Unsupervised Cross-domain Fault Diagnosis Method under Small Sample Conditions

doi:10.3969/j.issn.1004-132X.2026.09.001

Abstract

Abstract:

To address the issues of data annotation scarcity under small-sample conditions and domain shift in cross-domain applications in fault diagnosis， a domain adaptation strategy integrating contrastive learning was proposed. First， a hierarchical feature alignment mechanism was adopted to jointly optimize domain-level adversarial adaptation and class-level contrastive learning. This mechanism balanced the transfer of domain-invariant features and the preservation of class discriminability， thereby improving the fault classification accuracy in the target domain. Second， a one-dimensional domain-shared convolutional neural network（CNN） was constructed to extract the common features of data from the source domain and target domain. By combining the unsupervised domain discriminative loss with a gradient reversal layer to optimize domain-level adaptation， the transferability of domain-invariant characteristics was enhanced. Finally， an unsupervised contrastive loss function was designed to optimize class-specific information. This function constrains the intra-class compactness and inter-class separability in the feature space， enabling class-level alignment under limited samples and effectively suppressing misidentification of class boundaries. The cross-condition unsupervised experimental results on both the constructed planetary gearbox test rig dataset and the public bearing dataset show that the diagnostic accuracy generally exceeds 95% in multiple cross-condition transfer tasks. Compared with several unsupervised domain adaptation methods， the proposed method achieves a significant performance improvement， validating its effectiveness and superiority in small-sample unsupervised cross-condition fault diagnosis.

Key words: fault diagnosis, contrastive learning, small sample, unsupervised domain adaptation

摘要：

针对故障诊断中小样本条件下的数据标注缺失及跨域应用中的域偏移问题，提出了一种融合对比学习的域适应策略。通过层级化特征对齐机制，联合优化域级对抗适应与类级对比学习，兼顾域不变特征迁移与类判别性保持，提高目标域故障分类精度。构建一维域共享卷积神经网络提取源域和目标域数据的共性特征，结合无监督域判别损失与梯度反转层优化域级适配，增强域不变特性的可迁移性。设计无监督对比损失函数优化类特定信息，在特征空间内约束类内紧凑性与类间可分性，实现有限样本下类级对齐，有效抑制类边界误识别。在搭建的行星齿轮箱试验台数据集和公开轴承数据集上的跨工况无监督实验结果表明，在多个跨工况迁移任务中，诊断准确率普遍超过95%。所提方法相较于几种无监督域适应方法取得了明显的性能改进，验证了该方法在小样本无监督跨工况故障诊断中的有效性与优越性。

关键词: 故障诊断, 对比学习, 小样本, 无监督域适应

CLC Number:

TH17

QIAO Wan, LIU Xiuli, LI Yijian, WU Guoxin. Unsupervised Cross-domain Fault Diagnosis Method under Small Sample Conditions[J]. China Mechanical Engineering, 2026, 37(9): 2081-2091.

乔万, 刘秀丽, 栗祎健, 吴国新. 小样本无监督跨域故障诊断方法[J]. 中国机械工程, 2026, 37(9): 2081-2091.

