Collaborative fault diagnosis of rotating machinery via dual adversarial guided unsupervised multi-domain adaptation network
Most of the existing research on unsupervised cross-domain intelligent fault diagnosis is
based on single-source domain adaptation, which fails to simultaneously utilize various …
based on single-source domain adaptation, which fails to simultaneously utilize various …
Maximum mean square discrepancy: a new discrepancy representation metric for mechanical fault transfer diagnosis
Discrepancy representation metric completely determines the transfer diagnosis
performance of deep domain adaptation methods. Maximum mean discrepancy (MMD) …
performance of deep domain adaptation methods. Maximum mean discrepancy (MMD) …
Transfer learning for prognostics and health management: Advances, challenges, and opportunities
As failure data is usually scarce in practice upon preventive maintenance strategy in
prognostics and health management (PHM) domain, transfer learning provides a …
prognostics and health management (PHM) domain, transfer learning provides a …
IDSN: A one-stage interpretable and differentiable STFT domain adaptation network for traction motor of high-speed trains cross-machine diagnosis
C He, H Shi, J Li - Mechanical Systems and Signal Processing, 2023 - Elsevier
A surge of transfer fault diagnosis techniques has been proposed to guarantee the safe
operation of traction motor systems. However, existing efforts highly depend on the …
operation of traction motor systems. However, existing efforts highly depend on the …
Digital twin enabled domain adversarial graph networks for bearing fault diagnosis
K Feng, Y Xu, Y Wang, S Li, Q Jiang… - … on Industrial Cyber …, 2023 - ieeexplore.ieee.org
The fault diagnosis of rolling bearings is of utmost importance in industrial applications to
ensure mechanical systems' reliability, safety, and economic viability. However …
ensure mechanical systems' reliability, safety, and economic viability. However …
A graph-guided collaborative convolutional neural network for fault diagnosis of electromechanical systems
Collaborative fault diagnosis has become a hot research topic in fault detection and
identification, greatly benefiting from emerging multisensory fusion techniques and newly …
identification, greatly benefiting from emerging multisensory fusion techniques and newly …
A federated transfer learning method with low-quality knowledge filtering and dynamic model aggregation for rolling bearing fault diagnosis
Intelligent mechanical fault diagnosis techniques have been extensively developed in recent
years. Owing to the advantage of data privacy protection, federated learning has recently …
years. Owing to the advantage of data privacy protection, federated learning has recently …
Adaptive intermediate class-wise distribution alignment: a universal domain adaptation and generalization method for machine fault diagnosis
Many transfer learning methods have been proposed to implement fault transfer diagnosis,
and their loss functions are usually composed of task-related losses, distribution distance …
and their loss functions are usually composed of task-related losses, distribution distance …
A partial domain adaptation scheme based on weighted adversarial nets with improved CBAM for fault diagnosis of wind turbine gearbox
Y Zhu, Y Pei, A Wang, B Xie, Z Qian - Engineering Applications of Artificial …, 2023 - Elsevier
Most domain adaptation methods for fault diagnosis depend heavily on the precondition that
the source and target domain have an identical label space, which is hard to be satisfied in …
the source and target domain have an identical label space, which is hard to be satisfied in …
Interpretable physics-informed domain adaptation paradigm for cross-machine transfer diagnosis
C He, H Shi, X Liu, J Li - Knowledge-Based Systems, 2024 - Elsevier
While transfer learning-based intelligent diagnosis has achieved significant breakthroughs,
the performance of existing well-known methods still needs urgent improvement, given the …
the performance of existing well-known methods still needs urgent improvement, given the …