024-11-26

A Two-stage Domain Generalization Learning Framework for Fault Diagnosis of Bearings

XIE Gang^{1, 2
,},
HAN Qin^{1, 2
,},
NIE Xiao-Yin^{1, 2
,},
SHI Hui^{1, 2
,},
ZHANG Xiao-Hong^{1, 2
,},
TIAN Juan^{1, 2
,}

1.
School of Electronic and Information Engineering, Taiyuan University of Science and Technology, Taiyuan 030024
2.
Advanced Control and Industrial Intelligence Shanxi Provincial Key Laboratory, Taiyuan 030024

Funds: Supported by Major Science and Technology Project of Shanxi Province (202201090301013), Key Research and Development Plan of Shanxi Province (202102020101002, 202202100401002, 202202150401005), and Basic Research Program of Shanxi Province (20210302123206, 202203021211194, 202203021221142)

More Information

Author Bio:
XIE Gang　Professor at Taiyuan University of Science and Technology. His research interest covers machine vision and image processing, big data-driven intelligent fault diagnosis

HAN Qin　Master student at the School of Electronic and Information Engineering, Taiyuan University of Science and Technology. She received her bachelor degree in measurement and control technology and instruments from Zhongyuan University of Technology in 2021. Her research interest covers big data-driven intelligent fault diagnosis

NIE Xiao-Yin　Lecturer at the School of Electronic and Information Engineering, Taiyuan University of Science and Technology. Her research interest covers big data-driven intelligent fault diagnosis. Corresponding author of this paper

SHI Hui　Professor at the School of Electronic and Information Engineering, Taiyuan University of Science and Technology. Her research interest covers fault prediction and health management of complex systems

ZHANG Xiao-Hong　Professor at Taiyuan University of Science and Technology. Her research interest covers fault prediction and health management of complex systems, big data equipment and manufacturing

TIAN Juan　Lecturer at the School of Electronic and Information Engineering, Taiyuan University of Science and Technology. Her research interest covers big data-driven intelligent fault diagnosis

摘要

摘要: 设备在实际运行过程中工况复杂多变, 导致振动信号分布存在较大差异. 现有的多数方法通过添加度量指标来约束特征提取过程, 提取源域和目标域的相似特征以解决从单一源域到目标域的诊断问题. 然而, 实际运行过程往往包含多个源域数据, 且目标域信息在不同源域中存在较大差异, 难以有效学习不同域之间的域不变特征. 针对上述问题, 提出了一种基于两阶段域泛化学习框架的轴承故障诊断方法. 在第一阶段, 利用大尺寸卷积特征提取模型对多视图振动信号进行预训练, 提取多个源域数据之间的初级故障特征. 在第二阶段, 将初级故障特征输入动静双态融合的时空图卷积模型中, 捕捉随时间变化的动态特征和全局时空特征. 通过两阶段的学习, 将多个源域的数据映射到一个共有特征空间, 提取判别性和泛化性特征. 实验结果表明, 该方法在多源域轴承故障诊断任务中具有较高的诊断精度和较强的泛化能力.
- 故障诊断 /
- 域泛化 /
- 多源域 /
- 多视图 /
- 时空关系
Abstract: During the actual operation of the equipment, the working conditions are complex and changeable, resulting in large differences in vibration signal distribution. Many existing methods constrain the feature extraction process by incorporating measurement metrics, aiming to extract similar features from both the source and target domains to address diagnostic problems from a single source domain to a target domain. However, the actual operational process often involves data from multiple source domains, and the target domain information exhibits significant differences across these various source domains, making it difficult to extract the domain invariant feature. In response to the above problems, this paper proposes a two-stage domain generalization learning framework for fault diagnosis of bearings. In the first stage, the large-scale convolutional feature extraction model is used to pre-train multi-view vibration signals to extract primary fault features between multiple source domain data. In the second stage, the primary fault features are input into the spatial-temporal graph convolutional model for dynamic and static two-state fusion combining dynamic and static states to capture the dynamic features and global spatiotemporal features that change over time. Through two-stage learning, data from multiple source domains are mapped to a common feature space, and discriminative and generalization features are extracted. Experimental results show that this method has high diagnostic accuracy and strong generalization ability in multi-source domain bearing fault diagnosis tasks.
- Fault diagnosis /
- domain generalization /
- multi-source domain /
- multi-view /
- spatial-temporal relationship

