Attention mechanism in intelligent fault diagnosis of machinery: A review of technique and application
Attention Mechanism has become very popular in the field of mechanical fault diagnosis in
recent years and has become an important technique for scholars to study and apply. The …
recent years and has become an important technique for scholars to study and apply. The …
A review on multiscale-deep-learning applications
In general, most of the existing convolutional neural network (CNN)-based deep-learning
models suffer from spatial-information loss and inadequate feature-representation issues …
models suffer from spatial-information loss and inadequate feature-representation issues …
[HTML][HTML] A hybrid prognosis scheme for rolling bearings based on a novel health indicator and nonlinear Wiener process
This paper proposes a novel hybrid method aiming at the fault prognosis of bearings. A
nonlinear health indicator (HI) is first constructed using Complete Ensemble Empirical Mode …
nonlinear health indicator (HI) is first constructed using Complete Ensemble Empirical Mode …
Subdomain adaptation transfer learning network for fault diagnosis of roller bearings
Due to the data distribution discrepancy, fault diagnosis models, trained with labeled data in
one scene, likely fails in classifying by unlabeled data acquired from the other scenes …
one scene, likely fails in classifying by unlabeled data acquired from the other scenes …
Multimodal fusion convolutional neural network with cross-attention mechanism for internal defect detection of magnetic tile
H Lu, Y Zhu, M Yin, G Yin, L Xie - IEEE Access, 2022 - ieeexplore.ieee.org
The internal defect detection of magnetic tile is extremely significant before mounting.
Currently, this task is completely realized by manual operation in the magnetic tile …
Currently, this task is completely realized by manual operation in the magnetic tile …
Bi-LSTM-based two-stream network for machine remaining useful life prediction
In industry, prognostics and health management (PHM) is used to improve the system
reliability and efficiency. In PHM, remaining useful life (RUL) prediction plays a key role in …
reliability and efficiency. In PHM, remaining useful life (RUL) prediction plays a key role in …
A new convolutional dual-channel Transformer network with time window concatenation for remaining useful life prediction of rolling bearings
L Jiang, T Zhang, W Lei, K Zhuang, Y Li - Advanced Engineering …, 2023 - Elsevier
Deep learning has achieved numerous breakthroughs in bearing predicting remaining
useful life (RUL). However, the current mainstream deep learning framework inevitably has …
useful life (RUL). However, the current mainstream deep learning framework inevitably has …
A difference attention ResNet-LSTM network for epileptic seizure detection using EEG signal
X Qiu, F Yan, H Liu - Biomedical Signal Processing and Control, 2023 - Elsevier
Epileptic seizures can affect the patient's physical function and cause irreversible damage to
their brain. It is vital to detect epilepsy seizures in time and give patients antiepileptic …
their brain. It is vital to detect epilepsy seizures in time and give patients antiepileptic …
Temporal self-attention-based Conv-LSTM network for multivariate time series prediction
E Fu, Y Zhang, F Yang, S Wang - Neurocomputing, 2022 - Elsevier
Time series play an important role in many fields, such as industrial control, automated
monitoring, and weather forecasting. Because there is often more than one variable in reality …
monitoring, and weather forecasting. Because there is often more than one variable in reality …
RUL prediction of machinery using convolutional-vector fusion network through multi-feature dynamic weighting
Based on the features extracted from the condition monitoring data, data-driven prognostic
approaches are able to predict the remaining useful life (RUL) of machinery. Existing …
approaches are able to predict the remaining useful life (RUL) of machinery. Existing …