Figures/Tables 19

References 27

[1]	WANG Xin， MAO Dongxing， LI Xiaodong. Bearing Fault Diagnosis Based on Vibro-acoustic Data Fusion and 1D-CNN Network［J］. Measurement， 2021， 173： 108518.
[2]	BAI Ruxue， XU Quansheng， MENG Zong， et al. Rolling Bearing Fault Diagnosis Based on Multi-channel Convolution Neural Network and Multi-scale Clipping Fusion Data Augmentation［J］. Measurement， 2021， 184： 109885.
[3]	LI Weihua， CHEN Zhuyun， HE Guolin. A Novel Weighted Adversarial Transfer Network for Partial Domain Fault Diagnosis of Machinery［J］. IEEE Transactions on Industrial Informatics， 2021， 17（3）： 1753-1762.
[4]	CHEN Xiaohan， YANG Rui， XUE Yihao， et al. Deep Transfer Learning for Bearing Fault Diagnosis： a Systematic Review since 2016［J］. IEEE Transactions on Instrumentation and Measurement， 2023， 72： 3508221.
[5]	潘林. 频谱分析法在齿轮故障诊断中的应用［J］. 石油化工设备技术， 2019， 40（1）： 19-21.
	PAN Lin. Application of Spectral Analysis Method in Gear Fault Diagnosis［J］. Petrochemical Equipment Technology， 2019， 40（1）： 19-21.
[6]	ZHOU Peng， CHEN Shiqian， HE Qingbo， et al. Rotating Machinery Fault-induced Vibration Signal Modulation Effects： a Review with Mechanisms， Extraction Methods and Applications for Diagnosis［J］. Mechanical Systems and Signal Processing， 2023， 200： 110489.
[7]	GAWDE S， PATIL S， KUMAR S， et al. Multi-fault Diagnosis of Industrial Rotating Machines Using Data-driven Approach： a Review of Two Decades of Research［J］. Engineering Applications of Artificial Intelligence， 2023， 123： 106139.
[8]	YANG Bin， LEI Yaguo， JIA Feng， et al. A Polynomial Kernel Induced Distance Metric to Improve Deep Transfer Learning for Fault Diagnosis of Machines［J］. IEEE Transactions on Industrial Electronics， 2020， 67（11）： 9747-9757.
[9]	Pin LYU， CHENG Yihong， ZHANG Ming， et al. GPSC-GAN： a Data Enhanced Model for Intelligent Fault Diagnosis［J］. IEEE Transactions on Instrumentation and Measurement， 2024， 73： 3532116.
[10]	TIAN He， LIANG Chao， MA Wenpeng， et al. Fault Diagnosis of Magnetically Controlled On-column Circuit Breaker Based on Small Sample Condition［J］. IEEE Access， 2025， 13： 18457-18469.
[11]	张笑璐，邹益胜，曾大懿，等. 样本不均衡下的DCGAN轴承故障诊断方法［J］. 机械科学与技术， 2022， 41（1）： 9-15.
	ZHANG Xiaolu， ZOU Yisheng， ZENG Dayi， et al. DCGAN Bearing Fault Diagnosis Method under Unbalanced Samples［J］. Mechanical Science and Technology for Aerospace Engineering， 2022， 41（1）： 9-15.
[12]	SUN Shilin， HU Wenyang， LIU Yuekai， et al. Matching Contrastive Learning： an Effective and Intelligent Method for Wind Turbine Fault Diagnosis with Imbalanced SCADA Data［J］. Expert Systems with Applications， 2023， 223： 119891.
[13]	PENG Peng， LU Jiaxun， XIE Tingyu， et al. Open-set Fault Diagnosis via Supervised Contrastive Learning with Negative Out-of-distribution Data Augmentation［J］. IEEE Transactions on Industrial Informatics， 2023， 19（3）： 2463-2473.
[14]	LE-KHAC P H， HEALY G， SMEATON A F. Contrastive Representation Learning： a Framework and Review［J］. IEEE Access， 2020， 8： 193907-193934.
[15]	CUI Long， TIAN Xincheng， WEI Qingzhe， et al. A Self-attention Based Contrastive Learning Method for Bearing Fault Diagnosis［J］. Expert Systems with Applications， 2024， 238： 121645.
[16]	ZHANG Weiwei， CHEN Deji， XIAO Yang， et al. Semi-supervised Contrast Learning Based on Multiscale Attention and Multitarget Contrast Learning for Bearing Fault Diagnosis［J］. IEEE Transactions on Industrial Informatics， 2023， 19（10）： 10056-10068.
[17]	SHANG Ronghua， KONG Jiarui， FENG Jie， et al. Feature Selection via Non-convex Constraint and Latent Representation Learning with Laplacian Embedding［J］. Expert Systems with Applications， 2022， 208： 118179.
[18]	乔万，刘秀丽，吴国新，等. 时变小样本条件下基于对比学习的故障诊断［J］. 电子测量与仪器学报 2024， 38（8）： 113-123.
	QIAO Wan， LIU Xiuli， WU Guoxin， et al. Fault Diagnosis Based on Contrastive Learning under Time-varying Small Sample Conditions［J］. Journal of Electronic Measurement and Instrumentation， 2024， 38（8）： 113-123.
[19]	QIAO Wan， LIU Xiuli， HUANG Jinpeng， et al. A Prior Knowledge Embedding Contrastive Attention Learning Network for Variable Working Conditions Bearing Fault Diagnosis with Small Samples［J］. IEEE Sensors Journal， 2024， 24（23）： 39967-39980.
[20]	CHEN Ting， KORNBLITH S， NOROUZI M， et al. A Simple Framework for Contrastive Learning of Visual Representations［C］∥International Conference on Machine Learning. Online， 2020： 1597-1607.
[21]	YAN Ruqiang， SHEN Fei， SUN Chuang， et al. Knowledge Transfer for Rotary Machine Fault Diagnosis［J］. IEEE Sensors Journal， 2020， 20（15）： 8374-8393.
[22]	DI Ziyang， SHAO Haidong， XIANG Jiawei. Ensemble Deep Transfer Learning Driven by Multisensor Signals for the Fault Diagnosis of Bevel-gear Cross-operation Conditions［J］. Science China Technological Sciences， 2021， 64（3）： 481-492.
[23]	LI Weihua， HUANG Ruyi， LI Jipu， et al. A Perspective Survey on Deep Transfer Learning for Fault Diagnosis in Industrial Scenarios： Theories， Applications and Challenges［J］. Mechanical Systems and Signal Processing， 2022， 167： 108487.
[24]	GANIN Y， LEMPITSKY V S. Unsupervised Domain Adaptation by Backpropagation［C］∥Proceedings of the 31st International Conference on Machine Learning. Beijing， 2014： 867-874.
[25]	TZENG E， HOFFMAN J， ZHANG Ning， et al. Deep Domain Confusion： Maximizing for Domain Invariance［C］∥Advances in Neural Information Processing Systems. Montreal， 2014： 1-9.
[26]	SUN Baochen， SAENKO K. Deep CORAL： Correlation Alignment for Deep Domain Adaptation［C］∥Computer Vision – ECCV 2016 Workshops. Amsterdam， 2016： 443-450.
[27]	BERTHELOT D， ROELOFS R， SOHN K， et al. AdaMatch： a Unified Approach to Semi-supervised Learning and Domain Adaptation［C］∥International Conference on Learning Representations.Online， 2022：1-50.

