Detection of compound faults in ball bearings using multiscale-SinGAN, heat transfer search optimization, and extreme learning machine
Intelligent fault diagnosis gives timely information about the condition of mechanical
components. Since rolling element bearings are often used as rotating equipment parts, it is …
components. Since rolling element bearings are often used as rotating equipment parts, it is …
Stock price prediction using a frequency decomposition based GRU transformer neural network
C Li, G Qian - Applied Sciences, 2022 - mdpi.com
Stock price prediction is crucial but also challenging in any trading system in stock markets.
Currently, family of recurrent neural networks (RNNs) have been widely used for stock …
Currently, family of recurrent neural networks (RNNs) have been widely used for stock …
Deep and hybrid learning technique for early detection of tuberculosis based on X-ray images using feature fusion
Tuberculosis (TB) is a fatal disease in developing countries, with the infection spreading
through direct contact or the air. Despite its seriousness, the early detection of tuberculosis …
through direct contact or the air. Despite its seriousness, the early detection of tuberculosis …
Fault severity classification of ball bearing using SinGAN and deep convolutional neural network
Condition monitoring and diagnosis of a bearing are very important for any rotating machine
as it governs the safety while the machine is in operating condition. To construct a feature …
as it governs the safety while the machine is in operating condition. To construct a feature …
[HTML][HTML] Experimental investigations and prediction of WEDMed surface of Nitinol SMA using SinGAN and DenseNet deep learning model
Shape memory alloys (SMA) hold a very promising place in the field of manufacturing,
especially in biomedical and aerospace applications. Owing to the unique and favorable …
especially in biomedical and aerospace applications. Owing to the unique and favorable …
Bandgap prediction of metal halide perovskites using regression machine learning models
Organometal halide perovskites represent a type of nanomaterials, which are extensively
used in solar cells, light-emitting diodes, detectors and memristors due to their outstanding …
used in solar cells, light-emitting diodes, detectors and memristors due to their outstanding …
A comparative study to predict bearing degradation using discrete wavelet transform (DWT), tabular generative adversarial networks (TGAN) and machine learning …
Prognostics and health management (PHM) is a framework to identify damage prior to its
occurrence which leads to the reduction of both maintenance costs and safety hazards …
occurrence which leads to the reduction of both maintenance costs and safety hazards …
A feature selection approach hybrid grey wolf and heap-based optimizer applied in bearing fault diagnosis
CY Lee, TA Le, YT Lin - IEEE Access, 2022 - ieeexplore.ieee.org
An effective bearing fault diagnosis model based on machine learning is proposed in this
study. The model can separate into three stages: feature extraction, feature selection, and …
study. The model can separate into three stages: feature extraction, feature selection, and …
Multisensor-driven motor fault diagnosis method based on visual features
Generalization ability is a critical property for practical motor fault diagnosis (FD). By
converting time-series to images, several studies have made certain achievements …
converting time-series to images, several studies have made certain achievements …
Fast robust capsule network with dynamic pruning and multiscale mutual information maximization for compound-fault diagnosis
H Chen, X Wang, ZX Yang - IEEE/ASME Transactions on …, 2022 - ieeexplore.ieee.org
Rotating machinery, such as ventilators and water pumps, are crucial components in
modern industry, of which safety monitoring requires intelligent fault diagnosis. Feature …
modern industry, of which safety monitoring requires intelligent fault diagnosis. Feature …