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 …
A review of artificial intelligence methods for condition monitoring and fault diagnosis of rolling element bearings for induction motor
The fault detection and diagnosis (FDD) along with condition monitoring (CM) and of rotating
machinery (RM) have critical importance for early diagnosis to prevent severe damage of …
machinery (RM) have critical importance for early diagnosis to prevent severe damage of …
A transfer convolutional neural network for fault diagnosis based on ResNet-50
With the rapid development of smart manufacturing, data-driven fault diagnosis has attracted
increasing attentions. As one of the most popular methods applied in fault diagnosis, deep …
increasing attentions. As one of the most popular methods applied in fault diagnosis, deep …
A new convolutional neural network-based data-driven fault diagnosis method
Fault diagnosis is vital in manufacturing system, since early detections on the emerging
problem can save invaluable time and cost. With the development of smart manufacturing …
problem can save invaluable time and cost. With the development of smart manufacturing …
Fault diagnosis of rotating machinery based on recurrent neural networks
Fault diagnosis of rotating machinery is essential for maintaining system performance and
ensuring the operation safety. Deep learning (DL) has been recently developed rapidly and …
ensuring the operation safety. Deep learning (DL) has been recently developed rapidly and …
Online fault diagnosis method based on transfer convolutional neural networks
G Xu, M Liu, Z Jiang, W Shen… - IEEE Transactions on …, 2019 - ieeexplore.ieee.org
Fault detection and diagnosis (FDD) is crucial for stable, reliable, and safe operation of
industrial equipment. In recent years, deep learning models have been widely used in data …
industrial equipment. In recent years, deep learning models have been widely used in data …
Deep learning-based intelligent fault diagnosis methods toward rotating machinery
S Tang, S Yuan, Y Zhu - Ieee Access, 2019 - ieeexplore.ieee.org
Fault diagnosis of rotating machinery plays a significant role in the industrial production and
engineering field. Owing to the drawbacks of traditional fault diagnosis methods, such as …
engineering field. Owing to the drawbacks of traditional fault diagnosis methods, such as …
A review of data-driven machinery fault diagnosis using machine learning algorithms
J Cen, Z Yang, X Liu, J **ong, H Chen - Journal of Vibration Engineering & …, 2022 - Springer
Purpose This article aims to systematically review the recent research advances in data-
driven machinery fault diagnosis based on machine learning algorithms, and provide …
driven machinery fault diagnosis based on machine learning algorithms, and provide …
A new reinforcement learning based learning rate scheduler for convolutional neural network in fault classification
Convolutional neural network (CNN) has gained increasing attention in fault classification.
However, the performance of CNN is sensitive to its learning rate. Some previous works …
However, the performance of CNN is sensitive to its learning rate. Some previous works …
Faults and diagnosis methods of permanent magnet synchronous motors: A review
Y Chen, S Liang, W Li, H Liang, C Wang - Applied Sciences, 2019 - mdpi.com
Permanent magnet synchronous motors (PMSM) have been used in a lot of industrial fields.
In this paper, a review of faults and diagnosis methods of PMSM is presented. Firstly, the …
In this paper, a review of faults and diagnosis methods of PMSM is presented. Firstly, the …