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 …
Fault detection and diagnosis for rotating machinery: A model based on convolutional LSTM, Fast Fourier and continuous wavelet transforms
Abstract Fault Detection and Diagnosis (FDD) of rotating machinery plays a key role in
reducing the maintenance costs of the manufacturing systems. How to improve the FDD …
reducing the maintenance costs of the manufacturing systems. How to improve the FDD …
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 …
A systematic review of deep transfer learning for machinery fault diagnosis
With the popularization of the intelligent manufacturing, much attention has been paid in
such intelligent computing methods as deep learning ones for machinery fault diagnosis …
such intelligent computing methods as deep learning ones for machinery fault diagnosis …
A hybrid generalization network for intelligent fault diagnosis of rotating machinery under unseen working conditions
The data-driven methods in machinery fault diagnosis have become increasingly popular in
the past two decades. However, the wide applications of this scheme are generally …
the past two decades. However, the wide applications of this scheme are generally …
Bearing fault diagnosis method based on adaptive maximum cyclostationarity blind deconvolution
Z Wang, J Zhou, W Du, Y Lei, J Wang - Mechanical Systems and Signal …, 2022 - Elsevier
Blind deconvolution has been proved to be an effective method for fault detection since it
can recover periodic impulses from mixed fault signals convoluted by noise and periodic …
can recover periodic impulses from mixed fault signals convoluted by noise and periodic …
Autoencoder-based representation learning and its application in intelligent fault diagnosis: A review
Z Yang, B Xu, W Luo, F Chen - Measurement, 2022 - Elsevier
With the increase of the scale and complexity of mechanical equipment, traditional intelligent
fault diagnosis (IFD) based on shallow machine learning methods is unable to meet the …
fault diagnosis (IFD) based on shallow machine learning methods is unable to meet the …
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 …
A new deep auto-encoder method with fusing discriminant information for bearing fault diagnosis
In recent years, deep learning techniques have been proved a promising tool for bearing
fault diagnosis. However, to extract deep features with better representative ability, how to …
fault diagnosis. However, to extract deep features with better representative ability, how to …
Deep learning for prognostics and health management: State of the art, challenges, and opportunities
B Rezaeianjouybari, Y Shang - Measurement, 2020 - Elsevier
Improving the reliability of engineered systems is a crucial problem in many applications in
various engineering fields, such as aerospace, nuclear energy, and water declination …
various engineering fields, such as aerospace, nuclear energy, and water declination …