HTML全文

图 1 多源域到单目标域的域泛化

Fig. 1 Domain generalization from multi-source domains to single target domain

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图 2 两阶段域泛化学习框架

Fig. 2 The two-stage domain generalization learning framework

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图 3 大尺寸卷积特征提取模型

Fig. 3 Large-scale convolutional feature extraction model

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图 4 动静双态融合的时空图卷积模型

Fig. 4 Spatial-temporal graph convolutional model for dynamic and static two-state fusion

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图 5 动态节点连接

Fig. 5 Dynamic node connection

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图 6 静态节点连接

Fig. 6 Static node connection

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图 7 轴承故障模拟实验台

Fig. 7 Bearing fault simulation test bench

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图 8 故障细节

Fig. 8 Fault details

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图 9 动静双态融合的时空图卷积模型的特征可视化

Fig. 9 Feature visualization of spatial-temporal graph convolutional model for dynamic and static two-state fusion

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图 10 不同诊断方法的特征可视化

Fig. 10 Feature visualizations of different diagnosis methods

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表 1 不同域泛化任务的轴承数据说明

Table 1 Bearing data description of different domain generalization tasks

域泛化任务		源域			目标域
域泛化任务		转速(rpm)	总样本数	特征序列数	转速(rpm)	总样本数	特征序列数
任务1	B-C-D→A	600、900、1200	30000	29988	300	10000	9996
任务2	A-C-D→B	300、900、1200	30000	29988	600	10000	9996
任务3	A-B-D→C	300、600、1200	30000	29988	900	10000	9996
任务4	A-B-C→D	300、600、900	30000	29988	1200	10000	9996

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表 2 结构参数设计

Table 2 Structural parameter design

网络结构			输入尺寸	输出尺寸	参数设置	填充方式
第一阶段	输入层		64×3×1024	64×3×1024×1	—	—
	卷积层(C1)		64×3×1024×1	64×3×13×64	kernel_size=400, filter=64, stride=50	same
	最大池化层(P2)		64×3×13×64	64×3×2×64	pool_size=5, strides=5	valid
	Dropout层(D3)		64×3×2×64	64×3×2×64	p=0.5	—
	展平层(F4)		64×3×2×64	64×3×128	—	—
	全连接层(F5)		64×3×128	64×384	—	—
	故障分类器		64×384	64×5	—	—
第二阶段	输入层		64×3×128	64×3×128	—	—
	添加时间步		64×3×128	64×5×3×128	—	—
	动态图卷积层		64×5×3×128	64×5×3×64	k=3, filter=64, $ \lambda =0.001 $	valid
	时序卷积层		64×5×3×64	64×5×3×64	filter=64, stride=(1,1), kernel=(3,1)	same
	静态图卷积层		64×5×3×128	64×5×3×64	k=3, filter=64	valid
	时序卷积层		64×5×3×64	64×5×3×64	filter=64, stride=(1,1), kernel=(3,1)	same
	全连接层		64×5×3×64, 64×5×3×64	64×5×3×128	—	—
	展平层		64×5×3×128	64×1920	—	—
	故障分类	全连接层	64×1920	64×64	—	—
	故障分类	故障分类器	64×64	64×5	—	—
	域分类	梯度反转层	64×1920	64×1920	$ \beta =0.01 $	—
		全连接层	64×1920	64×64	—	—
		域分类器	64×64	64×4	—	—

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表 3 五种健康状态分类结果统计表

Table 3 Table of classification results of five health states

健康状态	准确率(%)	F1得分(%)	精确率(%)	召回率(%)
滚子故障	97.05±2.32	97.39±2.02	95.95±2.87	99.96±0.01
内圈故障	99.06±0.10	99.06±0.10	99.80±0.01	98.33±0.57
内外圈故障	92.27±1.05	97.61±1.31	99.98±0.01	96.34±0.07
外圈故障	99.15±0.04	99.15±0.01	98.44±0.18	99.20±0.50
正常状态	99.98±0.01	99.98±0.01	99.97±0.01	99.99±0.01
平均值	98.77±0.36	98.77±0.38	98.83±0.53	98.77±0.32

