正交约束域适应的跨工况滚动轴承剩余使用寿命预测方法

韩延; 林志超; 黄庆卿; 向敏; 文瑞; 张焱

doi:10.11999/JEIT230274

正交约束域适应的跨工况滚动轴承剩余使用寿命预测方法

doi: 10.11999/JEIT230274

韩延^{1, 2},
林志超¹,
黄庆卿^{1, 2, ,},
向敏¹,
文瑞¹,
张焱^{1, 2}

1.
重庆邮电大学自动化学院重庆 400065
2.
重庆邮电大学工业互联网研究院重庆 401122

基金项目: 国家重点研发计划(2022YFE0114300)，重庆市教委科学技术研究项目(KJQN202100612)，重庆市博士后科学基金(cstc2021jcyj-bshX0094)

详细信息

作者简介:
韩延：男，讲师，研究方向为装备智能运维、深度学习、边缘计算

林志超：男，硕士生，研究方向为装备智能运维与健康管理

黄庆卿：男，副教授，研究方向为工业物联网、边缘计算、机械故障预测与健康管理

向敏：男，教授，研究方向为工业测控和工业物联网等

文瑞：男，硕士生，研究方向为装备智能运维与健康管理

张焱：男，副教授，研究方向为工业互联网与边缘智能、工业大数据等

通讯作者:
黄庆卿　huangqq@cqupt.edu.cn

中图分类号: TN911.7; TH165.3
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- 被引次数: 0
出版历程
- 收稿日期: 2023-04-17
- 修回日期: 2023-07-14
- 网络出版日期: 2023-07-21
- 刊出日期: 2024-03-27

A Domain Adaptive Method with Orthogonal Constraint for Predicting the Remaining Useful Life of Rolling Bearings under Cross Working Conditions

HAN Yan^{1, 2},
LIN Zhichao¹,
HUANG Qingqing^{1, 2
, ,},
XIANG Min¹,
WEN Rui¹,
ZHANG Yan^{1, 2}

1.
School of Automation, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
2.
Industrial Internet Reaearch Institute of Chongqing University of Posts and Telecommunications, Chongqing 401122, China

Funds: National Key Research and Development Program of China (2022YFE0114300), Science and Technology Research Program of Chongqing Municipal Education Commission (KJQN202100612), Chongqing Postdoctoral Science Foundation (cstc2021jcyj-bshX0094)

摘要

摘要: 针对跨工况轴承剩余使用寿命(RUL)预测模型的决策边界不明显、特征可辨识性低的问题，该文提出一种正交约束的最大分类器差异方法(MCD_OC)。首先，将采集的轴承原始振动信号进行快速傅里叶变换，得到振动信号的频域信号作为模型的输入；然后，通过卷积神经网络(CNN)和门控循环神经网络(GRU)提取轴承信号的深层时空特征，利用最大分类器差异将源域和目标域特征对齐，并对目标域轴承深层特征进行正交约束，增大无标签目标域样本特征之间的可辨识性；最后，基于轴承寿命数据集开展了跨工况轴承寿命预测对比实验，对该文所提方法进行评估，并在多组实验中取得最优结果。
- 滚动轴承 /
- 剩余使用寿命 /
- 正交约束 /
- 最大分类器差异
Abstract: To address the problems that blurred decision boundaries and low identifiability of features in the rolling bearing Remaining Useful Life (RUL) prediction under cross working conditions, a domain adaptive method with Maximum Classifier Discrepancy network with Orthogonal Constraints (MCD_OC) is proposed. Firstly, the fast Fourier transform is applied to transform the raw vibration signal into the frequency domain signal and input it to the model. Then, Convolutional Neural Network (CNN) and Gate Recurrent Unit (GRU) are used to extract the depth spatiotemporal features of the bearing signal, the source and target domain feature is aligned using the maximum classifier discrepancy, and the orthogonal constraint is applied to constrain target domain features to increase the identifiability between features of unlabeled target domain feature. Finally, comparative experiments are conducted on the prediction of cross working condition RUL predict based on the bearing life dataset to evaluate the method in this work, and the optimal results are obtained in multiple experiments.
- Rolling bearing /
- Remaining Useful Life(RUL) /
- Orthogonal constraint /
- Maximum classifier discrepancy

HTML全文

图 1 普通的域适应方法和最大分类器差异

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图 2 对目标域特征正交约束增大特征差异

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图 3 MCD_OC 网络结构

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图 4 正交约束域适应的跨工况滚动轴承剩余使用寿命预测方法流程

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图 5 轴承2-1的时域信号和归一化后的频域信号

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图 6 不同方法在不同任务下的轴承寿命预测结果对比

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表 1 轴承运行的3种不同工况

变量	工况1	工况2	工况3
压力(N)	4000	4200	5000
转速(r/min)	1800	1650	1500

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表 2 MCD_OC试验数据集

任务	源域训练集	目标域训练集	测试集
工况1→工况2(C12)	轴承1-1～1-7	轴承2-1,2-2	轴承2-6
工况1→工况3(C13)	轴承1-1～1-7	轴承3-1,3-2	轴承3-3
工况2→工况1(C21)	轴承2-1～2-7	轴承1-1,1-2	轴承1-7
工况2→工况3(C23)	轴承2-1～2-7	轴承3-1,3-2	轴承3-3
工况3→工况1(C31)	轴承3-1～3-3	轴承1-1,1-2	轴承1-7
工况3→工况2(C32)	轴承3-1～3-3	轴承2-1,2-2	轴承2-6

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表 3 模型参数

网络层	参数	激活函数
卷积层1, BN	卷积核大小7×1，数量 80，步长1	ReLU
最大池化层1	大小 8×1，步长8	\
卷积层2, BN	卷积核大小5×1，数量160，步长1	ReLU
最大池化层2	大小8×1，步长1	\
卷积层3, BN	卷积核大小3×1，数量 320，步长1	ReLU
最大池化层3	大小4×1，步长1	\
GRU	输出维度 1440	\
全连接层1	神经元个数128，Dropout 0.5	\
全连接层2	神经元个数32，Dropout 0.5	\
全连接层3	神经元个数1	\

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表 4 MCD_OC和对比模型的预测结果

方法	评价指标	C12	C13	C21	C23	C31	C32	平均值
DDC	MAE	0.179	0.118	0.224	0.212	0.160	0.307	0.200
DDC	RMSE	0.218	0.136	0.258	0.246	0.208	0.354	0.237
DANN	MAE	0.120	0.070	0.271	0.227	0.166	0.413	0.211
DANN	RMSE	0.149	0.092	0.311	0.257	0.213	0.442	0.244
CORAL	MAE	0.146	0.065	0.230	0.198	0.162	0.414	0.202
CORAL	RMSE	0.167	0.080	0.269	0.238	0.234	0.435	0.237
DDC_OC	MAE	0.174	0.080	0.184	0.195	0.160	0.323	0.186
DDC_OC	RMSE	0.209	0.116	0.223	0.219	0.210	0.363	0.223
MCD_DA	MAE	0.125	0.068	0.229	0.217	0.165	0.385	0.198
MCD_DA	RMSE	0.150	0.086	0.274	0.253	0.219	0.426	0.235
MCD_OC	MAE	0.117	0.084	0.163	0.178	0.160	0.202	0.151
MCD_OC	RMSE	0.144	0.106	0.220	0.216	0.208	0.234	0.188

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