Compound fault diagnosis using optimized MCKD and sparse representation for rolling bearings
W Deng, Z Li, X Li, H Chen… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
The effective separation of fault characteristic components is the core of compound fault
diagnosis of rolling bearings. The intelligent optimization algorithm has better global …
diagnosis of rolling bearings. The intelligent optimization algorithm has better global …
Deep network-based maximum correlated kurtosis deconvolution: A novel deep deconvolution for bearing fault diagnosis
Deconvolution methods (DMs) which can adaptively design the filter for the feature
extraction is the most effective tool to counteract the effect of the transmission path …
extraction is the most effective tool to counteract the effect of the transmission path …
Multisource domain feature adaptation network for bearing fault diagnosis under time-varying working conditions
Intelligent fault diagnosis methods based on domain adaptation (DA) have been extensively
employed for tackling domain shift problems, and the basic diagnosis tasks under time …
employed for tackling domain shift problems, and the basic diagnosis tasks under time …
Digital twin aided adversarial transfer learning method for domain adaptation fault diagnosis
J Wang, Z Zhang, Z Liu, B Han, H Bao, S Ji - Reliability Engineering & …, 2023 - Elsevier
Abstract Machine health management has become the focus of equipment monitoring
upgrading with the advance of digital twin (DT). The DT model is able to generate system …
upgrading with the advance of digital twin (DT). The DT model is able to generate system …
A zero-shot fault semantics learning model for compound fault diagnosis
Compound fault diagnosis of bearings has always been a challenge, due to the occurrence
of various faults with randomness and complexity. Existing deep learning-based methods …
of various faults with randomness and complexity. Existing deep learning-based methods …
An improved GNN using dynamic graph embedding mechanism: a novel end-to-end framework for rolling bearing fault diagnosis under variable working conditions
Z Yu, C Zhang, C Deng - Mechanical Systems and Signal Processing, 2023 - Elsevier
Traditional deep learning (DL)-based rolling bearing fault diagnosis methods usually use
signals collected under specific working condition to train the diagnosis models. This may …
signals collected under specific working condition to train the diagnosis models. This may …
A novel generation network using feature fusion and guided adversarial learning for fault diagnosis of rotating machinery
Z Meng, H He, W Cao, J Li, L Cao, J Fan, M Zhu… - Expert Systems with …, 2023 - Elsevier
The imbalanced dataset in actual engineering negatively affects the precision of fault
diagnosis because of the severe lack of collected fault data. To effectively address this issue …
diagnosis because of the severe lack of collected fault data. To effectively address this issue …
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 …
Multistate fault diagnosis strategy for bearings based on an improved convolutional sparse coding with priori periodic filter group
C Han, W Lu, H Wang, L Song, L Cui - Mechanical Systems and Signal …, 2023 - Elsevier
Bearings are a critical component of rotating machines; when they fail, critical equipment
becomes unavailable, damage may occur beyond the bearing itself, and safety concerns …
becomes unavailable, damage may occur beyond the bearing itself, and safety concerns …
Adaptive class center generalization network: A sparse domain-regressive framework for bearing fault diagnosis under unknown working conditions
Fault diagnosis is essential to ensure the bearing safety in smart manufacturing. As the
rotating bearings usually work under variable working conditions, there may exist …
rotating bearings usually work under variable working conditions, there may exist …