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表 4 不同的多视图数据组合对性能的影响统计表

Table 4 Table of the impact of different multi-view data combinations on performance

不同视图组合	准确率(%)	F1得分(%)	精确率(%)	召回率(%)
$ xz $	90.75±0.50	90.24±0.62	92.06±0.76	90.42±0.61
$ xy $	92.31±0.71	92.67±0.62	93.18±0.45	92.31±0.71
$ yz $	94.78±2.23	94.57±2.55	95.70±1.33	94.76±2.54
$ xyz $	98.77±0.36	98.77±0.38	98.83±0.53	98.77±0.32

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表 5 动静双态融合的时空图卷积模型不同组成结构对性能的影响统计表

Table 5 Table of the impact of different structures of the spatial-temporal graph convolutional model for dynamic and static two-state fusion on performance

动态时空图卷积模块		静态时空图卷积模块		准确率(%)	F1 得分(%)	精确率(%)	召回率(%)
动态图卷积层	时序卷积层	静态图卷积层	时序卷积层	准确率(%)	F1 得分(%)	精确率(%)	召回率(%)
√	×	√	×	90.36±0.63	90.32±0.52	90.87±2.40	90.36±0.37
×	√	×	√	92.58±4.35	92.66±4.13	93.57±2.01	92.25±4.35
×	×	√	√	90.69±3.12	90.75±3.24	92.35±1.05	90.70±4.49
√	√	×	×	90.35±0.51	90.34±0.51	91.91±0.56	90.35±0.59
√	√	√	√	98.77±0.36	98.77±0.38	98.83±0.53	98.77±0.32

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表 6 不同特征可视化效果的量化指标统计表

Table 6 Table of quantitative indicators of different feature visualization effects

量化指标	$ {\boldsymbol{X}}_{text}^{{{s}_{q}}} $	$ {\boldsymbol{X}}_{DGCN}^{{{s}_{q}}} $	$ {\boldsymbol{X}}_{DTCN}^{{{s}_{q}}} $	$ {\boldsymbol{X}}_{GCN}^{{{s}_{q}}} $	$ {\boldsymbol{X}}_{TCN}^{{{s}_{q}}} $	$ {{{\boldsymbol{F}}}^{{{s}_{q}}}} $
轮廓系数	0.0828	0.1134	0.4807	0.1920	0.5021	0.5431
CH指数	1194.49	2336.10	13642.92	5208.58	14917.97	17765.84

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表 7 不同方法的诊断结果统计表

Table 7 Table of statistics of diagnostic results of different methods

评估指标	域泛化任务B-C-D→A
评估指标	CNN	WDCNN	GCN	TCN	DAGCN	CVC-Net	所提方法
准确率(%)	60.24±9.14	63.20±6.51	59.77±8.56	62.64±5.38	85.01±6.75	86.31±5.25	95.14±1.75
F1得分(%)	64.01±6.86	65.70±5.30	54.85±5.17	57.37±7.54	83.30±8.21	85.11±4.50	95.81±0.86
精确率(%)	63.61±8.43	61.86±6.46	57.38±6.52	61.15±8.64	90.98±3.83	80.04±3.59	95.95±1.98
召回率(%)	61.13±7.25	63.19±5.43	59.07±7.89	62.32±7.95	85.37±9.47	86.67±5.58	95.03±0.85

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表 8 不同方法特征可视化效果的量化指标统计表

Table 8 Table of quantitative indicators of feature visualization effects of different methods

量化指标	CNN	WDCNN	GCN	TCN	DAGCN	CVC-Net	所提方法
轮廓系数	0.2164	0.1926	0.1843	0.2390	0.4440	0.4602	0.5082
CH指数	3920.27	3484.86	3483.51	4795.91	12051.26	11544.79	14780.77

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