Federated adversarial domain generalization network: A novel machinery fault diagnosis method with data privacy

Wang, Rui; Huang, Weiguo; Shi, Mingkuan; Wang, Jun; Shen, Changqing; Zhu, Zhongkui

doi:10.1016/j.knosys.2022.109880

Cited by 49 publications

(5 citation statements)

References 24 publications

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“…For example, in 2021, Han et al [17] introduced a combined network founded on DG, using internal and external generalization objectives to regularize the discriminative structure of deep networks. In 2022, Wang et al [18] proposed an adversarial DG method for fault diagnosis, which integrates a generative adversarial network (GANs) that incorporates class information to dynamically generate reference distributions. In 2023, Zhao and Shen [19] introduced a cooperative network for semi-supervised DG in fault diagnosis, which assigns reliable synthetic labels to unlabeled data.…”

Section: Dgmentioning

confidence: 99%

Domain generalized open-set intelligent fault diagnosis based on feature disentanglement meta-learning

Zhou,

Deng,

Liu

et al. 2024

Meas. Sci. Technol.

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Existing domain generalization-based intelligent fault diagnosis methods mainly focus on learning domain-invariant features. However, in practical scenarios, these features are difficult to extract and effectively distinguish from class-related features. Moreover, these methods often assume identical label distributions between the source and target domain, making it challenging to handle scenarios where unknown classes exist in the target domain. To address these issues, this paper proposes a domain generalized open-set intelligent fault diagnosis method based on feature disentanglement meta-learning. A binary mask feature disentanglement module is constructed to overcome the information loss caused by feature reconstruction, enabling the separation of domain-specific and class-related features. Additionally, a meta-purification loss function is defined, incorporating a correlation loss term to remove impurity features from the class-related features, and further purifying class information through feature combination pairing. The method is trained on multiple source domains using a meta-learning strategy and generalized to target domains with unknown classes. The method is utilized for bearing fault diagnosis, designing multi-task experimental scenarios under different rotational speeds, and compared with existing domain generalization methods. Experimental results show that the proposed method exhibits excellent generalization ability and effectively addresses the issue of domain generalized open-set fault diagnosis.

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Section: Dgmentioning

confidence: 99%

Domain generalized open-set intelligent fault diagnosis based on feature disentanglement meta-learning

Zhou,

Deng,

Liu

et al. 2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…However, the alignment of labels can be confusing for multi-classification tasks [15], and the discriminator may still be fooled even if the feature extractor extracts features without domain invariant properties [16]. Wang et al [17] also designed an adversarial learningbased feature alignment module, which is implemented by aligning the real feature distribution of each client to a reference distribution, thus giving the feature extraction the ability to extract domain-invariant features. Compared with other federated transfer learning methods, adversarial learning-based feature transfer achieves satisfactory results with the advantages of strict data privacy protection and consideration of all clients' interests.…”

Section: Ftl-based Fault Diagnosismentioning

confidence: 99%

Open-set federated adversarial domain adaptation based cross-domain fault diagnosis

Song

2023

Meas. Sci. Technol.

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Data-driven fault diagnosis techniques utilizing deep learning have achieved widespread success. However, their diagnostic capability and application possibility are significantly reduced in real-world scenarios where fault modes are not fully covered and labels are lacking. Owing to potential conflicts of interest and legal risks, industrial equipment fault data usually exist in the form of isolated islands, making it difficult to carry out large-scale centralized model training. This paper proposes open-set federated adversarial domain adaptation (OS-FADA) to achieve collaborative evolution of fault diagnosis capabilities among cross-domain data owners while protecting privacy. The OS-FADA is a general fault diagnosis framework that employs two-phase adversarial learning. First, faced with the data distribution shift caused by variable working conditions, a generative adversarial feature extractor training strategy is designed to achieve domain-invariant fault feature extraction by approximating the feature distributions of clients to a unified generated distribution. Second, considering the label distribution shift of unknown faults occurring in the target client, an adversarial learning method is proposed to establish decision boundaries between known and unknown faults. Ultimately, the co-evolution of fault diagnosis models between clients is achieved by combining two-phase adversarial learning and federated aggregation. Results from an industrial gearbox case demonstrate that our proposed method achieves over 20% diagnostic accuracy improvement and has excellent potential for cross-domain fault diagnosis tasks with unknown faults when the data silos problem cannot be ignored.

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“…To address concerns related to data dispersion and privacy protection, federated learning has gained attention from scholars as a promising approach for fault diagnosis in recent years. For instance, Wang et al [31] introduced a federated adversarial domain generalization network for mechanical fault diagnosis. This model achieves collaborative training between a central server and multiple clients, establishing a global fault-diagnosis model while ensuring data privacy.…”

Section: Federated Learning For Fault Diagnosismentioning

confidence: 99%

Federated Few-Shot Learning-Based Machinery Fault Diagnosis in the Industrial Internet of Things

Liang,

Zhao,

Wang

2023

Applied Sciences

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Deep learning has undergone significant progress for machinery fault diagnosis in the Industrial Internet of Things; however, it requires a substantial amount of labeled data. The lack of sufficient fault samples in practical applications remains a challenge. One feasible approach is to leverage prior knowledge from similar source domains to enhance fault diagnosis with limited samples in the target domain. Nevertheless, complex operating conditions and fault types can give rise to domain shift issues between different domains, therefore hindering direct data-sharing due to data privacy concerns. To address these challenges, this article introduces a novel federated few-shot fault-diagnosis method called FedCDAE-MN. FedCDAE-MN employs a convolutional denoising auto-encoder and feature-space metric learning to enhance the model’s generalization across domains for improving the adaptability to varying working conditions, new fault types, and noisy data. Moreover, our approach ensures privacy preservation by avoiding the need to share sensitive data with other participants. Through extensive experiments on real-world datasets, FedCDAE-MN surpasses existing methods and significantly improves the accuracy of fault diagnosis.

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Federated adversarial domain generalization network: A novel machinery fault diagnosis method with data privacy

Cited by 49 publications

References 24 publications

Domain generalized open-set intelligent fault diagnosis based on feature disentanglement meta-learning

Domain generalized open-set intelligent fault diagnosis based on feature disentanglement meta-learning

Open-set federated adversarial domain adaptation based cross-domain fault diagnosis

Federated Few-Shot Learning-Based Machinery Fault Diagnosis in the Industrial Internet of Things

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