Applications of machine learning to machine fault diagnosis: A review and roadmap
Intelligent fault diagnosis (IFD) refers to applications of machine learning theories to
machine fault diagnosis. This is a promising way to release the contribution from human …
machine fault diagnosis. This is a promising way to release the contribution from human …
A comprehensive review on convolutional neural network in machine fault diagnosis
With the rapid development of manufacturing industry, machine fault diagnosis has become
increasingly significant to ensure safe equipment operation and production. Consequently …
increasingly significant to ensure safe equipment operation and production. Consequently …
Intelligent fault diagnosis of machinery using digital twin-assisted deep transfer learning
Digital twin (DT) is emerging as a key technology for smart manufacturing. The high fidelity
DT model of the physical assets can produce system performance data that is close to …
DT model of the physical assets can produce system performance data that is close to …
Deep residual shrinkage networks for fault diagnosis
M Zhao, S Zhong, X Fu, B Tang… - IEEE Transactions on …, 2019 - ieeexplore.ieee.org
This article develops new deep learning methods, namely, deep residual shrinkage
networks, to improve the feature learning ability from highly noised vibration signals and …
networks, to improve the feature learning ability from highly noised vibration signals and …
Federated learning for machinery fault diagnosis with dynamic validation and self-supervision
Intelligent data-driven machinery fault diagnosis methods have been successfully and
popularly developed in the past years. While promising diagnostic performance has been …
popularly developed in the past years. While promising diagnostic performance has been …
Deep-convolution-based LSTM network for remaining useful life prediction
Accurate prediction of remaining useful life (RUL) has been a critical and challenging
problem in the field of prognostics and health management (PHM), which aims to make …
problem in the field of prognostics and health management (PHM), which aims to make …
Understanding and learning discriminant features based on multiattention 1DCNN for wheelset bearing fault diagnosis
Recently, deep-learning-based fault diagnosis methods have been widely studied for rolling
bearings. However, these neural networks are lack of interpretability for fault diagnosis …
bearings. However, these neural networks are lack of interpretability for fault diagnosis …
Machinery fault diagnosis with imbalanced data using deep generative adversarial networks
Despite the recent advances of intelligent data-driven fault diagnosis methods on rotating
machines, balanced training data for different machine health conditions are assumed in …
machines, balanced training data for different machine health conditions are assumed in …
Deep transfer learning based on sparse autoencoder for remaining useful life prediction of tool in manufacturing
Deep learning with ability to feature learning and nonlinear function approximation has
shown its effectiveness for machine fault prediction. While, how to transfer a deep network …
shown its effectiveness for machine fault prediction. While, how to transfer a deep network …
Generative adversarial networks for data augmentation in machine fault diagnosis
Generative adversarial networks (GANs) have been proved to be able to produce artificial
data that are alike the real data, and have been successfully applied to various image …
data that are alike the real data, and have been successfully applied to various image …