Cross-domain Emerging Fault Diagnosis of Rotating Machinery Using Source-free Self-supervised Domain Adaptation Network

doi:10.3901/JME.2025.03.067

Abstract

Abstract: With the rapid advancement of next-generation artificial intelligence technologies, data-driven intelligent fault diagnosis methods have found widespread applications in mechanical equipment. High-precision diagnosis of intelligent diagnostic models typically relies on a large volume of labeled data. However, unpredictable new faults may occur during the operation of mechanical equipment, which makes it difficult to adopt the model trained on known samples to accurately identify newly occurring faults. Furthermore, data privacy restricts the accessibility of data, adding substantial complexity to the domain adaptation process for diagnostic models. To address these challenges, a source-free self-supervised domain adaptation network is proposed for cross-domain emerging fault diagnosis of rotating machinery, which enables diagnosis emerging fault in target domain without access to source domain. First, a source domain fault diagnosis model is established using labeled source samples. Subsequently, a self-supervised pseudo-labeling technique based on uncertainty information entropy is utilized to acquire a target domain dataset with high-quality pseudo-labels. Finally, we merge the pseudo-labeled fault dataset with unlabeled fault dataset, then train the model through an adversarial training strategy to realize emerging fault detection. The effectiveness of the proposed method is validated through experiments conducted on an automotive gearbox dataset. Experimental results show that the proposed method can accurately detect emerging faults while maintaining the performance in classifying known fault types.

Key words: intelligent fault diagnosis, source-free domain adaptation, emerging fault, self-supervised learning, transfer learning

CLC Number:

TG156

YUE Ke, LI Jipu, CHEN Zhuyun, HE Guolin, DENG Shuhan, LI Weihua. Cross-domain Emerging Fault Diagnosis of Rotating Machinery Using Source-free Self-supervised Domain Adaptation Network[J]. Journal of Mechanical Engineering, 2025, 61(3): 67-76.

References

[1] 高金吉. 人工自愈概论[J]. 机械工程学报，2021，57(2):1-10. GAO Jinji，Overview on Artificial Self-recovery[J]. Journal of Mechanical Engineering， 2021，57(2):1-10.
[2] 王辉，徐佳文，严如强. 基于多尺度注意力深度强化学习网络的行星齿轮箱智能诊断方法[J]. 机械工程学报，2022，58(11):133-142. WANG Hui， XU Jiawen， YAN Ruqiang. Multi-scale attention based deep reinforcement learning for intelligent fault diagnosis of planetary gearbox[J]. Journal of Mechanical Engineering， 2022，58(11):133-142.
[3] 邵海东，林健，闵志闪，等. 分布外样本干扰下基于改进半监督原型网络的齿轮箱跨域故障诊断[J]. 机械工程学报:2024，60(4):212-221. SHAO Haidong， LIN Jian， MIN Zhishan， et al， Improved semi-supervised prototype network for cross-domain fault diagnosis of gearbox under out-of-distribution interference samples[J]. Journal of Mechanical Engineering，2024，60(4):212-221.
[4] XU Yadong，FENG Ke，YAN Xiaoan，et al. Cross-modal fusion convolutional neural networks with online soft label training strategy for mechanical fault diagnosis[J]. IEEE Transactions on Industrial Informatics，2024，20(1):73-84.
[5] 雷亚国，杨彬，李乃鹏，等. 跨设备的机械故障靶向迁移诊断方法[J]. 机械工程学报，2022，58(12):1-9. LEI Yaguo， YANG Bin， LI Naipeng， et al. Targeted transfer diagnosis method across different machines[J]. Journal of Mechanical Engineering，2022，58(12):1-9.
[6] ZHANG Liangwei，FAN Qi，LIN Jing，et al. A nearly end-to-end deep learning approach to fault diagnosis of wind turbine gearboxes under nonstationary conditions[J]. Engineering Applications of Artificial Intelligence，2023， 119:105735.
[7] SHI Mingkuan，DING Chuancang，WANG Rui，et al. Deep hypergraph autoencoder embedding:An efficient intelligent approach for rotating machinery fault diagnosis[J]. Knowledge-Based Systems，2023，260: 110172.
[8] PENG Binsen，XIA Hong，LÜ Xinzhi，et al. An intelligent fault diagnosis method for rotating machinery based on data fusion and deep residual neural network[J]. Applied Intelligence，2022，52(3):3051-3065.
[9] MIAN T，CHOUDHARY A，FATIMA S. Multi-sensor fault diagnosis for misalignment and unbalance detection using machine learning[J]. IEEE Transactions on Industry Applications，2023，59(5):5749-5759.
[10] TANG Shengnan，ZHU Yong，YUAN Shouqi. Intelligent fault diagnosis of hydraulic piston pump based on deep learning and Bayesian optimization[J]. ISA Transactions， 2022，129:555-563.
[11] 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.
[12] ZHANG Wei，LI Xiang，MA Hui，et al. Open-set domain adaptation in machinery fault diagnostics using instance-level weighted adversarial learning[J]. IEEE Transactions on Industrial Informatics，2021，17(11):7445-7455.
[13] 陈祝云，林慧斌，夏景演，等.基于选择性加权适配网络的多域新故障识别方法[J]. 仪器仪表学报，2022，43(10):270-279. CHEN Zhuyun， LIN Huibin， XIA Jingyan，et al. Selective weighted adaptive network for multi-domainemerging fault identification[J]. Chinese Journal of Scientific Instrument，2022，43(10):270-279.
[14] WEN Long，SU Shaoquan，WANG Bin，et al. A new multi-sensor fusion with hybrid convolutional neural network with wiener model for remaining useful life estimation[J]. Engineering Applications of Artificial Intelligence，2023，126:106934.
[15] 李响，付春霖，雷亚国，等. 保证数据隐私的装备协同智能故障诊断联邦迁移学习方法[J]. 机械工程学报，2023，59(6):1-9. LI Xiang，FU Chunlin，LEI Yaguo，et al. Federated transfer learning method for privacy-preserving collaborative intelligent machinery fault diagnostics[J]. Journal of Mechanical Engineering，2023，59(6):1-9.
[16] YUE Ke，LI Jipu，CHEN Zhuyun，et al. Multiple source-free domain adaptation network based on knowledge distillation for machinery fault diagnosis[J]. IEEE Transactions on Instrumentation and Measurement， 2023，72:3523111.
[17] KRAUSE A， PERONA P， GOMES R. Discriminative clustering by regularized information maximization[J]. Advances in Neural Information Processing Systems， 2010，23:775-783.
[18] SAITO K，YAMAMOTO S，USHIKU Y，et al. Open set domain adaptation by backpropagation[C]//Proceedings of the European conference on computer vision (ECCV). 2018:153-168.
[19] HAN Te，LIU Chao，YANG Wenguang，et al. A novel adversarial learning framework in deep convolutional neural network for intelligent diagnosis of mechanical faults[J]. Knowledge-Based Systems， 2019，165:474-487.
[20] LI Jipu，HUANG Ruyi，HE Guolin，et al. A deep adversarial transfer learning network for machinery emerging fault detection[J]. IEEE Sensors Journal，2020，20(15):8413-8422.
[21] LIANG Jian，HU Dapeng，FENG Jiashi. Do we really need to access the source data? source hypothesis transfer for unsupervised domain adaptation[C]//International conference on machine learning. PMLR，2020:6028-6039.