Data-based techniques focused on modern industry: An overview
This paper provides an overview of the recent developments in data-based techniques
focused on modern industrial applications. As one of the hottest research topics for …
focused on modern industrial applications. As one of the hottest research topics for …
From model, signal to knowledge: A data-driven perspective of fault detection and diagnosis
X Dai, Z Gao - IEEE Transactions on Industrial Informatics, 2013 - ieeexplore.ieee.org
This review paper is to give a full picture of fault detection and diagnosis (FDD) in complex
systems from the perspective of data processing. As a matter of fact, an FDD system is a data …
systems from the perspective of data processing. As a matter of fact, an FDD system is a data …
Meta-learning as a promising approach for few-shot cross-domain fault diagnosis: Algorithms, applications, and prospects
Y Feng, J Chen, J Xie, T Zhang, H Lv, T Pan - Knowledge-Based Systems, 2022 - Elsevier
The advances of intelligent fault diagnosis in recent years show that deep learning has
strong capability of automatic feature extraction and accurate identification for fault signals …
strong capability of automatic feature extraction and accurate identification for fault signals …
Machine learning-based fault diagnosis for single-and multi-faults in induction motors using measured stator currents and vibration signals
In this paper, a practical machine learning-based fault diagnosis method is proposed for
induction motors using experimental data. Various single-and multi-electrical and/or …
induction motors using experimental data. Various single-and multi-electrical and/or …
Convolutional discriminative feature learning for induction motor fault diagnosis
A convolutional discriminative feature learning method is presented for induction motor fault
diagnosis. The approach firstly utilizes back-propagation (BP)-based neural network to learn …
diagnosis. The approach firstly utilizes back-propagation (BP)-based neural network to learn …
Fault diagnosis method based on principal component analysis and broad learning system
H Zhao, J Zheng, J Xu, W Deng - IEEE Access, 2019 - ieeexplore.ieee.org
Traditional feature extraction methods are used to extract the features of signal to construct
the fault feature matrix, which exists the complex structure, higher correlation, and …
the fault feature matrix, which exists the complex structure, higher correlation, and …
Stacked sparse autoencoder-based deep network for fault diagnosis of rotating machinery
As a breakthrough in the field of machine fault diagnosis, deep learning has great potential
to extract more abstract and discriminative features automatically without much prior …
to extract more abstract and discriminative features automatically without much prior …
Extended Kalman filtering for remaining-useful-life estimation of bearings
RK Singleton, EG Strangas… - IEEE Transactions on …, 2014 - ieeexplore.ieee.org
Condition-based maintenance, which includes both diagnosis and prognosis of faults, is a
topic of growing interest for improving the reliability of electrical drives. Bearings constitute a …
topic of growing interest for improving the reliability of electrical drives. Bearings constitute a …
An automatic and robust features learning method for rotating machinery fault diagnosis based on contractive autoencoder
Fault diagnosis of rotating machinery is vital to improve the security and reliability as well as
avoid serious accidents. For instance, robust fault features are crucial to achieve a high …
avoid serious accidents. For instance, robust fault features are crucial to achieve a high …
Detection of localized bearing faults in induction machines by spectral kurtosis and envelope analysis of stator current
VCMN Leite, JGB da Silva, GFC Veloso… - IEEE Transactions …, 2014 - ieeexplore.ieee.org
Early detection of faults in electrical machines, particularly in induction motors, has become
necessary and critical in reducing costs by avoiding unexpected and unnecessary …
necessary and critical in reducing costs by avoiding unexpected and unnecessary …