Improved Shift-Invariant Sparse Parsing of Mechanical Fault Based on Feature Atom

Han, Changkun; Lu, Wei; Cui, Lingli; Song, Liuyang; Wang, Huaqing

doi:10.1109/tim.2024.3436126

IEEE Trans. Instrum. Meas.

2024

DOI: 10.1109/tim.2024.3436126

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Improved Shift-Invariant Sparse Parsing of Mechanical Fault Based on Feature Atom

Changkun Han,

Wei Lu,

Lingli Cui

et al.

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Cited by 6 publications

References 47 publications

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A personalized federated meta-learning method for intelligent and privacy-preserving fault diagnosis

Zhang,

Li,

Han

et al. 2024

Advanced Engineering Informatics

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A personalized federated meta-learning method for intelligent and privacy-preserving fault diagnosis

Zhang,

Li,

Han

et al. 2024

Advanced Engineering Informatics

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Sparse graph structure fusion convolutional network for machinery remaining useful life prediction

Cui,

Shen,

Xiao

et al. 2025

Reliability Engineering & System Safety

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Federated transfer learning-based distributed fault diagnosis method for rolling bearings

Yang,

Su,

et al. 2024

Meas. Sci. Technol.

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Current methods for bearing fault diagnosis often fall short in addressing data privacy concerns and typically rely on one-to-one transfer strategies, which are inadequate for achieving knowledge transfer in distributed environments. To address this issue, a distributed fault diagnosis method for rolling bearings based on federated transfer learning is proposed. This method ensures data privacy while integrating fault knowledge from multiple domains, thereby enabling more efficient knowledge transfer. Specifically, a domain adversarial neural network (DANN) is introduced as the base model within the federated learning framework. Additionally, maximum mean discrepancy (MMD) is incorporated into the DANN to enhance the transfer of fault knowledge. Finally, a dynamic weighting parameter update method based on MMD is designed to evaluate the feature discrepancies between source and target domains, thereby updating the parameters of the federated framework and achieving global model aggregation. Experimental results on two bearing datasets demonstrate that the proposed method excels in both distribution alignment and fault diagnosis.

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Improved Shift-Invariant Sparse Parsing of Mechanical Fault Based on Feature Atom

Cited by 6 publications

References 47 publications

A personalized federated meta-learning method for intelligent and privacy-preserving fault diagnosis

A personalized federated meta-learning method for intelligent and privacy-preserving fault diagnosis

Sparse graph structure fusion convolutional network for machinery remaining useful life prediction

Federated transfer learning-based distributed fault diagnosis method for rolling bearings

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