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 …
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 …
Applications of unsupervised deep transfer learning to intelligent fault diagnosis: A survey and comparative study
Recent progress on intelligent fault diagnosis (IFD) has greatly depended on deep
representation learning and plenty of labeled data. However, machines often operate with …
representation learning and plenty of labeled data. However, machines often operate with …
Semi-supervised meta-learning networks with squeeze-and-excitation attention for few-shot fault diagnosis
In the engineering practice, lacking of data especially labeled data typically hinders the wide
application of deep learning in mechanical fault diagnosis. However, collecting and labeling …
application of deep learning in mechanical fault diagnosis. However, collecting and labeling …
Novel convolutional neural network (NCNN) for the diagnosis of bearing defects in rotary machinery
A Kumar, G Vashishtha, CP Gandhi… - IEEE Transactions …, 2021 - ieeexplore.ieee.org
This work presents the development of novel convolutional neural network (NCNN) for
effective identification of bearing defects from small samples. For effective feature learning …
effective identification of bearing defects from small samples. For effective feature learning …
Few-shot fault diagnosis of turnout switch machine based on semi-supervised weighted prototypical network
The turnout switch machine is a critical equipment of the signal system, which has a
significant influence on the safety of train. However, it is difficult to obtain a mass of labeled …
significant influence on the safety of train. However, it is difficult to obtain a mass of labeled …
Fault diagnosis in rotating machines based on transfer learning: literature review
With the emergence of machine learning methods, data-driven fault diagnosis has gained
significant attention in recent years. However, traditional data-driven diagnosis approaches …
significant attention in recent years. However, traditional data-driven diagnosis approaches …
Convolutional neural network in intelligent fault diagnosis toward rotatory machinery
S Tang, S Yuan, Y Zhu - IEEE Access, 2020 - ieeexplore.ieee.org
Rotating machinery is of vital importance in the field of engineering, including aviation and
navigation. Its failure will lead to severe loss to personnel safety and the stability of the …
navigation. Its failure will lead to severe loss to personnel safety and the stability of the …
Spatial graph convolutional neural network via structured subdomain adaptation and domain adversarial learning for bearing fault diagnosis
Unsupervised domain adaptation (UDA) has shown remarkable results in fault diagnosis
under changing working conditions in recent years. However, most UDA methods do not …
under changing working conditions in recent years. However, most UDA methods do not …