Neural-transformer: A brain-inspired lightweight mechanical fault diagnosis method under noise
Recently, as a representative of deep learning methods, Transformers have shown great
prowess in intelligent fault diagnosis, offering powerful feature extraction and modeling …
prowess in intelligent fault diagnosis, offering powerful feature extraction and modeling …
Multi-domain Class-imbalance Generalization with Fault Relationship-induced Augmentation for Intelligent Fault Diagnosis
Domain generalization-based fault diagnosis (DGFD) has attracted considerable attention
due to its potential to extend diagnostic knowledge to previously unseen operational …
due to its potential to extend diagnostic knowledge to previously unseen operational …
A Bayesian adversarial probsparse Transformer model for long-term remaining useful life prediction
Long-term remaining useful life (RUL) prediction is essential for the maintenance of safety-
crucial engineering assets. Deep learning (DL) models, especially Transformer-based …
crucial engineering assets. Deep learning (DL) models, especially Transformer-based …
CIS2N: Causal independence and sparse shift network for rotating machinery fault diagnosis in unseen domains
Intelligent fault diagnosis (IFD) based on deep learning (DL) has demonstrated its powerful
performance to promote the reliability and safe operation of rotating machinery. In industrial …
performance to promote the reliability and safe operation of rotating machinery. In industrial …
Multi-stream domain adversarial prototype network for integrated smart roller TBM main bearing fault diagnosis across various low rotating speeds
Highlights•The TBM main bearing fault diagnosis algorithm applies to low-speed, few fault
samples, and variable working conditions.•Developed smart rollers integrate vibration …
samples, and variable working conditions.•Developed smart rollers integrate vibration …
Cross-Supervised multisource prototypical network: A novel domain adaptation method for multi-source few-shot fault diagnosis
Multi-source domain adaptation (MSDA) has demonstrated superior performance in
intelligent fault diagnosis (IFD) compared to single-source domain adaptation (SSDA), as it …
intelligent fault diagnosis (IFD) compared to single-source domain adaptation (SSDA), as it …
A Semi-supervised Gaussian Mixture Variational Autoencoder method for few-shot fine-grained fault diagnosis
Z Zhao, Y Xu, J Zhang, R Zhao, Z Chen, Y Jiao - Neural Networks, 2024 - Elsevier
In practical engineering, obtaining labeled high-quality fault samples poses challenges.
Conventional fault diagnosis methods based on deep learning struggle to discern the …
Conventional fault diagnosis methods based on deep learning struggle to discern the …
Forecasting battery degradation trajectory under domain shift with domain generalization
Rechargeable batteries play a pivotal role in the carbon-neutral green environment by
electrifying transportation and mitigating the intermittency of renewable energies …
electrifying transportation and mitigating the intermittency of renewable energies …
Prior knowledge embedding convolutional autoencoder: A single-source domain generalized fault diagnosis framework under small samples
The proposed transfer learning-based fault diagnosis models have achieved good results in
multi-source domain generalization (MDG) tasks. However, research on single-source …
multi-source domain generalization (MDG) tasks. However, research on single-source …
Domain generalization for rotating machinery real-time remaining useful life prediction via multi-domain orthogonal degradation feature exploration
The domain adaptation-based approach for remaining useful life (RUL) prediction has
gained significant attention in addressing the challenges of cross-domain RUL prediction …
gained significant attention in addressing the challenges of cross-domain RUL prediction …