网络层	通道	核尺寸	激活函数
Input	1
Convolution 1	32	12	ReLU
Batch Norm	32
MaxPooling 1		2
Convolution 2	64	3	ReLU
Batch Norm	64
MaxPooling 2		2
Convolution 3	64	3	ReLU
Batch Norm	64
MaxPooling 3		2
Adaptive AvgPooling	64

网络层	通道	核尺寸	激活函数
Input	1
Convolution 1	32	12	ReLU
Batch Norm	32
MaxPooling 1		2
Convolution 2	64	3	ReLU
Batch Norm	64
MaxPooling 2		2
Convolution 3	64	3	ReLU
Batch Norm	64
MaxPooling 3		2
Adaptive AvgPooling	64

齿轮	齿数	模数/mm	压力角/（°）
行星轮	44（3）	0.5	20
太阳轮	18	0.5	20
内齿圈	108	0.5	20

齿轮	齿数	模数/mm	压力角/（°）
行星轮	44（3）	0.5	20
太阳轮	18	0.5	20
内齿圈	108	0.5	20

数据集	工况	转速/负载	数据长度	故障种类
行星齿轮箱	A	1140 r/min	1024	7
	B	1260 r/min	1024	7
	C	1380 r/min	1024	7
CWRU轴承	A	0 kW	1024	10
	B	0.7355 kW	1024	10
	C	1.471 kW	1024	10
	D	3.6775 kW	1024	10