A perspective survey on deep transfer learning for fault diagnosis in industrial scenarios: Theories, applications and challenges
Abstract Deep Transfer Learning (DTL) is a new paradigm of machine learning, which can
not only leverage the advantages of Deep Learning (DL) in feature representation, but also …
not only leverage the advantages of Deep Learning (DL) in feature representation, but also …
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 cross-domain rolling bearing fault diagnosis based on time-frequency information fusion
H Tao, J Qiu, Y Chen, V Stojanovic, L Cheng - Journal of the Franklin …, 2023 - Elsevier
In recent years, data-driven methods have been widely used in rolling bearing fault
diagnosis with great success, which mainly relies on the same data distribution and massive …
diagnosis with great success, which mainly relies on the same data distribution and massive …
Digital twin-driven partial domain adaptation network for intelligent fault diagnosis of rolling bearing
Fault diagnosis of rolling bearings has attracted extensive attention in industrial fields, which
plays a vital role in guaranteeing the reliability, safety, and economical efficiency of …
plays a vital role in guaranteeing the reliability, safety, and economical efficiency of …
A multi-source weighted deep transfer network for open-set fault diagnosis of rotary machinery
In real industries, there often exist application scenarios where the target domain holds fault
categories never observed in the source domain, which is an open-set domain adaptation …
categories never observed in the source domain, which is an open-set domain adaptation …
[HTML][HTML] Potential, challenges and future directions for deep learning in prognostics and health management applications
Deep learning applications have been thriving over the last decade in many different
domains, including computer vision and natural language understanding. The drivers for the …
domains, including computer vision and natural language understanding. The drivers for the …
Intelligent fault diagnosis by fusing domain adversarial training and maximum mean discrepancy via ensemble learning
Y Li, Y Song, L Jia, S Gao, Q Li… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
Nowadays, the industrial Internet of Things (IIoT) has been successfully utilized in smart
manufacturing. The massive amount of data in IIoT promote the development of deep …
manufacturing. The massive amount of data in IIoT promote the development of deep …
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 …
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 …
Bearing fault detection and diagnosis using case western reserve university dataset with deep learning approaches: A review
A smart factory is a highly digitized and connected production facility that relies on smart
manufacturing. Additionally, artificial intelligence is the core technology of smart factories …
manufacturing. Additionally, artificial intelligence is the core technology of smart factories …