Big data analytics for intelligent manufacturing systems: A review
With the development of Internet of Things (IoT), 5 G, and cloud computing technologies, the
amount of data from manufacturing systems has been increasing rapidly. With massive …
amount of data from manufacturing systems has been increasing rapidly. With massive …
Intelligent fault diagnosis of machines with small & imbalanced data: A state-of-the-art review and possible extensions
The research on intelligent fault diagnosis has yielded remarkable achievements based on
artificial intelligence-related technologies. In engineering scenarios, machines usually work …
artificial intelligence-related technologies. In engineering scenarios, machines usually work …
Unsupervised domain-share CNN for machine fault transfer diagnosis from steady speeds to time-varying speeds
The existing deep transfer learning-based intelligent fault diagnosis studies for machinery
mainly consider steady speed scenarios, and there exists a problem of low diagnosis …
mainly consider steady speed scenarios, and there exists a problem of low diagnosis …
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 …
Deep learning algorithms for rotating machinery intelligent diagnosis: An open source benchmark study
Rotating machinery intelligent diagnosis based on deep learning (DL) has gone through
tremendous progress, which can help reduce costly breakdowns. However, different …
tremendous progress, which can help reduce costly breakdowns. However, different …
Imbalanced fault diagnosis of rolling bearing using improved MsR-GAN and feature enhancement-driven CapsNet
Traditional fault diagnosis approaches of rolling bearing often need abundant labeled data
in advance while some certain fault data are difficult to be acquired in engineering …
in advance while some certain fault data are difficult to be acquired in engineering …
Data synthesis using deep feature enhanced generative adversarial networks for rolling bearing imbalanced fault diagnosis
S Liu, H Jiang, Z Wu, X Li - Mechanical Systems and Signal Processing, 2022 - Elsevier
Rolling bearing fault diagnosis is of great significance to the stable operation of rotating
machinery systems. However, the fault data collected in practical engineering is seriously …
machinery systems. However, the fault data collected in practical engineering is seriously …
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 novel complexity-based mode feature representation for feature extraction of ship-radiated noise using VMD and slope entropy
Y Li, B Tang, Y Yi - Applied Acoustics, 2022 - Elsevier
To extract more distinguishing features of ships, slope entropy (SloE) is introduced into
underwater acoustic signal processing as a new feature to analyze ship-radiated noise …
underwater acoustic signal processing as a new feature to analyze ship-radiated noise …
Development of intelligent fault-tolerant control systems with machine learning, deep learning, and transfer learning algorithms: a review
Abstract Intelligent Fault-Tolerant Control (IFTC) refers to the applications of machine
learning algorithms for fault diagnosis and design of Fault-Tolerant Control (FTC). The …
learning algorithms for fault diagnosis and design of Fault-Tolerant Control (FTC). The …